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Enabling Joint Communication and Radio Sensingin Mobile Networks - A Survey

Md Lushanur Rahman, J. Andrew Zhang, Senior Member, IEEE, Kai Wu, Xiaojing Huang, Senior Member, IEEE,Y. Jay Guo, Fellow, IEEE, Shanzhi Chen Fellow, IEEE, and Jinhong Yuan, Fellow, IEEE

Abstract—Mobile network is evolving from a communication-only network towards the one with joint communication andradio/radar sensing (JCAS) capabilities, that we call perceptivemobile network (PMN). Radio sensing here refers to informationretrieval from received mobile signals for objects of interest in theenvironment surrounding the radio transceivers. In this paper, weprovide a comprehensive survey for systems and technologies thatenable JCAS in PMN, with a focus on works in the last ten years.Starting with reviewing the work on coexisting communicationand radar systems, we highlight their limits on addressing theinterference problem, and then introduce the JCAS technology.We then set up JCAS in the mobile network context, and envisageits potential applications. We continue to provide a brief reviewfor three types of JCAS systems, with particular attention totheir differences on the design philosophy. We then introduce aframework of PMN, including the system platform and infras-tructure, three types of sensing operations, and signals usablefor sensing, and discuss required system modifications to enablesensing on current communication-only infrastructure. Withinthe context of PMN, we review stimulating research problemsand potential solutions, organized under nine topics: performancebounds, waveform optimization, antenna array design, cluttersuppression, sensing parameter estimation, resolution of sensingambiguity, pattern analysis, networked sensing under cellulartopology, and sensing-assisted secure communication. This paperprovides a comprehensive picture for the motivation, methodol-ogy, challenges, and research opportunities of realizing PMN. ThePMN is expected to provide a ubiquitous radio sensing platformand enable a vast number of novel smart applications.

Index Terms—Joint communication and radio/radar sensing(JCAS), Dual-functional Radar-Communications, RadCom, Mo-bile networks, Sensing parameter estimation, Clutter suppres-sion, Networked sensing, Sensing-assisted secure communication,Waveform optimization.

I. INTRODUCTION

Wireless communication and radar sensing (C&S) havebeen advancing in parallel yet with limited intersections fordecades. They share many commonalities in terms of signalprocessing algorithms, devices and, to a certain extent, systemarchitecture. This has recently motivated significant researchinterests in the coexistence, cooperation, and joint design ofthe two systems [1]–[13].

Md Lushanur Rahman, J. Andrew Zhang, Kai Wu, Xiaojing Huangand Y. Jay Guo are with University of Technology Sydney, (UTS),Global Big Data Technologies Centre (GBDTC), Australia. Email:lushanur.rahman@gmail.com; {Andrew.Zhang; Kai.Wu; Xiaojing.Huang;Jay.Guo}@uts.edu.au

Shanzhi Chen is with China Academy of Telecommunications Technology(CATT), Beijing, China. Email: chensz@cict.com

Jinhong Yuan is with University of New South Wales. Email:j.yuan@unsw.edu.au

The coexistence of communication and radar systems hasbeen extensively studied in the past decade, with a focuson developing efficient interference management techniquesso that the two individually deployed systems can operatesmoothly without interfering with each other [4], [5], [14]–[20]. Although radar and communication systems may beco-located and even physically integrated, they transmit twodifferent signals overlapped in time and/or frequency domains.They operate simultaneously by sharing the same resourcescooperatively, with a goal of minimizing interference to eachother. Great efforts have been devoted to mutual interferencecancellation in this case, using, for example, beamformingdesign in [20], cooperative spectrum sharing in [16], op-portunistic primary-secondary spectrum sharing in [17], anddynamic coexistence in [19]. However, effective interferencecancellation typically has stringent requirements on the mo-bility of nodes and information exchange between them. Thespectral efficiency improvement is hence limited in practice.

Since the interference in coexisting systems is causedby transmitting two separate signals, it is natural to askwhether we can use a single transmitted signal for bothcommunications and radar sensing. Radar systems typicallyuse specifically designed waveforms such as short pulsesand chirps, which enables high power radiation and simplereceiver processing [21]. However, these waveforms are notnecessary for radar sensing. Passive radar or passive sensingis a good example of exploring diverse radio signals forsensing [22]–[24]. In principle, the objects to be sensed canbe illuminated by any radio signal of sufficient power, suchas TV signals [25], WiFi signals [26], and mobile (cellu-lar) signals [27]–[29]. This is because the propagation ofradio signals is always affected by environmental dynamicssuch as transceiver movement, surrounding objects movementand profile variation, and even weather changes. Hence theenvironmental information is encoded to the received radiosignals and can be extracted by using passive radar techniques.However, there are two major limitations with passive sensing.Firstly, the clock phases between transmitter and receiver arenot synchronized in passive sensing and there is always anunknown and possibly time-varying timing offset between thetransmitted and received signals. This leads to timing andtherefore ranging ambiguity in the sensing results, and alsocauses difficulties in aggregating multiple measurements forjoint processing. Secondly, the sensing receiver may not knowthe signal structure. As a result, passive sensing lacks thecapability of interference suppression, and it cannot separatemultiuser signals from different transmitters. Of course, the

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Systems Signal Formats and Key Features Advantages Disadvantages

C&S withSeparatedWaveforms

C&S signals are separated in time,frequency, code and/or polariza-tion;C&S hardware and software arepartially shared.

Small mutual interference;Almost independent design of C&Swaveforms.

Low spectrum efficiency;Low order of integration;Complex transmitter hardware.

CoexistingC&S

C&S use separated signals butshare the same resource.

Higher spectrum efficiency Interference is a major issue;Nodes cooperation and complicated signal pro-cessing are typically required.

Passivesensing

Received radio signals are used forsensing at a specifically designedsensing receiver, external to thecommunication system;No joint signal design at transmit-ter.

Without requiring any change toexisting infrastructure;Higher spectrum efficiency.

Require dedicated sensing receiver;Timing ambiguity;Non-coherent sensing and limited sensing capa-bility when signal structure is complicated andunknown, e.g., incapable of separating multi-user signals from different transmitters;No waveform optimization.

JCAS A common transmitted signal isjointly design and used for C&S.

Highest spectral efficiency;Fully shared transmitter and largelyshared receiver;Joint design and optimization onwaveform, system and network;“Coherent sensing”.

Requirement for full-duplex or equivalent capa-bility of a receiver co-locating with the transmit-ter;Sensing ambiguity when transmitter and receiverare separated without clock synchronization.

TABLE I: Comparison of C&S systems with separated waveforms, coexisting C&S, passive sensing, and JCAS.

radio signals are not optimized for sensing in any way.The potential of using non-dedicated radio signals for radar

sensing is further boosted by machine learning, in particular,deep learning techniques [7], [30], [31]. With these techniques,traditional radar is evolving towards more general radio sens-ing. We prefer the term radio sensing to radar due to itsgenerality and breadth. Radio sensing here can be widelyreferred to retrieving information from received radio signals,other than the communication data modulated to the signal atthe transmitter. It can be achieved through the measurement ofboth sensing parameters related to location and moving speedsuch as time delay, angle-of-arrival (AoA), angle-of-departure(AoD), Doppler frequency and magnitude of multipath signal,and physical feature parameters (such as inherent patternsignals of devices/objects/activities), using radio signals. Thetwo corresponding processing activities are called sensingparameter estimation and pattern recognition in this paper.In this sense, radio sensing refers to more general sensingtechniques and applications using radio signals, correspondingto video sensing using video signals. Radio sensing involvesmore diverse applications such as object, activity and eventrecognition in Internet of Things (IoT), WiFi and 5G networks[6]. In [7], the authors described the ubiquitous use of wirelesstechnologies such as WiFi, Bluetooth, FM radio and mobilecellular networks, as signals of opportunity in the implementa-tion of IoT. These radio signals are transmitted by an existinginfrastructure and are not specifically designed for the sensingpurpose. In [31], the authors surveyed works on WiFi sensingwhere WiFi signals can be used for people and behaviorrecognition in an indoor environment. In [32], it is shown thatother radio signals, such as RFID and ZigBee, can also beused for activity recognition. These publications demonstratethe strong potentials of using low-bandwidth communicationsignals for radio sensing applications.

It is seen that, joint communication and (radar/radio) sens-ing (JCAS, aka, dual-functional radar and communications,

or RadCom) [1], [3], [6], [8], [11], [13], [33] is emergingas an attractive solution for integrating communication andsensing into one system. JCAS aims to jointly design and use asingle transmitted signal for both communication and sensing.This means that a majority of the transmitter modules can beshared by C&S. Most of the receiver hardware can also beshared, but receiver processing, particularly the baseband sig-nal processing, is typically different for C&S. Via joint design,JCAS can also potentially overcome the two aforementionedlimitations in passive sensing. These properties make JCASsignificantly different from existing spectrum sharing con-cepts such as cognitive radio, the aforementioned coexistingcommunication-radar systems, and “integrated” systems usingseparated waveforms [2], where communication and sensingsignals are separated in resources such as time, frequency andcode, although the two functions may physically be combinedin one system. In Table I, we briefly compare the signalformats and key features, advantages and disadvantage ofthe C&S systems with separated waveforms, coexisting C&Ssystems, passive sensing, and JCAS systems.

The initial concept of integrated C&S systems may betraced back to 1970s [34], and had been primarily investigatedfor developing multimode or multi-function military radars.In the earlier days, most of such systems belong to thetype of C&S with separated waveforms, as detailed in TableI. There has been limited research on JCAS for domesticsystems until 2010s. In the past few years, JCAS has beenstudied based on both simple point-to-point communicationssuch as vehicular networks [13], [35]–[39] and complicatedmobile/cellular networks [12], [14], [15], [40]. The former canfind great applications in autonomous driving, while the lattermay revolutionize the current communication-only mobilenetworks.

JCAS has the potential of integrating radio sensing intolarge-scale mobile networks, creating what we call PerceptiveMobile Networks (PMNs) [11], [12], [41]–[43]. Evolving

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Event Management

Smart Mobile Devices

Tracking, locating

Personal Assistance

Vehicular Network

Environmental Sensing

Smart Home

Smart City

Factory Automation Drones, UAVMilitary Devices

Fig. 1: Use cases of PMN

from the current mobile network, the PMN is expected toserve as a ubiquitous radio-sensing network, whilst providinguncompromising mobile communication services. It can bebuilt on top of existing mobile network infrastructure, with-out requiring significant changes on network structure andequipment. It will unleash the maximum capabilities of mobilenetworks, and avoid the prohibitively high infrastructure costsof building separate wide-area radio sensing networks. Witha large coverage, the integrated communication and sensingcapabilities are expected to enable many new applications forwhich current sensing solutions are either impractical or toocostly.

A. Potential Sensing Applications of PMNs

Large-scale sensing is becoming increasingly important forthe growth of our industry and society [11], [41], [42]. Itis a critical enabler for disruptive IoT applications and adiverse range of smart initiatives such as smart cities andsmart transportation [6]. Unfortunately, its adoption is severelyconstrained by the high infrastructure costs due to the limitedcoverage areas of existing sensors. For example, seamlesscamera surveillance over expansive areas will be prohibitivelyexpensive due to the sheer number of cameras and communi-cation links required to connect them. In addition, there aresignificant privacy concerns.

PMN is able to provide simultaneous communication andradio sensing services, and it can potentially become a ubiqui-tous solution for radio sensing because of its larger broadbandcoverage and powerful infrastructure. Its joint and harmonisedcommunication and sensing capabilities will increase the pro-ductivity of our society, and facilitate the creation and adoptionof a vast number of new applications that no existing sensors

can efficiently enable. Some earlier work on passive sensingusing mobile signals has demonstrated its potentials. Forexample, [27], [28] and [29] used GSM-based radio signalsfor traffic monitoring, weather prediction and remote sensingof rainfall, respectively. The perceptive network can be widelydeployed for both communication and sensing applications intransport, communications, energy, precision agriculture, andsecurity, where existing solutions are either infeasible or inef-ficient. It can also provide complementary sensing capabilitiesto existing sensor networks, with its unique features of day-and-night operation and see-through of fog, foliage, and evensolid objects.

There have been numerous WiFi sensing demonstrators de-veloped and reported in the literature, for applications coveringsafety, security, health and entertainment [31]. The PMN hasmore advanced infrastructure than WiFi sensing, includinglarger antenna arrays, larger signal bandwidth, more power-ful signal processing, and distributed and cooperative base-stations. In particular, with massive multiple-input multiple-output (MIMO), the PMN equivalently possesses a massivenumber of pixels for sensing. This enables radio devices toresolve numerous objects at a time and achieve sensing resultswith much better resolution.

Some of the sensing applications that can be enabled byPMN are illustrated in Fig. 1. More specific examples of novelapplications may include:

• Real-time city-wide vehicle classification and tracking,vehicle speed measurement, and on-road parking spacedetection;

• Extensive on-street and open space surveillance for secu-rity and safety;

• Low-cost automatic street lighting systems;

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• Fine-granularity environmental sensing including factoryemissions monitoring;

• Farm livestock movement and animal migration monitor-ing;

• Crowd management for major events and emergencyevacuation; and

• Integrated security, safety and health sensing applicationsin households.

B. Contributions and Structure of this Paper

This paper provides a comprehensive survey on the state-of-the-arts research on PMN that realizes JCAS technologyin mobile networks. Different to some existing overviewarticles [1]–[3], [5], [6], [8], we focus on JCAS techniquesthat are tailored to cellular/mobile networks, by providing aclear picture on what the PMN will look like and how itmay be evolved from the current communication-only networkfrom the viewpoints of both infrastructure and technology.In this survey, we consider mobile-specific JCAS challengesand solutions, associated with heterogeneous network archi-tecture and components, sophisticated mobile signal format,and complicated signal propagation environment. We referto complicated mobile signals as those with modulations oforthogonal frequency-division multiple access (OFDMA) andmultiuser-MIMO (or spatial division multiple access, SDMA).We discuss major challenges and required changes to systeminfrastructure for the paradigm shift from communication-only mobile network to PMN with integrated communicationand sensing, and provide a comprehensive review on existingtechnologies and open research problems, to address thesechallenges within the framework of PMN.

The rest of this paper is organized as follows.• In Section II, we first discuss the difference between

communication and radar waveforms. We then brieflyreview the research on three types of JCAS systems,including realizing communication function in a primaryradar system, realizing radio sensing function in a primarycommunication system, and joint design without beingconstrained to an underlying system. We pay particu-lar attention to how the three types of JCAS systemsovercome the waveform difference to meet the differentrequirements for C&S. Note that, the PMN is an exampleof realizing radio sensing in a primary communicationsystem.

• In Section III, we introduce the framework of a PMN, in-cluding system architecture, three types of unified sensingoptions, and signals usable for sensing.

• In Section IV, we discuss the required system mod-ifications for realizing sensing on an communication-oriented infrastructure. We review three near-term optionsthat enable JCAS in PMNs without requiring significantnetwork modifications, particularly for time-division du-plexing (TDD) systems.

• Section V discusses various major research challenges,as well as research opportunities, in PMNs, includingsensing parameter extraction, clutter suppression, jointdesign and optimization, and networked sensing.

• In Section VI, we provide a comprehensive review fortechnologies that have been developed to address thesechallenges and beyond, and remained open researchproblems. The research review is organized under ninetopics: performance bounds, waveform optimization, an-tenna array design, clutter suppression, sensing param-eter estimation, resolution of sensing ambiguity, patternanalysis, networked sensing under cellular topology, andsensing-assisted secure communication. We also discussthe technology matureness and research difficulty for eachtopic, and highlight key open research problems.

• Finally, conclusions are drawn in Section VII.A list of abbreviations used in this paper are provided in

Table II.

II. THREE TYPES OF JCAS SYSTEMS

Based on the design priority and the underlying signalformats, the current JCAS systems may be classified into thefollowing three categories, namely:• Realizing communication function in a primary radar

system (or integrating communication into radar);• Realizing radio sensing function in a primary communi-

cation system (or integrating radar into communication);and

• Joint design without being constrained to an underlyingsystem.

In the first two categories, the design and research focusare typically on how to realize the other function based onthe signal formats of the primary system, with the principleof not significantly affecting the primary system. The lastcategory considers the design and optimization of the signalwaveform, system and network architecture, without bias toeither communication or sensing, aiming at fulfilling thedesired applications only. PMNs belong to the second class,where communication is already very well realized and themain challenge is how to achieve radar sensing functionalitybased on the existing cellular network infrastructure.

Next, we first briefly discuss the major differences betweentraditional communication and radar signals, which are im-portant for understanding the design philosophy of the threecategories of JCAS systems. We then provide a brief reviewon the recent research progress in each of the categories.

A. Major Differences between C&S Signals

Conventional radar systems include pulsed and continuous-wave radars [2], [5], [44]. In pulsed radar systems, shortpulses of large bandwidth are transmitted either individuallyor in a group, followed by a silent period for receivingthe echoes of the pulses. Continuous wave radars transmitwaveforms continuously, typically scanning over a large rangeof frequencies. In either systems, the waveforms are typicallynon-modulated. These waveforms are used in both SISO andMIMO radar systems, with orthogonal waveforms used inMIMO radars [21], [44].

In most of radar systems, low peak-to-average power ratio(PAPR) is a desired feature for the transmitting signal, which

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TABLE II: List of abbreviations

Abbreviations MeaningsAoA Angle of arrivalAoD Angle of departureANM Atomic norm minimizationBBU Baseband unitCACC Cross-antenna cross-correlationCRAN Cloud radio access networkCS Compressive sensingCSI Channel state informationCSI-RS Channel state information reference signalsC&S Wireless communication and

radar/radio sensingDMRS Demodulation reference signalsFDD Frequency division duplexingGMM Gaussian mixture modelICA Independent component analysisIoT Internet of thingsJCAS Joint communication and radio/radar sensingLFM Linear frequency modulationLFM-CPM LFM-continuous phase modulationMAC Medium accessMI Mutual informationMIMO Multiple-input multiple-outputMMSE Minimum mean-square errorMMV Multi measurement vectormmWave Millimeter waveNR New radioOFDM Orthogonal frequency-division multiplexingOFDMA Orthogonal frequency-division multiple accessPHY PhysicalPAPR Peak-to-average power ratioPCA Principal component analysisPMN Perceptive mobile networkPDSCH Physical downlink shared channelPUSCH Physical uplink shared channelPRB Physical resource-blockRIP Restricted isometry propertyRRUs Remote radio unitsRMA Recursive moving averagingRMSE Root mean square errorSC Single carrierSDMA Spatial division multiple accessSISO Single input single outputSRS Sounding reference signalsSSB Synchronization signal and broadcast blocksSTAP Space-time adaptive processingSVD Singular value decompositionTDD Time-division duplexingUE User equipmentV2V Vehicle to vehicle

enables high efficiency power amplifier and long-range oper-ation. The transmitting waveform is also desired to have anambiguity function with steep and narrow mainlobes, whichis the correlation function of the received echo signals and thelocal template signal [9], [44]. These waveforms are designedto enable low-complexity hardware and signal processing inradar receivers, for estimating key sensing parameters such asdelay, Doppler frequency and angle of arrival. However, theyare not indispensable for estimating these parameters. A pulsedradar receiver typically samples the signal at a high samplingrate twice of the transmitted pulse bandwidths, or at relatively

lower sampling rate at the desired resolution of the delay(ranging); while a continuous-wave radar receiver typicallysamples signals at a rate much smaller than the scanningbandwidth, proportional to the desired detection capabilityof the maximal delay. Due to their special signal form andhardware, radar systems generally cannot support very high-rate communications, without significant modifications [8],[9].

Comparatively, communication signals are designed to max-imize the information-carrying capabilities. They are typicallymodulated, and modulated signals are typically appendedwith non-modulated intermittent training signals in a packet.To support diverse devices and communication requirements,communication signals can be very complicated. For example,they can be discontinuous and fragmented over time andfrequency domains, have high PAPR, have complicated signalstructures due to advanced modulations applied across time,frequency, and spatial domains.

Although being designed without considering the demandfor sensing, communication signals can potentially be used forestimating all the key sensing parameters. However, differentto conventional channel estimation which is already imple-mented in communication receiver, sensing parameter estima-tion requires extraction of the channel composition rather thanchannel coefficients only. Such detailed channel compositionestimation is largely limited by the hardware capability. Thecomplicated communication signals are very different to con-ventional radar and demand new sensing algorithms. There arealso practical limits in communication systems, such as full-duplex operation and asynchronisation between transmittingnode and receiving node, which requires new sensing solutionto be developed. We note that the detailed information on thesignal structure, such as resource allocation for time, frequencyand space, and the transmitted data symbols, can be criticalfor sensing. For example, the knowledge on signal structure isimportant for coherent detection. In comparison, most passiveradar sensing can only perform non-coherent detection withthe unknown signal structure, and hence only limited sensingparameters can be extracted from the received signals withdegraded performance [22], [23].

The differences and benefits of JCAS in comparison withindividual radar or communication system are summarized inTable III.

B. Realizing Communication in Primary Radar Systems

Radar systems, particularly military radar, have the extraor-dinary capability of long-range operation, up to hundreds ofkilometers. Therefore, a major advantage of implementingcommunication in radar systems is the possibility of achievingvery long range communications, with much lower latencycompared to satellite communications. However, the achiev-able data rates for such systems are typically limited, due tothe inherent limitation in the radar waveform. In [45], authorsimplemented a combined radar and communication systembased on a software defined radar platform, in which the radarpulses are used for communication. Research work in [5]and [46] shows that, communication network establishment

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TABLE III: Comparison between Radar, Communication and JCAS

Systems Radar Communication JCAS SystemSignalWaveform

Typically unmodulated single-carrier signals; Pulsed orcontinuous-waveform frequencymodulated; Orthogonal if multiplestreams; low peak-to-averagepower ratio (PAPR)

Mix of unmodulated (pilots) andmodulated symbols; Complicatedsignal and resource usage with theuse of OFDMA and multiuser-MIMO techniques; High PAPR.

JCAS can use both traditional radar andcommunication signals, with appropriatemodifications.

TransmissionPower

High Low Communications integrated into Radarcan achieve very long link distance.Sensing integrated into a single commu-nication device can only support shortrange, but overall JCAS can cover verylarge areas due to the wide coverage ofcommunication networks.

Bandwidth Large signal bandwidth. Resolu-tion proportional to bandwidth.

Typically much smaller than radar. mmWave signals are very promising forJCAS, due to large signal bandwidthand limited propagation. Sensing appli-cations do not have to rely on largebandwidth, such as known WiFi sensingexamples.

Signal Band X, S, C and Ku sub-6 GHz and mmWave bands Have an impact on operation distancesand resolution capabilities of JCAS.

TransmissionCapability(Duplex)

Full-duplex (continuous-waveform) or half-duplex (pulsed)

Co-located transmitter and re-ceiver typically cannot operate onthe same time or frequency block.

Full-duplex is a favourite condition, butnot essential.

Clock Syn-chronization

Transmitter and receiver are clock-locked.

Colocated transmitter and receivershare the same timing clock, butnon-colocated nodes typically donot.

Clock-level synchronization removesambiguity in sensing parameterestimation, but is not essential forsome sensing applications.

can be possible for both static and moving radars used inthe military and aviation domains. Adaptive transmit signalsfrom airborne radar mounted unmanned vehicles can also beused to simultaneously sense a scene and communicate senseddata to a receiver at the ground base station. The objectiveof such systems is to establish low latency, secure and long-range communications on top of existing radar systems. SuchJCAS systems have been mainly called as dual-function radar-communications (DFRC).

Realization of communication in radar systems needs to bebased on either pulsed or continuous-waveform radar signals.Hence information embedding is one of the major challenges.For example, in [47], random step frequency signal is used indesigning a JCAS system where the carrier frequency of theradar signal is used for modulating communication informa-tion. In [48], the authors showed that the quasi-orthogonalmulticarrier linear frequency modulation-continuous phasemodulation (LFM-CPM) waveform radiated by a MIMO radarcan be applied for communications with multiple users. Formore information on embedding communication informationto radar signals, the readers can refer to [9] which providesan excellent review on this topic.

What is missing here in the literature is the communicationprotocol design and receiver signal processing. Communi-cation protocols, particularly medium access (MAC) layerprotocol and physical layer frame structure, are well designedin communication systems. However, the design of commu-nications protocols which can be fitted into radar signals is

not straightforward. The main challenges lie on the require-ment that communication protocol design shall be seamlesslyintegrated into radar operation. Some early work is reportedin [49], where a frame structure is proposed for JCAS withfrequency-hopping continuous-wave radar signals. Based onthe frame structure, channel estimation techniques are thendeveloped without knowing the frequency hopping sequence atthe communication receiver. Nevertheless, a complete receiversignal processing for extracting the information embedded inradar waveform is not well studied yet.

C. Realizing Sensing in Primary Communication Systems

This is the category of JCAS systems that the PMN belongsto, and we will provide a comprehensive survey on it inthe rest of this paper. Here, we briefly review the researchin this category. Considering the topology of communicationnetworks, systems in this category can be classified intotwo sub-categories, namely, those realizing sensing in point-to-point communication systems particularly for applicationsin vehicular networks, and those realizing sensing in largenetworks such as mobile networks.

There have been quite a few works on sensing in vehicularnetworks using IEEE 802.11 signals. In [50], the authors im-plemented active radar sensing functions into a communicationsystem with OFDM signals for vehicular applications. Thepresented radar sensing functions involve Fourier transformalgorithms that estimate the velocity of multiple reflecting

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objects in IEEE 802.11.p based JCAS system. In [35], auto-motive radar sensing functions are performed using the singlecarrier (SC) physical (PHY) frame of IEEE 802.11ad in anIEEE 802.11ad millimeter wave (mmWave) vehicle to vehicle(V2V) communication system. In [36], OFDM communicationsignals, conforming to IEEE 802.11a/g/p, are used to performradar functions in vehicular networks. More specifically, abrute-force optimization algorithm is developed based onreceived mean-normalized channel energy for radar rangingestimation. The processing of delay and Doppler informationwith IEEE 802.11p OFDM waveform in vehicular networksis shown in [51] by applying the ESPRIT method.

There has been rapidly increasing JCAS work reportedfor modern mobile networks. In [52], some early work onusing OFDM signal for sensing was reported. In [53], sparsearray optimization is studied for MIMO JCAS systems. Sparsetransmit array design and transmit beampattern synthesis forJCAS are investigated in [54] where antennas are assignedto different functions. In [55], mutual information for anOFDM JCAS system is studied, and power allocation forsubcarriers is investigated based on maximizing the weightedsum of the mutual information for C&S. In [15], waveformoptimization is studied for minimizing the difference betweenthe generated signal and the desired sensing waveform. In[56], the multiple access performance bound is derived for amultiple antenna JCAS system. In [57], multicarrier waveformis proposed for dual-use radar-communications, for whichinterleaved subcarriers or subsets of subcarriers are assigned tothe radar or the communications tasks. These studies involvesome key signal formats in modern mobile networks, such asMIMO, multiuser MIMO, and OFDM. In [11], [12], [41]–[43],the authors systematically studied how JCAS can be realized inmobile networks by considering their specific signal, systemand network structures, and how radar sensing can be donebased on modern mobile communication signals. Based onreported results in the literature and our own experience andvision on this technology, we provide a comprehensive reviewof existing techniques and open research problems under theframework of PMNs in the following sections.

D. Joint Design Without an Underlying System

Although there is no clear boundary between the thirdcategory of technologies and systems and the previous twocategories, there is more freedom for the former in terms ofsignal and system design. That is, JCAS technologies can bedeveloped without being limited to existing communicationor radar systems. In this sense, they can be designed andoptimized by considering the essential requirements for bothcommunication and sensing, potentially providing a bettertrade-off between the two functions.

The mmWave JCAS systems are great examples of fa-cilitating such joint design. On one hand, with their largebandwidth and short wavelength, mmWave signals providegreat potentials for high date-rate communications and high-accuracy sensing. On the other hand, mmWave systems areemerging and are yet to be widely deployed. Millimeter wavebased JCAS can facilitate many new exciting applications

both indoor and outdoor. Existing research on mmWave JCAShas demonstrated its feasibility and potentials in indoor andvehicle networks [10], [13], [33], [37], [58]–[61]. The authorsin [10] provide an in-depth signal processing aspects ofmmWave-based JCAS with an emphasis on waveform designfor joint radar and communication system. Future mmWaveJCAS for indoor sensing is envisioned in [58]. Hybrid beam-forming design for mmWave JCAS systems is investigated in[59]. An adaptive mmWave waveform structure is designed in[60]. Design and selection of JCAS waveforms for automotiveapplications are investigated in [61], where comparisons be-tween phase-modulated continuous-wave JCAS and OFDMA-based JCAS waveforms are provided, by analyzing the systemmodel and enumerating the impact of design parameters. In[13], [37], multibeam technologies are developed to allowC&S at different directions, using a common transmittedsignal. Beamforming vectors are designed and optimized toenable fast beam update and achieve balanced performancebetween C&S.

A brief summary of selected recent research works on eachof the JCAS categories, as well as co-existing C&S, is providein Table IV.

E. Advantages of JCAS Systems

With harmonised and integrated communication and sensingfunctions, JCAS systems are expected to have the followingadvantages:• Spectral Efficiency: Spectral efficiency can ideally be

doubled by completely sharing the spectrum available forwireless communication and radar [2], [45], [18], [63];

• Beamforming Efficiency: Beamforming performancecan be improved through exploiting channel structuresobtained from sensing, for example, quick beam adaptionto channel dynamics and beam direction optimization[68]–[72];

• Reduced Cost/Size: Compared to two separated systems,the joint system can significantly reduce the cost and sizeof transceivers [2], [3], [53];

• Mutual Benefits to C&S: C&S can benefit from eachother with the integration. Communication links canprovide better coordination between multiple nodes forsensing; and sensing provides environment-awareness tocommunications, with potentials for improved securityand performance.

III. FRAMEWORK FOR A PMN

In this section, referring to the work in [11], [12], [41],we present a framework of PMN that integrates radio sensinginto the current communication-only mobile network, usingJCAS technologies. In this framework, we describe the systemarchitecture, introduce three types of unified sensing, anddiscuss communication signals that can be used for sensing.

A. System Platform and Infrastructure

The PMN can evolve from the current mobile network,with modification and enhancement to hardware, systems and

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TABLE IV: Exemplified research on Radar-Communication coexistence and JCAS solutions in the literature.

Category Reference Model Main Contribution

RealizingCommunica-

tion in PrimaryRadar Systems

[48] Multi-carrier quasi-orthogonal LFM-CPM wave-form of MIMO radar utilized for multiuser com-munication in JCAS

Shared waveform design, signal processing andperformance evaluation for the radar and commu-nication subsystems

[62] Integration of communication into MIMO radar Transmit and receive beamforming design for full-duplex communication in MIMO radar

[63] JCAS realized in primary MIMO radar Communication signals embedded into the radartransmit waveform for JCAS

[47] Carrier frequency of the radar signal used formodulating communication information.

Random step frequency signal used in designing aJCAS system

RealizingSensing in

Primary Com-munication

Systems

[35] IEEE 802.11ad mmWave V2V communicationsystem utilized as automotive radars.

Radar detection as well as range and velocityestimation by leveraging standard WLAN receiveralgorithms and classical pulse-Doppler radar algo-rithms

[36] IEEE 802.11 OFDM communications waveformused for sensing in vehicular network

Brute-force optimization performed based on re-ceived mean-normalized channel energy for radarrange estimation

[52] OFDM communication signals used for radarsensing

Signal processing aspects of OFDM radar by pe-riodogram and ESPRIT algorithms

[57] Generalized multicarrier radar and OFDM com-munication system are converged where subsetsof subcarriers assigned to the radar or the com-munications tasks

Multicarrier waveform proposed for JCAS

[50], [51] IEEE 802.11p OFDM communication waveformin vehicular networks used for radar sensing

Processing of delay and Doppler information byapplying the ESPRIT method

Joint DesignWithout anUnderlying

System

[1] Implementation of digital beamforming radarand MIMO communications integration at 24GHz ISM band for intelligent transportationapplications

Continuous single-carrier and multicarrier wave-forms design, as well as techniques for range,Doppler and DoA estimation by classical Fouriertransform-based approach and MUSIC algorithms

[40] Multiple input, single output (MISO) joint mul-tiple access channel topology considered forJCAS operation

Successive interference cancellation (SIC) for com-munication and Pulse-Doppler processing tech-niques are employed to extract Doppler and rangeestimates in radar operation

[64]–[66] Joint radar-communications integrated receiversimultaneously perform radar target parameterestimation and decode a communications signal

Matched filtering clutter suppression techniquesrelated to modern mobile networks based JCAS

[10], [58]–[61]

mmWave JCAS systems. Beamforming design, waveform design, adaptivemmWave waveform design for automotive appli-cations

[14], [15],[67]

Waveform design for MIMO radar and multi-user MIMO communication

Several optimization-based waveform design forgiven radar beam patterns and under constant mod-ulus constraints and similarity constraints (SC)

Co-existenceof C&SSystem

[16], [17],[20]

Spectrum sharing between downlink MU-MIMOcommunication and co-located MIMO radar onthe same frequency band.

Transmit beamforming optimization solution givenfor both shared and separated antenna deploymentand for both perfect and imperfect CSI

[19] Coexistence between a co-located MIMO radarsystem and a LTE communications system.

Spectrum sharing techniques introduced for JCASoperation and genetic algorithm used to solve theoptimization problem

algorithms. In principle, sensing can be realized in either theuser equipment (UE) or base-station (BS). Sensing in UE maymotivate wider end-user applications. Compared to UE, BShas advantages of networked connection, flexible cooperation,

large antenna array, powerful computation capability, andknown and fixed locations to enable more reliable sensingresults. Therefore, in the following, we mainly consider BS-side sensing.

9

Standalone BS

CRAN Central

BBU Pool

Sensing Processing

Unit

RRU 1

RRU 2

RRU 3

Clutter Uplink Comm. & Uplink SensingClutter Reflection

Fronthaul

Downlink Comm. SignalDownlink Active SensingDownlink Passive Sensing

Clutter

Revised as a special UE

Mobile Core

Fig. 2: Illustration of sensing in a PMN with both standalone BS and CRAN topologies. RRU1 is a node supposed to havefull-duplexing capability or equivalent. RRU3 is modified to be a special UE, transmitting uplink signals for uplink sensingin RRU2, with clock synchronization between them. RRU2 can also be modified as a receiver only, to do both uplink anddownlink sensing, as well as communications (receiver only) .

The evolution to PMN is not limited to a particular cellularstandard. Hence we try to generalize the discussions by con-sidering key components and technologies in modern mobilenetworks, such as antenna array, broadband, multi-user MIMOand orthogonal frequency-division multiple access (OFDMA),instead of a specific standard. When necessary, we will referto the 5G new radio (NR) standard.

Depending on the network setup, we consider two typesof topologies where JCAS can be implemented, that is, acloud radio access network (CRAN) and a standalone BS.Realization of sensing in a PMN based on these two topologiesis illustrated in Fig. 2. Below we elaborate the system andnetwork setup for the two topologies, and we will then discussthree types of sensing operations based on the topologies insubsection III-B. Requirements for modifying the setup toenable sensing will be discussed in Section IV.

1) CRAN: A typical CRAN consists of a central unit andmultiple distributed antenna units, which are called remoteradio units (RRUs). The RRUs are typically connected tothe CRAN central via optical fibre. Either quantized radiofrequency signals or baseband signals can be transmittedbetween RRUs and the central unit. As shown in Fig. 2, ina CRAN PMN, the densely distributed RRUs, coordinated bythe central unit, provide communication services to UEs. Theirreceived signals, either from themselves, other RRUs, or fromUEs, are collected and processed by the CRAN central, forboth C&S. The CRAN central unit hosts the original basebandunit (BBU) pool for processing communication functions andthe new sensing processing unit for sensing.

A typical communication scenario is as follows: severalRRUs work cooperatively to provide connections to UEs,using multiuser MIMO techniques over the same resource

blocks (same time and frequency slots). In CRAN commu-nication networks, power control is typically applied such thatsignals from one RRU may not reach other RRUs. Whileit is not necessary, we relax this constraint and assume thatcooperative RRUs are within the signal coverage area of eachother. This assumption is reasonable when dense RRUs aredeployed and used to support surrounding UEs via coordinatedmultipoint techniques. This is not necessary for some typesof sensing as we are going to discuss in next subsection,but it increases the options of sensing. Technically, it is alsofeasible at the cost of increased transmission power even ifonly for supporting sensing, as the downlink signals do notcause mutual communication interference to RRUs.

Note that, in this configuration, all RRUs are typicallysynchronized using the timing clock from the GPS signals.This forms an excellent network with distributed nodes forsensing applications.

2) Standalone BS: The CRAN topology is not necessaryfor realizing sensing in PMNs. A standalone BS can alsoperform sensing using the received signals either from itsown transmitted signals or from UEs. This includes the smallBS that may be deployed within a household, which pushesfor the concepts of edge computing and sensing. Like WiFisensing, such a small BS can be used to support indoor sensingapplications such as fall detection and house surveillance.

From now on, our discussions will be referred to the CRANtopology, but most of results are applicable to the standaloneBS one. Hence in the case without causing confusion, we willuse CRAN and BS interchangeably.

10

B. Three Types of Sensing OperationsThere are three types of sensing that can be unified and im-

plemented in PMNs, defined as uplink and downlink sensing,to be consistent with uplink and downlink communications. Inuplink sensing, signals received from UEs are used for sensing,while in downlink sensing, the sensing signals are from BSs.The downlink sensing is further classified as Downlink ActiveSensing and Downlink Passive Sensing, for the cases whenan RRU collects the echoes from its own and other RRU-transmitted signals, respectively. The terms active and passiveare used to differentiate the cases of sensing using self-transmitted signals and signals from other nodes. Below, weelaborate each sensing operation.

1) Downlink Active Sensing: In downlink active sensing,an RRU (or BS) uses the reflected/diffracted signals of itsown transmitted downlink communication signals for sensing.Like a mono-static radar, the sensing receiver is co-locatedwith the transmitter. Downlink active sensing enables an RRUto sense its surrounding environment. Since the transmitterand receiver are on the same platform, they can be readilysynchronized at the clock-level, and the sensing results canbe clearly interpreted by the node without external assistant.However, this setup would require full-duplexing capability orequivalent.

2) Downlink Passive Sensing: Here, downlink passive sens-ing refers to the case where an RRU uses the receiveddownlink communication signals from other RRUs for sensing.Downlink passive sensing signals will be available to this RRUwhen the transmission power is sufficiently large. In this case,they will always be there together with the downlink activesensing signals, the reflection and refraction of the RRU’s owntransmitted signal. They may arrive at the sensing receiverslightly later than the downlink active sensing signals, due tolonger propagation distances. When all RRUs cooperativelycommunicate with multiple UEs using SDMA, these two typesof signals cannot be readily separated in time or frequency,and therefore sensing algorithms also need to consider down-link active sensing signals if downlink passive sensing is inoperation. In general, downlink passive sensing senses theenvironment between RRUs.

3) Uplink Sensing: The uplink sensing conducted at theBS utilizes the received uplink communication signals fromUE transmitters. Uplink sensing can be directly implementedwithout requiring change of hardware and network setup.However, it estimates the relative, instead of absolute, timedelay and Doppler frequency since the clock/oscillator is typ-ically not locked between spatially separated UE transmittersand BS receivers. This ambiguity may be resolved with specialtechniques as we will discuss in details in Section VI-F. Uplinksensing senses UEs and the environment between UEs andRRUs.

4) Comparison: Downlink sensing can potentially achievemore accurate sensing results than uplink sensing. This isbecause, in the downlink sensing case, RRUs generally havemore advanced transmitters such as more antennas and highertransmission power, and the whole transmitted signals arecentrally known. Additionally, as the sensed results in thedownlink sensing are not directly linked to any UEs, the

privacy issue is largely not a problem. Comparatively, uplinksensing may disclose the information of UE, causing privacyconcerns.

Downlink and uplink sensing in PMNs are both feasiblefor practical applications in terms of sensing capabilities.According to the results in [11] and [41], the downlinkand uplink sensing with practical transmission power values(smaller than 25 dBm) can reliably detect objects more than150 and 50 meters away, respectively, in a dense multipathpropagation environment. Additionally, a distance resolutionat a few meters can be achieved for signal bandwidth of 100MHz, an angle resolution of about 10 degrees for a uniformlinear array of 16 antennas, and a resolution of 5 m/s movingspeed within channel coherence period.

A comparison of the three types of sensing is provided inTable V.

C. Basic Signal Models

We consider a CRAN system with Q RRUs. Each RRU hasa uniform linear array (ULA) with M antenna elements spacedat half wavelength. These RRUs cooperate and provide linksto K users using multiuser-MIMO OFDMA. Each user has aULA of MT elements, and may occupy and share a part of thetotal subcarriers with other users. The data symbols are firstspatially precoded in the frequency domain, and an IFFT isthen applied to each spatial stream. The time-domain signalsare then assigned to the corresponding RRUs. Let N denote thenumber of total subcarriers and B the total bandwidth. Thenthe subcarrier interval is f0 = B/N and the OFDM symbolperiod is Ts = N/B + Tp where Tp is the period of cyclicprefix.

1) A General Channel Model: The array response vectorof a size-M ULA is given by

a(M, θ) = [1, e jπ sin(θ), · · · , e jπ(M−1) sin(θ)]H, (1)

where θ is either AoD or AoA. Let the AoD and AoA of amultipath be θ` and φ` , ` ∈ [1, L], respectively. For M1 trans-mitting and M2 receiving antennas, the M2 × M1 frequency-domain baseband channel matrix at the n-th subcarrier in thet-th OFDM block is given by

Hn =

L∑=1

b`e−j2πn(τ`+τo (t)) f0 e j2πt( fD,`+ fo )Tsa(M2, φ`)aT (M1, θ`),

(2)

where for the `-th multipath, b` is its amplitude of complexvalue accounting for both signal attenuation and initial phasedifference, τ` is the propagation delay, and fD,` is the asso-ciated Doppler frequency, τo(t) denotes the potential timingoffset due to asynchronous clocks between transmitter andreceiver, and fo denotes the carrier frequency offset (CFO).Note that in (2), we have approximated the Doppler phasechanges over the samples in one OFDM block as a singlevalue.

Equation (2) represents a general channel model that canbe used for both communication and sensing. Note that forcommunications, we generally only need to know the compos-ited values of the matrix H(n), which are typically obtained by

11

TABLE V: Comparison of Three Types of Sensing Operations

Types Signals Action Advantages DisadvantagesDownlink ActiveSensing

Reflects from aRRU/BSs owntransmitted downlinkcommunicationsignal

Sense surroundingenvironment of theRRU/BS.

All data symbols in the receivedsignals can be used and are cen-trally known.

Generally require full duplexoperation and other networkmodifications. Devices can bespecially deployed to resolvethis problem.

DownlinkPassive Sensing

Received downlinkcommunicationsignals from otherRRUs

Sense environmentbetween RRUs.

RRUs are synchronized. Privacyis less an issue because sensedresults not directly linked to anyUEs.

Uplink Sensing Uplink communica-tion signals from UEtransmitters

Sense UEs andenvironmentbetween UEs andRRU.

Require minimum modificationto communication infrastructure.Does not require full-duplexing.

Timing and Doppler frequencymeasurement could be relative.Transmitted information signalsare not directly known. Rapidchannel variation when UEs aremoving.

directly estimating channel coefficients at some subcarriers andobtaining the rest via interpolation. For radio sensing, however,the system needs to resolve the detailed channel structureand estimate the sensing parameters {τ`, fD,`, φ`, θ`, b`}. Wedefine a channel static period when all these parameters main-tain almost unchanged, which is typically a few milliseconds(equivalent to the length of hundreds of OFDM symbols).When the oscillator clocks of the transmitter and receiverare not locked, the timing offset τo(t) is nonzero. It may beunknown to the receiver and can cause ranging ambiguity ifit cannot be removed in sensing. The value of τo(t) could bevarying across packets when they are transmitted discontinu-ously. The frequency offset fo varies very slowly over time.Hence its impact on sensing can generally be ignored, whenconventional CFO estimation and compensation techniques areapplied to the received signals in the time domain [73]. Forsimplicity, we will ignore CFO hereafter.

2) Formulation for Downlink Sensing: For downlink sens-ing, each RRU sees reflected downlink signals from itself andthe other Q − 1 RRUs. For any RRU, a general expression ofits received signal at the n-th subcarrier and the t-th OFDMblock is given by

yn,t =Q∑q=1

Lq∑=1

bq,`e−j2πn(τq,`+τq,o ) f0 e j2πt fD,q,`Ts ·

a(M, φq,`)aT (M, θq,`)xq,n,t + zn,t, (3)

where variables with subscript q are for the q-th RRU, xq,n,tare the transmitted signals at subcarrier n from the q-th RRU,and zn,t is the noise vector. There is typically a commonclock between the transmitter and receiver in one RRU, andbetween RRUs, hence the timing offset τq,o = 0. Note that thesignals here are specifically collected and used for sensing, notrequired for communications. However, the sensing results canbe used for improving communication performance, as will bediscussed in Section VI.

The signals xq,n,t are typically the precoded output. Let theprecoder at the n-th subcarrier, t-th OFDM block of the q-th

RRU be Pq,n,t , and the data symbols be sq,n,t . Then

xq,n,t = Pq,n,tsq,n,t . (4)

When joint precoding is applied across RRUs, the precodingmatrices Pq,n,t, q = 1, · · · ,Q are jointly designed.

According to (3), we can see that packing yn,t from multipleRRUs can increase its length, but the unknown parameters aresimilarly increased. Hence sensing does not directly benefitfrom jointly processing, although there may be some correla-tion between sensing parameters for different RRUs. However,due to channel reciprocity, parameters for signal propagationbetween RRUs could be similar. Such a property can beexploited for joint processing across RRUs.

When RRUs’ signals do not reach each other, then Q = 1in (3), and each RRU only sees its own reflected transmittedsignals.

3) Formulation for Uplink Sensing: For uplink signal trans-mitted from one or more UEs, the received signal at a RRUat the n-th subcarrier and the t-th OFDM block can berepresented as

yn,t =K∑k=1

Lk∑=1

bk,`e−j2πn(τk,`+τk,o ) f0 e j2πt fD,k,`Ts ·

a(M, φk,`)aT (MT , θk,`)xk,n,t + zn,t, (5)

When a single UE exclusively occupies the n-th subcarrier,K = 1 in (5). For uplink sensing, the received signals forsensing and communications are identical.

Comparing (5) and (3), we can see that their expressionsare quite similar, except that the variables have differentvalues. Therefore, many signal processing techniques as willbe discussed in this paper are applicable to both downlink anduplink sensing.

D. Signals Usable from 5G for Radio Sensing

For 5G NR, we can exploit the following signals for sensing.These communication signals may be further jointly optimizedfor C&S, using methods in, e.g., [14], [15], [74].

12

1) Signals Used for Channel Estimation: Deterministic sig-nals specifically designed for channel estimations are availablein many systems. The 5G NR [75] includes the demodulationreference signals (DMRS) for both uplink (Physical uplinkshared channel-PUSCH) and downlink (Physical downlinkshared channel-PDSCH), sounding reference signals (SRS)for uplink, and channel state information reference signals(CSI-RS) for downlink. Most of them are comb-type pilotsignals, circularly shifted across OFDM symbols, and areorthogonal between different users. Especially, DMRS signalsaccompanying the shared channel are always transmitted withdata payload and exhibit user specific features. ThereforeDMRS signals are random and irregular over time, whichrequires sensing algorithms that can deal with such irreg-ularity. Comparatively, signals used for beam managementin connected mode, like SRS and CSI-RS can be eitherperiodic or aperiodic, and hence they are more suitable forsensing algorithms based on conventional spectrum estimationtechniques such as ESPRIT.

The number and position of DMRS OFDM symbols areknown to BSs, and they can be adjusted and optimized acrossthe resource grid including slots and subcarriers (resourceblocks). This implies good prospects for both channel es-timation and sensing in different channel conditions. Theallocation of resource grid can be optimized by consideringrequirements from both communications and sensing. With agiven subcarrier spacing, the available radio resources in a sub-frame are treated as a resource grid composed of subcarriersin frequency and OFDM symbols in time. Accordingly, eachresource element in the resource grid occupies one subcarrierin frequency and one OFDM symbol in time. A resource blockconsists of 12 consecutive subcarriers in the frequency domain.A single NR carrier is limited to 3300 active subcarriers asdefined in Sections 7.3. and 7.4 of TS 38.211 in [75]. Thenumber and pattern of the subcarriers that DMRS signalsoccupy have a significant impact on the sensing performance,as we will see in Section VI-E.

In [43], some simulation results for both uplink and down-link sensing using DMRS are provided. The signal is generatedaccording to the Gold sequence as defined in [75] of 3GPP TS38.211, for both PDSCH and PUSCH. The generated physicalresource-block (PRB) is over a 3-D grid comprising a 14-symbol slot for the full subcarriers across the DMRS layersor ports. The interleaved DMRS subcarriers of PDSCH areused in downlink sensing, while groups of non-interleavedDMRS subcarriers of PUSCH are used in uplink sensing.The results demonstrate the feasibility of achieving excellentsensing performance with the use of the DMRS signals.However, a major problem of sensing ambiguity is also noteddue to the interleaved patten of the subcarriers.

2) Non-Channel Estimation Signals: Several deterministicnon-channel estimation signals such as the synchronizationsignal and broadcast blocks (SSB) can also be used forsensing. Such signals typically have regular patterns witha periodic appearance at an interval of several to tens ofmilliseconds. However, they only occupy a limited numberof subcarriers, which may lead to limited identification ofmultipath delay values.

3) Data Payload Signals: In addition, we can also exploitthe data payload signals for sensing. In downlink sensing, thedata symbols are known to the sensing receiver and hencecan be directly used. In uplink sensing, symbols need to beused in a decision-directed mode. Since these data symbolsare random and signals in different spatial streams are non-orthogonal, they are not ideal for sensing. If it is used foruplink sensing, the signals need to be demodulated first, whichcould also introduce demodulation error. However, they cansignificantly increase the number of available sensing signals,and hence improve the overall sensing performance at the costof increased complexity. Precoders for these signals can beoptimized by jointly considering the requirements from C&S.

IV. REQUIRED SYSTEM MODIFICATIONS

C&S can share a number of processing modules in a MIMO-OFDM transceiver, as illustrated in Fig. 3. The whole transmit-ter and many modules in the receivers that are shown in purpleare shared by C&S. The transmitted signal waveform can beoptimized by jointly consider the requirements for C&S, aswill be detailed in Section VI-B. Note that sensing parameterestimation can be done in both time domain and frequencydomain. The sensing applications may demand either sensingparameter estimation or pattern recognition results, or both.

Despite the numerous modules shareable by C&S, somemodifications at hardware and network levels to existingmobile networks are necessary for realizing PMNs. As dis-cussed in Section II-A, communication signals can generallybe directly used for estimating sensing parameters, but thecommunication system platform is not directly ready for sens-ing. On one hand, a communication node does not have thefull-duplex capability at the moment, that is, transmitting andreceiving signals of the same frequency at the same time. Thismakes mono-static radar sensing infeasible without modifyingcurrent communication infrastructure. On the other hand, fortransmitter and receiver in two nodes spatially separated, thereis typically no clock synchronization between them. This cancause ambiguity in ranging estimation, and makes processingsignals across packets difficult. Thus bi-static radar techniquescannot be directly applied in this case. These are fundamentalproblems that need to be solved at the system level, to makesensing in primary communication systems feasible.

We now describe the modifications of current hardware andsystems that are required to evolve current communicationonly mobile networks to PMNs. The depicted changes focus onthe fundamental reforms that allow the current mobile networkto do radio sensing simultaneously with communication. Inthis section, we do not consider low-level changes such asjoint waveform optimization [14], [15], [74], joint antennaplacement and sparsity optimization processing and poweroptimization [53], but leave them to Section VI.

For uplink sensing, if the sensing ambiguity in time andDoppler frequency can be tolerated, no change to hardwareand system architectures of current mobile systems is required.Otherwise, achieving non-ambiguous sensing in the PMNspotentially requires dedicated (static) UEs that are clock syn-chronized to BSs. Such ambiguity may also be resolved using

13

Transmitter (Multiuser‐MIMO OFDMA Signal) Channel

RF Processing

A/D conversion

Convert to frequency domain

synchronization

MIMO Equalization 

Channel Estimation

Demodulation and decoding

Pattern Recognition

Sensing Parameter Estimation

Sensing Applications

Fig. 3: A block diagram of a transceiver showing the compo-nents that can be shared by C&S. The blocks in purple areshared by C&S; blocks in blue and black are for communica-tions and sensing only, respectively.

signal processing techniques as will be detailed in SectionVI-F.

For downlink sensing, the leakage and reflected signalsfrom the transmitter can cause significant interference to thereceived signals. Resolving this problem would ideally requirefull-duplex technologies [76]. The full duplex technology,which uses a combination of antenna separation, RF sup-pression and baseband suppression to mitigate the leakagesignal from transmitter to receiver, is a potentially long-termsolution to enable seamless integration of downlink sensingwith communications. However, it is still very challenging toimplement particularly for MIMO system, and the technologyis immature and impractical for real implementations.

Referring to Fig. 2, there exist near-term solutions forrealizing JCAS in PMNs, where radio sensing can be realizedin some suboptimal way without full-duplexing, requiring onlya few slight modifications on hardware and system to theexisting network. These solutions are detailed below.

A. Dedicated Transmitter for Uplink Sensing

Conventionally, the phase clock between UEs and BSsis not synchronized; hence, the sensing ambiguity problemis present in uplink sensing. To eliminate the ambiguity,dedicated (static) UEs that are clock-synchronized to BSs canbe used. In terms of the required system modification, uplinksensing by static UE would be the most convenient way forachieving non-ambiguity sensing in the PMNs. This is shownas RRU3 in Fig. 2 for a CRAN, where RRU3 can be modifiedto operate as a UE, transmitting uplink signals.

B. Dedicated Receiver for Downlink Sensing

For downlink sensing without requiring full-duplexing ca-pability, one option is to deploy a BS that only works onthe receiving mode. It can be configured as a receiver either

for downlink sensing only or for both communication anddownlink sensing.

To implement this near-term downlink sensing, changesto the hardware may be required. This is because the re-ceiver in current BSs is conventionally designed to receiveuplink communication signals only, and downlink sensingrequires the receiving of downlink communication signals. Therequired change is insignificant for time-division duplexing(TDD) systems since a TDD transceiver generally uses aswitch to control the connection of antennas to the transmitteror receiver. Thus the change is only the adjustment of thetransmitting and receiving period so that the switch is equiva-lently always connected to the receiver. For frequency divisionduplexing (FDD) systems, the BS receivers may be incapableof working on downlink frequency bands, and modification tothe hardware is required. Therefore, it is more cost-effectiveto implement downlink sensing in TDD than in FDD systems.

Alternatively, we can also deploy a dedicated receiving-only node for both downlink and uplink sensing, as wellas communications if desired. This is particularly feasiblefor TDD systems. In TDD systems, downlink and uplinksensing signals can then be (largely) separated in time at thereceiver. Even if this node only has one receiving antenna,we can still use its collected signal to estimate the angle ofdeparting (AoD) values if multiple antennas are applied inthe transmitter with position known to the receiver [77]. Ofcourse, to remove the ambiguity in delay estimation, clocksynchronization is required between the transmitters and thisnode. An example is shown as RRU2 in Fig. 2 for a CRAN,which can perform downlink and uplink sensing using receivedsignals from RRU1 and RRU3, respectively.

C. BS with Spatially Widely Separated Transmitting and Re-ceiving Antennas

One possible solution for downlink sensing is to use well-separated transmitting and receiving antennas. The large sepa-ration will significantly reduce the leakage from transmittedsignals. The receiver baseband also accepts feedback fromthe transmitter baseband, so that a baseband self-interferencecancellation may be further applied. However, this spatiallywell-separated antenna structure requires extra antenna instal-lation space and can increase the overall cost. One option ofminimizing the cost is to use a single antenna for receivingsensing signals.

Fig. 4 shows an example of this option in TDD systems.The system has a normal transceiver for communication withfour antennas. A fifth antenna is installed at a position wellseparated from the four antennas, and it is connected to thereceiver via a long cable. Signals from the fifth antenna areused for downlink active sensing. Fig. 4-(a) plots the generalconcept, and Fig. 4-(b) shows a potential implementation inexisting FDD systems. The switches (SPDT1-4) are operatingnormally for a TDD communication system. For the fifthantenna, it is always connected to the fifth receiver. Given thatthe on-board circuit leakage is small and the TDD switchescan be separately controlled, this option can be convenientlyrealized in an existing TDD system that supports 5x5 MIMO.

14

Normal Tx/Rx

Sensing receiver only

SPDT1

Tx1

SPDT2

SPDT3

SPDT4

Rx1 (C)

Tx2

Tx3

Tx4

Rx2 (C)

Rx3 (C)

Rx4 (C)

Rx5 (S)

(a)(b)

Fig. 4: Simplified TDD transceiver model with a single re-ceiving antenna dedicated to sensing. (a) A general concept;(b) Possible realization on existing hardware platform.

Sensing using a single receiving antenna in this case can berealized by exploring the multiple transmitted spatial streams[77].

V. MAJOR RESEARCH CHALLENGES FOR PMN

There exist a number of challenges in the research anddevelopment of PMN. These challenges are mainly associatedwith realizing sensing on the infrastructure of communicationnetworks, joint design and optimization, exploring the mutualbenefits of communication and sensing via the integration,and sensing in a networked environment. In this section, wewill discuss several major research challenges from the signalprocessing aspect. In next section, we will then review detailedtechnologies and algorithms that have been developed toaddress these challenges, and present remaining open researchproblems and future directions.

A. Sensing Parameter Extraction from Sophisticated MobileSignals

Sophisticated signal structure of mobile networks makessensing parameter estimation in PMNs challenging. A mod-ern mobile network is a complex heterogeneous network,connecting diverse devices that occupy staggered resourcesinterleaved and discontinued over time, frequency and space.Mobile signals are also very complicated because of multiuseraccess, diverse and fragmented resource allocation, and spatialmultiplexing. The communication signals that are also usedfor sensing are randomly modulated using multiuser-MIMOand OFDMA technologies and can be fragmented for eachuser - discontinuous over time, frequency or space. Thisstructure is detailed in our work in [12]. Most existing sensingparameter estimation techniques are not directly applicable tothe PMNs because of such signal structure. For example, activeradar sensing technologies mostly transmit linear FM (LFM)chirp modulated transmitted signals [21]; and most passivebistatic and multistatic radars consider simple single carrierand OFDM signals [22]–[24], [78]. In addition, conventionalspectrum analysis and array signal processing techniques, suchas MUSIC [1] and ESPRIT [52], are not applicable either, as

they require continuous observations that are not constantlyexisting here. As a result, specific sensing techniques needto be developed for estimating sensing parameters from thecomplicated and fragmented signals.

Sensing parameters describe the propagation of signals inthe environment and the detailed composition of channels.They typically have continuous but not discrete values. Thusmost existing channel estimation and localization algorithmsare not directly applicable either. Existing channel estimationtechniques developed for modern mobile networks principallyemphasize on estimating composite channel coefficients atquantized discrete grids, and localization mainly focus onthe line-of-sight path and determines the locations of signalemitting objects. However, some recent techniques developedfor channel estimation in millimeter wave systems [79], [80]can potentially be extended and applied to sensing parameterestimation, as will be detailed in Section VI-E.

B. Clutter Suppression

Rich multipath in mobile networks creates another chal-lenge for sensing parameter estimation in PMNs. In a typicalenvironment, BSs receive many multipath signals that areoriginated from permanent or long-period static objects. Thesesignals are useful for communications, but for a fixed BS, theyare generally not of interest for continuous sensing becausethey bear little new information. Such undesirable multipathsignals are known as clutter in the traditional radar literature.

Although high-end military radar can simultaneously detectand track hundreds of objects, the capability is built onadvanced hardware such as huge antenna arrays of hundredsand thousands of antenna elements. For a PMN BS with tens ofantennas, the sensing capability largely depends on the sensingalgorithm, which is closely related to the number of unknownparameters. Most existing parameter estimation algorithmsrequire more measurements than unknown parameters and theestimation performance typically degrades with the numberof unknown parameters increasing. Therefore, it is crucial toidentify and remove non-information-bearing clutter signalsfrom the input to a sensing parameter estimator.

Clutter suppression techniques for conventional radars arenot directly applicable here because the signals and workingenvironment for the two systems are very different. Typicalradar systems are optimized for sensing a limited number ofobjects in open spaces using narrow beamforming, and clutteris typically from ground, sea, rain, etc. and has notable distinctfeatures [1], [81]–[83]. The well-known algorithms in radarsystems, such as space-time adaptive processing (STAP) [81],[84], independent component analysis (ICA) [82], singularvalue decomposition (SVD) [85] and Doppler focusing [86],are adapted to such scenarios. For communications, narrowbeamforming may occur in emerging millimetre wave systems,but not in more general microwave radio systems due to thelimited number of antennas and the use of multibeam tech-nology to support multiuser MIMO. The signal propagationenvironment in PMNs can also be very complex and differentfrom typical radar working environment. Therefore, existingclutter suppression methods developed for radar systems, e.g.,

15

those in [83], [87], [88], may not directly suit for clutterreduction in PMNs.

C. Joint Design and Optimization

One key research problem in JCAS, as well as PMNs, ishow to jointly design and optimize signals and systems forC&S. A number of studies have investigated the impact ofthe waveform and basic signal parameters on the performanceof a joint system, as will be detailed in Section VI-B. Suchwaveform and system parameter optimisation can result inperformance improvement in standalone systems, but it hasless impact compared to those at high levels, i.e., system andnetwork levels.

C&S have very different requirements at the system andnetwork levels. For example, in a multiuser MIMO commu-nication system, the transmitted signal is a mix of multi-usersrandom symbols, while ideal MIMO-radar sensing signals areunmodulated and orthogonal [89]. When using an array, radarsensing focuses on optimising the formation and structureof virtual subarrays to increase antenna aperture and thenresolution [90], but communication emphasises beamforminggain and directivity. Such conflicting requirements can makejoint design and optimisation very challenging. More researchis required to exploit the commonalities and suppress theconflicts between the two functions.

Another important issue is how C&S can benefit more fromeach other via the integration. This is far from being wellunderstood. Current research has been limited to propagationpath optimization such as the work in [91].

D. Networked Sensing

Integrating sensing into mobile communication networksprovides great opportunities for radio sensing under a cellularstructure. However, research on sensing under a cellular topol-ogy is still very limited. The cellular structure for communica-tion is designed to greatly increase the frequency reuse factorand hence improve spectrum efficiency and communicationcapacity. A cellular sensing network intuitively also increasesfrequency reuse factor, and hence the overall “sensing” capac-ity. On one hand, there is almost no known performance boundfor such cellular sensing networks yet, except for a limitednumber of slightly related works, such as performance analysisfor coexisting radar and cellular communication systems [92]and radar sensing using interfered OFDM signals [52]. Onthe other hand, although research exists on distributed radarand multi-static radar, sensing algorithms that consider andexploit the cellular structure, such as co-cell interference, nodecooperation, and sensing-handover over base-stations, are yetto be developed. The challenge lies in the way to address com-petition and cooperation between different base-stations underthe cellular topology, for both performance characterisationand algorithm development of networked sensing.

VI. DETAILED TECHNOLOGIES AND OPEN RESEARCHPROBLEMS

As a new platform and network, PMN is still in its very earlystage of research and development. As described in the last

section, there are a number of challenges to overcome to makeit practical, which also imply great research opportunities.Here we review existing technologies and algorithms thathave been developed to address these challenges, organizedunder eight topics. We also discuss open research problemsfor each topic. In Table VI, remarks are provided on thetechnology maturity and research difficulty for each topic andhighlights selected key open research problems. The scores formaturity and difficulty are indicative only, as they are basedon our own expertise and experience. Since the major issue inPMN is how to achieve radio sensing without compromisingthe performance of existing communications, we focus onthe issues in realizing radio sensing, leveraging the existingcellular communication infrastructure.

A. Performance Bounds

There are two types of performance bounds that can be usedto characterize the performance limits of sensing in PMNs.One is based on the mutual information (MI), and the other isbased on the estimation accuracy of sensing parameters, suchas the Cramer-Rao lower bounds (CRLBs).

MI [93] can be used as a tool to measure both the radarand communication performance. To be specific, for commu-nications the MI between wireless channels and the receivedcommunication signals can be employed as the waveformoptimization criterion, while for sensing, the conditional MIbetween sensing channels and the sensing signals is used [94]–[96]. So MI for sensing measures how much informationabout the channel, the propagation environment, is conveyedto the receiver. Maximizing MI hence is particularly useful forsensing applications that rely more on feature signal extractionthan on sensing parameters estimation, for example, for targetrecognition. The usage of MI and capacity is well known tothe communication community. The usage of MI for radarwaveform design can also be traced back to 1990s [94]. MIhas also been used to optimize the performance of coexistingradar communication systems, e.g., in [97].

Let us consider simplified signal models to illustrate theformulations of MI. Let Hs , Hc denote the channels for sens-ing and communications, respectively; let Ys and Yc be thereceived signals for sensing and communications, respectively;and let X = f (S) be the transmitted signals where S is theinformation symbols and f (S) denotes the function convertingS to X. The function f (S) can be linear or nonlinear, andcan be across multiple domains including time, frequency,andspatial domains. When a spatial precoding matrix P is used inplace of f (·), the received signals are given by

Ys = HsPS + Z;Yc = HcPS + Z,

for sensing and communications, respectively. The MI expres-sions for communication and sensing can be represented as

I(Hs; Ys |X) = h(Ys |X) − h(Z), for sensing;I(S; Yc |Hc) = h(Yc |Hc) − h(Z), for comm., (6)

where I(·) denotes the (conditional) MI, and h(·) denotesthe entropy of a random variable. We can thus see that

16

TABLE VI: Technology matureness, research difficulty and selected key open research problems. Higher scores stands formore mature, and more difficult.

Research Topics TechnologyMatureness(1 to 10)

ResearchDifficulty (1to 10)

Selected Key Open Research Problems

PerformanceBounds

5 7• MI formulation specific to PMNs by considering uplink and downlink sensing,

and actual signal and packet structure;• Closed-form expressions of CRLB of sensing parameter estimation for broadband

mobile signals;• Combine MI and other metric such as CRLB of estimates for performance

characterization.

Waveformoptimization

6 5• Waveform optimization for hybrid antenna arrays;• Low-complexity optimization schemes that can be quickly adapt to channel

variation in both C&S;• Multiuser correlation in waveform optimization for uplink sensing.

Antenna arraydesign

3 7• Using virtual array and antenna grouping techniques to achieve balance between

processing gain and resolution in sensing, and diversity and multiplexing incommunications;

• Sparse array design and signal processing in PMNs.

Clutter suppres-sion

7 5• Parameter optimization in the recursive moving averaging method;• Low-complexity algorithms for parameter estimation in Gaussian mixed model.

Sensing parame-ter estimation

3 8• Off-grid compressive sensing with discontinuous samples;• Off-grid Tensor signal processing algorithms;• Sensing parameter estimation with clustered multipath channels;• Resolution of sensing ambiguity with asynchronous nodes.

Pattern analysis 2 5• Application-driven problem formulation and pattern analysis;• Environment robust algorithms.

Networked sens-ing

1 8• Fundamental theories and performance bounds for cellular sensing networks;• distributed sensing with node grouping and cooperation.

Sensing-assisted securecommunication

2 6 Characterize the Secrecy capacity and develop practical code design methods forinformation encryption using sensing results.

the MI for communications and sensing are defined as themetrics conditional on the channel and the transmitted signals,respectively. In each case, the signals X, or more specificallythe precoder P, are optimized to maximize the MI.

MI for JCAS systems has been studied and reported ina few publications. They are typically conducted by jointlyoptimizing the two MI expressions and their variations in (6).The work in [98] formulates radar mutual information andthe communication channel capacity for a JCAS system, andprovides preliminary numerical results. In [93], radar wave-form optimization is studied for a JCAS system by maximizingMI expressions. In [5], the estimation rate, defined as the MIwithin a unit time, is used for analyzing the radar performance,together with the capacity metric for communications. In [99],an optimized OFDM waveform is proposed by maximizinga weighted sum of the communication data rates and theconditional mutual information for radar detection in an JCASsystem.

These available results serve as good bases for studyingthe MI for PMNs. Some specific problems to PMNs can beconsidered to make the results more practical.

• Firstly, the MI formulations for uplink and downlinksensing are different, due to the different knowledge onthe transmitted signals and the channel differences. In thedownlink sensing, the symbols are known to the receiver,and the channels for C&S are different but could becorrelated. For uplink sensing, the symbols are unknownto the receiver and the channels are the same for C&S.Hence, the optimization objective functions and resultscan be quite different for uplink and downlink sensing.

• Secondly, the specific packet and signal structures incellular networks can have significant impact on MI forboth C&S. For example, a packet signal may includetraining sequence and data symbols which will lead todifferent MI formulation and results, as their statisticalproperties are different. In [100], the MI is studied forPMN, considering the frame structure and estimationerrors. The findings from [100] indicate that the optimalsolution for one function (communication or sensing)is generally not optimal for the other, and some trade-off needs to be made, particularly when the require-ments for C&S are very different, for example, when

17

the directions of sensing and communications deviatesignificantly. This implies the importance of sensing-motivated user scheduling, i.e., taking user schedulinginto joint optimization of C&S.

CRLB is a more traditional metric that has been widely usedin characterizing the lower bound of parameter estimation inradar [101]–[103]. For PMN, the closest work on CRLB isreported in [104], in the scenario of passive sensing usingUMTS (3G) narrowband mobile signals. However, the CRLBexpressions are not always available in closed-form, particu-larly for MIMO-OFDM signals, primarily because the receivedsignals are nonlinear functions of sensing parameters. There-fore, although they can be found and evaluated numerically,the CRLB metrics are not easy to be applied in analyticaloptimization. It is even harder to apply them in optimization,jointly with another cost function.

MI has also been combined with other metrics to studythe performance of radar systems. For example, two criteria,namely, maximization of the conditional MI and minimizationof the minimum mean-square error (MMSE), are studiedin [95] to optimize the waveform design for MIMO radar byexploiting the covariance matrix of the extended target impulseresponse. In [105], the optimal waveform design for MIMOradar in colored noise is also investigated by considering twocriteria: by maximizing the MI and by maximizing the relativeentropy between two hypotheses that the target exists or doesnot exist in the echoes. Research for JCAS and PMNs basedon these combined criteria is still very limited.

A brief summary of recent analytic results of using MI inJCAS system is given in Table VII.

B. Waveform Optimization

For JCAS, joint waveform optimization is a key researchproblem as the single transmitted signal is used for bothfunctions but the two functions have different requirementsfor the signal waveform. As discussed in Section II-A, tra-ditional radar and communication systems use very differentwaveforms, which are optimized for respective applications.For example, recall that radar uses orthogonal and unmod-ulated pulsed or continuous-waveform frequency modulatedsignals, while in PMNs, typically the signals are random, withmulticarrier modulation and multiuser access. However, thewaveform for one function may be modified to accommodatethe requirements of the other, under joint design and optimiza-tion. The work in [1] is one of the earliest ones that investigatewaveform design for JCAS systems. The waveform design andsignal parameters can have a significant impact on the overallperformance of a JCAS system. For example, the numericalanalysis in [36] demonstrates the close linkage between thesensing resolution capabilities and the signal parameters forboth single carrier and multicarrier communication systems.

For PMNs, apart from the MI-based waveform optimizationas discussed in Section VI-A, there are two more practicalmethods. One method is optimizing the signal conversionfunction f (·), or specifically the precoding matrices Pq,n,t in(5) when f (·) = P, to make the statistical properties of thetransmitted signals X best suitable for both C&S. Another

method is to add the sensing waveform to the underlyingcommunication waveform, while considering coherent com-bination of the two waveforms for destination nodes. The twomethods have respective advantages and disadvantages. Weelaborate them below.

In the first method, when optimization is with respect to thespatial precoding matrix, it is designed to alter the statisticalproperties of the transmitted signal. This method is particu-larly suitable for global optimization of cost functions jointlyformulated for C&S. The basic optimization formulation is asfollows:

arg maxP

λ(P),

subject to Constraints 1, 2 · · · , (7)

where λ(P) is the objective function. There could be variousmethods and combinations in defining the objective functionsand the constraints. Each can be either for communicationor sensing individually, or a weighted joint function. Someexamples are as follows. In [14], waveform optimization isrealized via minimizing the difference between the generatedsignal and the desired sensing waveform under the restrictionsof signal-to-interference-and-noise ratio (SINR) for multiuserMIMO downlink communications. A multi-objective functionis further utilized to trade off the similarity between thegenerated waveform and the desired one [15]. In [106],adaptive weighted-optimal and Pareto-optimal waveform de-sign approaches are proposed to simultaneously improve theestimation accuracy of range and velocity and the channelcapacity for communication. In [55], the weighting vectorfor subcarriers in OFDM systems is optimized by consideringa multi-objective function involving communication capacityand CRLBs for the estimates of sensing parameters. One maindisadvantage of this method is that, the precoding matrix needsto be optimized or redesigned once the communication orsensing setup changes.

In the second method, basic waveforms can be designed inadvance for either or both C&S, and the two waveforms arethen added in a way to jointly optimize the performance ofC&S. The basic idea can be mathematically represented asfollows:

argmaxα,Pc,or Ps

λ(P),

subject to Constraints 1, 2 · · · , (8)

where Pc and Ps are the precoding matrices primarily targetedfor communication and sensing, respectively, and P is afunction of Pc and Ps . Examples are P = αPc + (1−α)Ps andP = [αPc, (1−α)Ps] where α is a complex scalar. Here, among{α,Pc,Ps}, either Pc , Ps , or both of them can be pre-designedand fixed, while real-time optimization is with respect to theother parameters. Although the results may be suboptimal,this method provides great flexibility and can adapt quicklyto changes on the requirements for C&S.

This method could be particularly useful for mmwave sys-tems where directional beamforming is used. One example isavailable from [13], where a multibeam approach is proposedto flexibly generate communication and sensing subbeams

18

TABLE VII: Recent analytic results for usage of MI in JCAS and related systems.

Reference Models Main contributions

[5] JCAS system consists of an active, mono-static,pulsed radar and a single user communicationssystem.

MI is used for analyzing the radar performance, together withthe capacity metric for communications.

[93] Radar and cellular system share the same spec-trum for simultaneous operation.

Radar waveform optimization is studied by maximizing MIexpressions.

[98] MIMO radar based JCAS system. Formulate radar MI and the communication channel capacity.

[99] Mono static radar transceiver is employed fortarget classification while simultaneously usedas a communications transmitter

Propose an OFDM waveform optimized by maximizing aweighted sum of the communication data rates and theconditional MI for radar detection.

[100] JCAS MIMO downlink system with a signalpacket structure, including training sequence andinformation data symbols.

MI is studied for PMN, considering the frame structure andestimation errors.

using analogue antenna arrays. Optimization of combining thetwo subbeams is further investigated in [37], [38]. Of course,the efficiency of multibeam is related to the requirements ofC&S. According to [71], getting the correct solutions of beamsteering and beamwidth adaptation for JCAS operation highlydepends on environmental context. Indeed, reflector position,blockage height, motion speed and other environmental con-text factors could have a significant impact on the efficiencyof the multibeam method.

For waveform optimization in PMNs, the following specificproblem associated with multiuser access is yet to be consid-ered, particularly for uplink sensing. For downlink sensing,multiuser access and multiuser interference only need tobe considered for communications, because the transmittedsignals are known to the sensing receiver and the environmentto be sensed is common to multiuser signals. Thus waveformoptimization only needs to consider the multiuser aspectfor communication, as studied in [15]. However, for uplink,signals need to be specific to each user for both C&S, becausethe signal propagation environments between different usersand the BS could be different. But these environments couldalso be correlated. Thus waveform optimization in the uplinkis a more challenging task.

A brief summary of recent analytic results of waveformoptimization in JCAS system is given in Table VIII.

C. Antenna Array Design

For radio sensing, each antenna with an independent RFchain is like a pixel in the camera. But a radio systemallows more flexible control and processing of both transmittedand received signals. Therefore, there are more designs forantenna arrays in PMNs that we can do apart from theMIMO precoding for waveform optimization as discussed inlast subsection. Below, we exemplify two research topics onantenna array design.

1) Virtual MIMO and Antenna Grouping: There are manycontradictory requirements for antenna array design betweenC&S. Beamforming and antenna placement are two goodexamples. For beamforming, an array with steerable beam-forming and narrow beamwidth is typically required for sens-ing; however, communications require fixed and accurately

pointed beams to achieve large beamforming gain. For an-tenna placement, increment of antenna aperture is the mainconcern for radar [90], while MIMO communication focuseson beamforming gain for spatial diversity and low correlationamong antennas for spatial multiplexing. These different andcontradicting requirements require some new antenna designmethods.

One potential solution is to introduce the concept of antennagrouping and virtual subarrays [107]. By dividing existingantennas into two or more groups, we can designate tasks ofC&S and optimize the design across groups of antennas. Therecould be overlap between different groups of antennas. Usingvirtual subarrays, we can conveniently generate multibeams[13] satisfying different beamforming requirements from C&S.We can also virtually optimize the antenna placement, by an-tenna selection and grouping. While designing the virtual sub-arrays, we can explore the following commonalities betweenMIMO communication and radar. Similar to the diversity andmultiplexing trade-off in communications, there is a trade-offbetween processing gain and resolution in sensing, related tothe number of independent spatial streams.

Considering the benefits of antenna grouping for both C&S,using hybrid antenna arrays [59], [108] will be an attractivelow-cost option. This is particularly true for mmWave systemswhere propagation loss is high and beamforming gain isessential for achieving sufficiently high SNR for both C&S.The research on hybrid array JCAS systems is still in its veryearly stage.

2) Sparse Array Design: Besides antenna grouping, sparsearray design is another method to exploit the degrees offreedom that can be achieved via configuring the locationsof antennas when the total number of antennas is fixed.

Sparse array design, such as coprime array [109], is oftencast as optimally placing a given number of antennas on alarger number of possible uniform grid points [110]. In thisway, a small number of antennas can span a large arrayaperture with a high spatial resolution and low sidelobes.So far, the sparse array design-based JCAS has mainly beenstudied in integrating communication to radar systems, i.e.,embedding information into radar waveforms to perform datacommunication [53], [110]. In [110], antenna position and

19

TABLE VIII: Recent analytic results of waveform optimization in JCAS.

Reference Models Main contributions

[1] Joint implementation of digital beamformingradar and MIMO communications.

Investigate waveform design for JCAS systems.

[14] Joint design of MIMO radar and multi-userMIMO communication

Waveform optimization is realized via minimizing the dif-ference between the generated signal and the desired sensingwaveform under the restrictions of SINR for multiuser MIMOdownlink communications.

[37] C&S at different directions, using a commontransmitted signal

Optimization of combining both of the communication andsensing subbeams is investigated.

[55] OFDM JCAS system The weighting vector for subcarriers in OFDM systems isoptimized by considering a multi-objective function involvingcommunication capacity and CRLB for the estimates ofsensing parameters.

[106] OFDM integrated radar and communication sys-tem

Adaptive weighted-optimal and Pareto-optimal waveform de-sign approaches are proposed to simultaneously improve theestimation accuracy of range and velocity and the channelcapacity for communication.

beamforming weights are optimized to design beams withmainlobe performing radar detection and sidelobe for commu-nications through modulations like ASK or PSK. In [53], theMIMO waveform orthogonality is further exploited to permutethe waveform across selected antenna grids and hence conveyextra information bits.

Sparse array design is particularly suitable for massiveMIMO array with tens to hundreds antennas but a limitednumber of RF chains, i.e., switched arrays or hybrid arrays.This setup can provide more degrees of freedom and potentialperformance enhancement, with reduced cost, in PMNs. Forexample, the sparse array design can add index modulationto the communication part; while the sparse array design canprovide better spatial resolution for radar detection. To thisend, some interesting problems remain to be solved, such ashow to formulate the problems with two goals satisfied andnew trade-offs between C&S.

3) Spatial Modulation: Spatial modulation uses the setof antenna indexes to modulate information bits and havebeen extensively investigated for communication systems. Formulti-antenna JCAS systems, spatial modulation can alsobe potentially applied. In [9], [49], a concept similar tospatial modulation is exploited to increase communicationdata rate in a frequency-hopping MIMO DFRC system. In[111], spatial modulation is applied to JCAS by allocatingantenna elements based on the transmitted message, achievingincreased communication rates by embedding additional databits in the antenna selection. A prototype is developed in [111]and demonstrates that the proposed scheme can improve theangular resolution and reduce the sidelobe level in the transmitbeam pattern compared to using fixed antenna allocations.

Although these works are based on pulsed and continuous-waveform radars, they can potentially be extended to PMN,by adding antenna selection to existing space-time modula-tions. In particular, the rich scattering environment in PMNprovides lower correlation between spatial channels, leadingto potentially better performance.

A brief summary of recent analytic results of antenna array

design in JCAS system is given in Table IX.

D. Clutter Suppression Techniques

In PMNs, we treat multipath signals as clutter if they remainlargely unchanged and have near-zero Doppler frequenciesover a period of interest. A lot of clutter could be present inthe received signals because the rich multipath environmentof mobile networks. Clutter contains little information and isbetter to be removed from the signals being sent to the sensingparameter estimator.

As discussed in Section V-B, clutter suppression techniquesin traditional radar [1], [81]–[83] may be improved and used inPMNs, but they cannot be directly applied. These techniquestypically need to exploit different features of desired andunwanted echoes, such as low correlation between them. Thesedifferent features may not always be available in mobilenetworks, because the desired multipath and clutter can comefrom the same classes of reflectors.

Alternative approaches exploit the correlation in time, fre-quency and space domains, and use recursive averaging ordifferential operation to construct or remove clutter signals[12], [64]–[66], [66], [112], [113]. These approaches couldbe more viable for perceptive mobile networks. They havesimilarities to background subtraction in image processing[114]. However, there are two major differences:

• In image processing, the difference between two imagesis exhibited via pixel variation. In radio sensing, bothDoppler shifts and variation in sensing parameters causedifference in received sensing signals at different time;

• In an image, background is overlapped/covered by fore-ground. In radio sensing, clutter and desired multipathsignals are typically additive, and coexist in the receivedsignals.

Nevertheless, the many background subtraction methods de-veloped for image processing can be revised and appliedfor radio sensing in PMNs. Below we review two types oftypical background subtraction techniques that can be used

20

TABLE IX: Recent analytic results for antenna array design in JCAS.

Reference Models Main contributions

[13] C&S at different directions, using a commontransmitted signal.

Generate multibeams satisfying different beamforming re-quirements from C&S.

[53] Joint MIMO-radar communications platformequipped with a reconfigurable transmit antennaarray.

MIMO waveform orthogonality is exploited to permute thewaveform across selected antenna grids and hence conveyextra information bits.

[110] Joint radar communications platform equippedwith a re-configurable transmit antenna arraythrough an antenna selection network.

Antenna position and beamforming weights are optimized todesign beams with mainlobe performing radar detection andsidelobe for communications through modulations like ASKor PSK.

[111] Joint system equipped with a phased array an-tenna implementing active radar sensing whilecommunicating with a remote receiver.

Spatial modulation is applied to JCAS by allocating antennaelements based on the transmitted message, achieving in-creased communication rates by embedding additional databits in the antenna selection.

in PMNs: recursive moving averaging (RMA) and Gaussianmixture model (GMM).

1) Recursive Moving Averaging (RMA): Assume sensingparameters are fixed over the coherence time period, thenideally the received signals for each path at two differenttimes will only have a phase difference caused by the Dopplerphase shift. If the Doppler frequency is near zero, then the twosignals are nearly identical. Based on this assumption, we canuse an RMA method [12] to estimate the clutter and thenremove it from the received signal.

The received signals as shown in, e.g.,(3) and (5), cannotbe directly used in RMA, due to the transmitted signals x andpossibly the unknown timing offset τo which introduce a time-varying phase shift across discontinuous packets. They need tobe removed or compensated before RMA can be applied. Todemonstrate the idea of RMA, we refer to the signal strippingapproach [12]. We assume that τo = 0 and an estimate of thechannel matrix Hn is available, given by

Hn = Hn + ∆n, (9)

where ∆n is the reconstruction error.The RMA method uses a small forgetting vector to re-

cursively average the received signal over a window, witha length sufficiently large to allow suppressing time-varyingsignals of non-static paths, but smaller than the coherent time.Mathematically, it can be represented as a recursive equation

Hn(i) = αHn(i − 1) + (1 − α)Hn(i), (10)

where Hn(i) is the output at the i-th iteration, α is theforgetting factor (learning rate), and the initial Hn(1) can beeither zero or computed as the average of several initial Hn(i)s.In RMA, the window length can be adapted to the variationspeed of the channels. The time interval between the inputs tothe averaging determines how signals with different Dopplerfrequencies are added, either constructively or destructively.Hence it has a significant impact on suppressing signalsof different Doppler frequencies. The forgetting factor andthe window length determine the suppression power ratio.Although experimental results have been reported in [12]for the relationship between these parameters and the effectof clutter estimation and suppression, optimal combinations

of these parameters, in consideration of channel statisticalproperties, are yet to be studied.

Although the RMA method works well in principle, it maybecome inefficient due to practical issues, such as timing andfrequency offset commonly existing in actual systems. Thesesignal imperfectness needs to be well compensated before theRMA method can work effectively.

2) Gaussian Mixture Model: GMM has been widely usedfor analyzing and separating moving objects from the back-ground in image and video analysis [114], target identificationand classification in radar system [115], and positioning solu-tions [116]. The statistical learning of the GMM model withrespect to the mean and variance in background subtraction isused to determine the state of each pixel whether a pixel isbackground or foreground. It has also been applied recentlyto extract static channel state information from channel mea-surement in [117]. Different from GMM in video analysiswhere background and foreground overlap each other, clutterand multipath of interest in PMNs are additive and cancoexist. Therefore, it is infeasible in PMNs to place foreground(dynamic signals) and background (static signals) into twodifferent sets by classical clustering approaches that happenedin image or video signal processing.

GMM’s working principle for clutter suppression in PMNsis as follows. Wireless channels can be modeled and estimatedby a mixture of Gaussian distributions since each densityrepresents multipaths in the channel [117]. Static and dynamicpaths can be represented by Gaussian distributions with verydifferent parameters over the time domain. This is becauseover a short time period, static paths change little and dynamicpaths may vary significantly. It is also quite common thatstatic paths typically have larger mean power than dynamicones. Hence, in terms of their distributions, static paths havenear-zero variances, which are much smaller than those of thedynamic ones. Therefore, by learning the mean values of thedistribution, static paths can be identified and separated viacomparing the variance.

The main advantage of GMM for clutter estimation in PMMis that much less samples are required to achieve a givenaccuracy, compared to the matched filtering and RMA meth-ods. However, the estimation usually needs to be realized by

21

0 5 10 15 20 25

Signal to Interference Ratio (dB), Υ

10-6

10-5

10-4

10-3

10-2

Clu

tte

r e

stim

atio

n R

MS

E (

dB

) RMA @ r=10

RMA @ r=150

GMM-EM-CE @ Nm

=10

Fig. 5: Simulation results comparing different clutter suppressionmethods.

high-complexity algorithms such as expectation maximization.Low-complexity estimation based on the GMM formulation isa key research problem here.

Fig. 5 compares the root mean square error (RMSE) resultsfor clutter estimation between RMA and GMM methods. Thesignal to interference ratio Υ denotes the ratio between clutter-to-dynamic power ratio. The estimation for GMM is based on10 samples. For RMA, the forgetting factor is 0.95 over 10and 150 samples. According to the figure, the GMM methodachieves significantly lower RMSE for clutter estimation thanRMA at both r = 10 and 150 iterations.

E. Sensing Parameter Estimation

The tasks of sensing in PMNs include both explicit es-timation of sensing parameters for locating objects and es-timating their moving speeds, and application oriented pat-tern recognition such as object and behaviour recognitionand classification. In this subsection, we review research onsensing parameter estimation, considering typical multiuser-MIMO OFDM signals used in modern mobile networks. Wewill review work on pattern recognition in subsection VI-G.

We note that sensing parameter estimation is a non-linearproblem, and hence most classical linear estimators, whichhave been widely used in channel estimation in communi-cations, cannot be applied. Here, we review the followingtechniques: periodogram such as 2D DFT, subspace basedspectrum analysis techniques, on-grid compressive sensing(CS) algorithms, off-grid CS algorithms and grid densification,Tensor tools, estimation in clustered channels, and resolutionof sensing ambiguity. Most of these techniques have highercomplexity than classical channel estimation algorithms. Sincethe required sensing rate is typically at the order of mil-liseconds to seconds, such high computational complexity isaffordable at BSs. Comparison of some of these techniquesfor sensing parameter estimation in PMNs is summarized inTable X. Details of the research are elaborated as follows.

1) Periodogram such as 2D DFT: The classical 2D DFTmethod is a periodogram method being widely used in radar.It can be used to coarsely estimate sensing parameters by

combining two of the following three transformations: con-verting the time-domain samples to frequency domain, spatial-domain samples to angle domain, and phase shifting samplesto Doppler frequency domain. A 3D DFT may also be used.But due to the complexity, it is generally replaced by two orthree 2D DFTs. The resolution of this method is low becauseof the long tail of the inherent sinc function in the DFT.A windowing operation can be applied to slightly improvethe resolution. This method typically requires a full set ofcontinuous measurements in time or frequency domain, whichcan limit its application in PMNs due to the discontinuoussamples.

2) Subspace Based Spectrum Analysis Techniques: Clas-sical subspace based spectrum analysis techniques such asMUSIC and ESPRIT can estimate parameters of continuousvalues with high resolution [118]. However, their applica-tions in PMNs may also be limited as they typically requiresamples of equal intervals. Techniques that can deal withnon-uniform sampling have been proposed, e.g., the coupledcanonical polyadic decomposition approach in [119] and thegeneralized array manifold separation approach in [120], butthey have very high computational complexity. To achievehigh resolutions, MUSIC and ESPRIT typically also requirea large number of samples so that the signal subspace andnoise subspace can be well separated. This may not always beavailable in some domains, such as the spatial domain, whichwould require a large number of antennas. However, it maybe a good option to combine them with other techniques forsensing parameter estimation, by exploiting their capabilitiesof high resolution and estimating parameters of continuousvalues.

3) On-Grid Compressive Sensing Algorithms: CompressiveSensing (CS) techniques [121] have been widely used incommunication systems for channel estimation [122]–[126]and in radar systems [127]. CS techniques formulate parameterestimation as a sparse signal recovery problem, which can besolved by many algorithms such as l1 recovery (convex relax-ation), greedy algorithms and probabilistic inference [121]. Atthe least, only twice the number of samples are required toaccurately recover a certain number of unknown parameters,in the noise free case. Typical CS techniques use on-gridquantized dictionaries, and hence errors are caused due toquantization when the original parameters have continuousvalues. One main advantage of CS for sensing parameterestimation in PMNs is that it does not require consecutivesamples. Actually, higher randomness of samples in time,frequency and spatial domains can generally lead to betterestimation performance.

We now demonstrate how a CS problem can be formulatedfrom the received signals, referring to (3) and (5). Let Nu

and S be the number and index set of available subcarriersfor sensing, respectively. For downlink sensing, Nu = N . Weassume that N � L and N is large enough such that thequantization error of τ` is small and the delay estimation canbe well approximated as an on-grid estimation problem. Letthe delay term e−j2πnτ` f0 be quantized to e−j2πn`/(gN ), where g

is a small integer and its value depends on the method used forestimating τ` . The minimal delay quantization is then 1/(gB).

22

TABLE X: Comparison of Sensing Parameter Estimation Algorithms

Algorithms Properties Suitability and main limitationPeriodogram suchas 2D DFT

Simple, but low resolution. May be used as thestarting point for other algorithms.

Generally, requires a full set of continuous samples inall domains, which may not always be satisfied.

Subspace methodssuch as ESPRITand MUSIC

High resolution and can do off-grid estima-tion. High complexity. High dimension Tensorbased ESPRIT and MUSIC algorithms withreduced complexity are also available.

Typically require a large segment of consecutive sam-ples, which may not always be satisfied.

Compressive sens-ing (On-grid)

Flexible. Does not require consecutive sam-ples. Various recovery algorithms that can beselected to adapt to complexity and perfor-mance requirements.

Works well even for estimating a small amount of off-grid parameters. Performance can degrade significantlywith many paths of continuous parameter values.

CompressiveSensing (Off-grid)such as atomicnorm minimization

Flexible and do not require consecutive sam-ples. Capable of estimating off-grid values.

Limitation in real time operation due to very highcomplexity. Still require sufficient separation betweenparameter values.

Tensor based algo-rithms

High-order formulation using the Tensor toolssuch as 3D Tensor CS simplified computationalcomplexity and provides capability in resolvingmultipath with repeated parameter values.

Tensor tools need to be combined with other algo-rithms such as ESPRIT and CS. Thus they face theinherent problems of these algorithms.

Let K , M and MT denote the total number of users/RRUs, thenumber of antennas for sensing, and the number of antennas ineach user/RRU for transmitting, respectively, for either uplinkor downlink sensing. We can now convert the multipath signalmodels in (5) and (3) to a generalized on-grid (delay only)sparse model, by representing it using Np � L, Np ≤ gNmultipath signals where only L signals are non-zeros:

yn,t = A(M,φ)CnDtBUT︸ ︷︷ ︸Hn

xn,t + zn,t, (11)

with

A(M,φ) = (A1(M,φ1), · · · ,AQ(M,φQ)),xn,t = (x1,n,t, · · · , xQ,n,t )T ,U = diag{A1(M, θ1),A2(M, θ2), · · · ,AQ(M, θQ)},

Here, Cn = diag{e−j2πn/(gN ), · · · , e−j2πnNp/(gN )}; B is a Np ×L rectangular permutation matrix that maps the signals froma user/RRU to its multipath signal, and has only one non-zero element of value 1 in each row; U is an MT K × L blockdiagonal radiation pattern matrix for MT arrays; xn,t is theMT K × 1 symbol vector; the `-th column in Aq(M,φq) (orAq(M, θq)) is a(M, φq,`) (or a(M, θq,`)); and Dt is a diagonalmatrix with the `-th diagonal element being b`e j2πt fD,`Ts . Notethat the columns in A(M,φ) of size M × Np and the diagonalelements in Dt of size Np × Np are ordered and tied to themultipath delay values.

Based on (11), the signal samples from all available subcar-riers and OFDM blocks can then be combined to formulate CSestimation problems. The sensing parameters to be estimatedin PMNs include delay, AoA and Doppler in three differentdomains. Sometimes, the AoD and magnitude of path are alsoof interest, which are not considered here. Since the signalsare relatively independent in the three domains, they can beformulated in a high-dimension (3D here) vector Kronecker

product form or even Tensor form. Therefore, we can apply 1Dto 3D CS techniques to estimate these sensing parameters. Thefollowing two problems need to be considered when selectingCS techniques of different dimensions.

• Quantization error and number of available samples: Al-though high-dimensional on-grid CS algorithms such asthe Kronecker CS [128] could offer better performance,they require more samples than unknown variables ineach dimension. In a typical BS, we can get sufficientnumber of observations for the delay (linked to subcar-riers), a reasonable number of samples in the Dopplerfrequency domain (linked to intermittent packets overa segment of channel coherent period), and a limitednumber of AoA observations (linked to antennas).

• Complexity: Exploiting the Kronecker CS property, thecomputational complexity is in the order of the productof the complexity in each domain, which is typicallyproportional to the cube of the number of samples.

Therefore, a high dimensional CS algorithm is not alwaysthe viable option, particularly for the Doppler frequencyand AoA estimation due to the limited number of sam-ples. Comparatively, mobile signals generally have tens tothousands of subcarriers, which provide numerous samplesfor delay estimation. Thus, we can formulate two multi-measurement vector (MMV) CS problems, by stacking spatial-domain and Doppler-frequency domain signals, respectivelywith frequency domain signals. From the MMV-CS amplitudeestimates, we can then estimate the AoA and Doppler frequen-cies [12], [43]. The details of CS algorithms from 1D to 3Dand their performance are presented in [43]. One commonproblem associated with using lower dimension CS is thatparameters with overlapped values in one or more dimensionscannot be separately estimated. In this case, techniques suchas the one proposed in [129] can be used, by taking advantageof the capability of model-based algorithms, for example,

23

modified matrix enhancement and matrix pencil.For multiuser-MIMO signals, for example, signals received

at an RRU from multiple RRUs in downlink passive sensing,we can use two methods to formulate the CS problems [12].The first, direct sensing method, directly uses the receivedsignals as inputs to CS sensing algorithms. Since the receiverknows the transmitted information data symbols, the problemcan be formulated as a block CS model [12], [125], [130],without decorrelating signals from multiusers. Correlationbetween the parameters can also be exploited in this model, viaintroducing intra-block correlation coefficients. The second,indirect sensing, is based on signal stripping that decorrelatessignals between users [12], [41]. Then the sensing parameterscan be estimated for each individual user by conventional CSalgorithms. Direct sensing can achieve better performance thanindirect sensing, as the decorrelation process introduces noiseenhancement, at the cost of higher complexity. If the datasymbols are unknown, e.g., in uplink sensing, decorrelatingand demodulation errors also exist. Such errors may be explic-itly considered and removed in the estimation [131]. In [131],a passive sensing algorithm for multiple objects is proposedby using demodulated signals. The delay-Doppler values areestimated by exploiting the sparsity of the demodulation errorsand numbers of objects. The positions and velocities of objectsare then estimated based on the estimated delay-Doppler, usingneural network techniques.

Overall, on-grid CS algorithms are promising for sensingparameter estimation in PMNs. However, the quantizationerror is a major problem as true sensing parameters havecontinuous values. For parameters of continuous values, thereexist mismatch between the assumed and actual dictionaries,generally known as “dictionary mismatch”, which can causesignificant performance degradation [132]. The degradationis severer when the number of unknown variables is larger.Therefore, resolving the quantization error and dictionarymismatch is a major challenge here.

4) Grid Densification and Off-Grid CS Algorithms: Thereare mainly two types of techniques that have been developed totackle the quantization error problem in CS: grid densificationand off-grid CS algorithms [133], [134]. Both techniques havehigher complexity than conventional on-grid CS algorithms.

Grid densification uses denser dictionaries to reduce quan-tization error. The discretization of the physical space isunavoidable since CS has been focused on the signals thatcan be represented under a finite dictionary by reconstruction.It is intuitively reasonable that both dictionary mismatch andparameter estimation error can be reduced with a dense grid.Therefore, the question comes whether a denser grid leads tomore accurate sparse signal recovery or not. In fact, accordingto the CS theory, the sampled grids should not be too dense.As in densely sampled grids, the dictionaries have a high inter-column correlation. The high correlation of dictionary itemsviolates the restricted isometry property (RIP) condition of CS[122]. This is particularly of concern when the SNR is not veryhigh. Therefore, there is a trade-off in dictionary mismatchand estimation accuracy while constructing a densified dic-tionary. Dynamic dictionaries with multi-resolution capabilityare proposed to resolve this problem. For example, in [135], a

dynamic dictionary based re-focused DOA estimation methodis developed with the number of extremely sparse grids refinedto the number of detected sources.

There are extensive research interests in extending CS to off-grid models, via, e.g., the perturbation method [136], CS plusmaximal likelihood [137], and the atomic norm minimization(ANM) method [79], [133], [138]. The ANM method [133],[138] can handle continuous dictionary and recover unknownvariables with a reasonable number of samples at a highprobability via a semidefinite program. It has been widelyapplied for channel estimation in, e.g., generalized spatialmodulation systems [79], MIMO radar via MMV models[139], and mmWave MIMO systems with planar arrays [140].However, the ANM method still requires that the variablessuch as delays have well separated values. This may notalways be satisfied in PMNs as an object may not always beapproximated as a point reflector/scatter and reflected/scatteredsignals may come in clusters due to the limited distance amongthe transmitter, the object and the receiver. Enhancing theANM method and making it capable of handling such signalsare important for its practical application in PMNs.

5) Sensing with Clustered Multipath Channels: In clustersparsity patterns, non-zero taps of sparse signal appear in clus-ters rather than being arbitrarily spread over the vector, whichmeans that sparse signal exhibits a structure in the form of non-zero coefficients occurring in clusters. In practice, multipathsignals in mobile systems often arrive in clusters [141], andpaths from one cluster typically come from the same scatter(s)and have similar parameter values. The situation becomescomplex once the clusters originated in a propagation scenehave correlation among other clusters of the same user andacross different users. Eventually getting sensing parametersfrom delay or spatial domain without acknowledging thechannel cluster structure can create accuracy problems.

We can find several research results on reconstructing clus-ter sparse signals in general, for example, through periodiccompressive support [142], model based CS [143], variationalBayes approach [144], and block Bayesian method [145]. Theexploitation of the cluster property in multipath channels forsensing parameter estimation in PMNs is possible throughcreating a prior probability distribution. In particular, a clusterprior probability density function needs to be introduced inthe CS reconstruction algorithm in order to efficiently detectthe coarse locations of the clusters, leading to more accuratesparse reconstruction performance when CS algorithms areapplied. Detailed technology on how cluster sparsity canbe exploited in JCAS systems such as PMNs that involveOFDMA and multi-user MIMO is yet to be developed.

F. Resolution of Sensing Ambiguity

As discussed in Section IV, there is typically no clock-levelsynchronization between a sensing receiver and the transmitterin PMNs, particularly in uplink sensing. In this case, thereexist both timing and carrier frequency offsets in the receivedsignals. The timing offset τo, as shown in (2), is typicallytime-varying, i.e., it has a random value which can changeduring any two discontinuous transmission. The CFO may

24

slowly vary over time due to oscillator stability. Unlike thecase in communications, where timing offset and CFO canbe absorbed into channel estimation, in sensing they causemeasurement ambiguity and accuracy degradation. Timingoffset can directly cause timing ambiguity and then rangingambiguity, and CFO can cause Doppler estimation ambiguityand then speed ambiguity. They also prevent aggregatingsignals from discontinuous packets for joint processing, as theycause unknown and different phase shifting across packets.Thus it is very important to resolve the clock timing offsetproblem. Should it be solved, uplink sensing can be efficientlyrealized requiring little changes of network infrastructure.

There have been a limited number of works that addressthis problem in passive sensing [146]–[148]. A cross-antennacross-correlation (CACC) method is applied to passive WiFi-sensing, to resolve the timing ambiguity issues. The basicassumption is that timing offsets across multiple antennas inthe receiver are the same, and hence they can be removed bycomputing the cross-correlation between signals from multiplereceiving antennas. Referring to (5) but considering a simpli-fied case with a single UE and a single transmitting antenna,the received signal at the m-th antenna can be rewritten as

yn,t,m =

L∑=1

b`e−j2πn(τ`+τo ) f0 e j2πt fD,`Ts e jmπ sin(φ` )xn,t + zn,t .

Let the m0-th antenna be the reference. Computing the cross-correlation between yn,t,m and yn,t,m0 yields

R(n, t) = yn,t,my∗n,t,m0

=

L∑`m=1

L∑`m0=1

b`m b∗`m0e−j2πn(τ`m−τ`m0

) f0

· e j2πt( fD,`m− fD,`m0)Ts e jmπ(sin(φ`m )−sin(φ`m0

)) |xn,t |2

+ zn,t,mz∗n,t,m0 . (12)

Note that in (12), the timing offset τo is removed. However,cross-correlation causes increased terms and unknown param-eters. The sensing parameters also become relative ones.

To proceed with the estimation of sensing parameters, it isassumed that line-of-sight (LOS) path exists and has has muchlarger magnitude than the non-LOS paths. The UE and BS areassumed to be static and the relative locations of the UE is alsoassumed to be known to the BS. In this case, terms multipliedwith the LOS path are much larger than the others in (12). Thecross-product between LOS paths contain static multipath onlyand is invariant over the channel coherent time. Thus it canbe removed by passing R(n, t) through a high pass filter. Thecross-terms between NLOS and LOS paths thus dominate inthe output of the filter. The sensing parameters can then beestimated, with respect to the known parameters of the LOSpath.

The CACC method has been successfully used in WiFi-sensing. In [148], CACC is used to obtain estimates for rangesand velocities of targets. In [147], CACC is adopted to getthe angle-of-arrival (AoA) spectrum, which represents theprobabilities of the direction or angle of target. However, theoutputs after CACC contain cross-product terms and actually

doubled the number of unknown parameters to be estimated.The authors in [147] proposed a method to suppress signalscontaining half of the unknown parameters, but the method isfound to be susceptible to the number and power distributionof static and dynamic signal propagation paths. Therefore,although the idea of CACC looks attractive in resolving thesensing ambiguity problem, more advanced techniques needto be developed to handle the output signals from CACC.

The required setup to enable CACC working is practicalin PMNs. For example, the fixed nodes that receive fixedbroadband service are ideal options. Those with LOS-path linkcan also be found, particularly when mmWave communicationis deployed. It is generally more challenging to extend thismethod to more complicated signal formats, and to effectivelyextract the sensing parameters from the output of CACC. InPMNs, the transmitted signals may also be optimized to enablebetter implementation of the CACC method.

G. Pattern Analysis

Using radio signals, high-level application-oriented object,behaviour and event recognition and classification can beachieved by combining machine learning and signal processingtechniques. They can be realized with or without using thesensing parameter estimation results, which provide locationand velocity information.

The feasibility and benefits of applying machine learn-ing technologies to communication systems have been welldemonstrated, for example, fast beamforming design via deeplearning [149], behavioral modeling and linearization of wide-band RF power amplifiers in 5G system [150], vehicularnetwork modeling in 6G by machine learning [151], routecomputation for software defined communication systems bydeep learning strategy [152], and heterogeneous network trafficcontrol by deep learning [153].

Although the work on pattern analysis using mobile signalsis still in its infancy stage, we have seen some interestingexamples, such as [27]–[29]. We can foresee its booming in thenear future, as we have been observing from many successfulWiFi sensing applications. Using WiFi signals for object andbehaviour recognition and classification has been well demon-strated [154]–[158]. Mobile signals are more complicatedthan WiFi signals, and the outdoor propagation environmentis also more challenging. However, the PMNs have moreadvanced infrastructure than WiFi systems, including largerantenna arrays, more powerful signal processing capability,and distributed and cooperative nodes. Using massive MIMO,a PMN BS equivalently possesses a massive number of“pixels” for sensing. It is able to resolve numerous objectsat a time and achieve imaging results with better field-of-viewand resolution, like optical cameras.

Based on the various approaches developed for WiFi sens-ing, we can deduce the procedures of applying pattern anal-ysis to mobile signals, as shown in Fig. 6. They typicallyinvolve four steps: signal collection, signal preprocessing,feature extraction, and recognition and classification. In thesignal collection step, the signals are collected at the receiveraccording to the desired rate. In the signal preprocessing step,

25

Signal Collection (Rate)

Signal Preprocessing(Stripping, Cleaning, 

Compression)

Feature Extraction (Supervised, non‐

supervised)

Recognition and 

Classification

Sensing parameter estimation

Fig. 6: Block diagram showing the procedure for patternrecognition.

the collected signals may be stripped, cleaned and compressed.Signal stripping removes the modulated symbols from thereceived signal, and hence the pure channel state information(CSI) is obtained. Multiuser signals may also be decorrelatedhere. Signal cleaning removes signal distortions associatedwith, e.g., timing, CFO and phase noise, and suppressesclutter signals. The purpose is to keep mostly informationcarrying signals. Many of the algorithms described beforecan be applied for this purpose. If signals arrive irregularlyin the first step, the CSI can also be interpolated here ifdesired. Signal compression makes the signal condense, sothat the useful information can be enhanced and the processingcomplexity in the following steps can be reduced. Commoncompression techniques include principal component analysis(PCA) and correlation [158]. Feature signals are then extractedfrom preprocessed signals, using machine learning techniquessuch as supervised and non-supervised deep learning. Finally,recognition and classification are conducted, with inputs fromthe extracted feature signals, the preprocessed signals, andestimated sensing parameters.

H. Networked Sensing under Cellular Topology

PMNs provide great opportunities for radio sensing undera cellular structure, which could be well beyond the scale andcomplexity of distributed radar systems. The main challengefor networked sensing under a cellular topology remains in theway to address competition and cooperation between differentnodes for sensing performance characterization and algorithmdevelopment. The research in this area is almost blank at themoment. Here, we envision two potential research directions.

1) Fundamental Theories and Performance Bounds for“Cellular Sensing Networks”: This is about investigating thepotentials of the cellular structure on improving the spec-tral efficiency and performance of sensing, and developingfundamental theories and performance bounds for such im-provement. Similar to communications, a cellular networkintuitively also increases frequency reuse factor and hencethe overall capacity for sensing. Stochastic geometry modelmay be an excellent tool for analyzing the dynamics in thesensing network, as have been applied to characterize theaggregated radar interference in an autonomous vehicular net-work in [159], [160]. Both intra-cell and inter-cell interferencewould then be taken into consideration in deriving the mutualinformation for networked sensing.

2) Distributed Sensing with Node Grouping and Coopera-tion: One way of exploiting networked sensing is to develop

distributed and cooperative sensing techniques by schedulingand grouping UEs and enable cooperation between RRUs. Onone hand, existing research has shown that distributed radartechniques can improve location resolution and moving targetdetection by providing large spatial diversity and wide angularobservation [161]. Such diversity can be maximized by opti-mizing both waveform design and placement of radar nodes. InPMNs, we can group multiple UEs sensing results to improveuplink sensing. On the other hand, distributed radar canenable high-resolution localisation, exploiting coherent phasedifference of carrier signals from different distributed nodes[89]. This requires phase synchronisation among radar nodes,and can only be potentially achieved in downlink sensing bygrouping RRUs. For both cases, we may develop distributedsensing techniques, leveraging on extensive research works ondistributed beamforming and cooperative communications.

I. Sensing-Assisted Secure Communication

When communication and sensing are integrated, it isimportant to understand how they can mutually benefit fromeach other. Existing research has investigated how to use thechannel structure obtained in sensing to improve the reliabilityof communications [162], [163]. Such detailed information onchannel composition may play a more important role in securewireless communication, with the application of physical layersecurity techniques. Current physical layer security studies aremainly based on channel state information. Comparatively, thesensing results contain more essential information about theenvironment between a pair of transmitter and receiver. Theycan motivate more informative secret-key generating methodsand agreement in cellular communication networks. As a start,we can characterize the secrecy capacity of PMNs, and developpractical code design methods for information encryption.

VII. CONCLUSION

We have provided a comprehensive review on the perceptivemobile network (PMN), which integrates radio sensing intocurrent communication-only mobile network, using the jointcommunication and radio/radar sensing (JCAS) techniques.Referring to 5G NR standard, we have illustrated that uplinkand downlink sensing can be realized with different degreesof modifications and enhancement to current mobile networkinfrastructure. We have provided a detailed review for majorresearch challenges, potential solutions and diverse researchopportunities within the context of PMN.

The PMN is expected to deliver a revolutionary ubiquitousradio sensing network that can significantly drive smart ini-tiatives such as smart cities and smart transportation, inte-grated with enriched mobile communication. In relation to the(stereo) optical vision in camera sensing, the PMN is expectedto realise 3D+ radio vision, including 3D location + speed +features for objects surrounding the radio transceivers, withadditional attractive features such as day-and-night availability,fog/leaf-penetration, and continuous tracking. It will enablemany new applications for which current sensing solutions areimpractical or too costly. While there are significant challengesand a long way ahead to make the PMN fully operational, our

26

survey here is a solid presentation, indicating the feasibilityand providing the potential directions to pursue.

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