MULTIPOS D4.8 Version 1.0 Mid-project report on novel...

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MULTIPOS

D4.8 Version 1.0

Mid-project report on novel algorithms and software implementation for

Signals of Opportunity positioning phase

Contractual Date of Delivery: 23 (months)

Actual Date of Delivery: 23

Editor:

Author(s): Pedro Manuel Figueiredo e Silva

Participant(s): TUT

Work package: WP 4.2 – Signals of Opportunity

Version: 1.0

Total number of pages:

Abstract: This document presents the main results obtained over the first year of the project, which

support the usage of signals of opportunity for cognitive positioning systems. The first part

introduces the reader to the several signals available and discusses several techniques for

identification of signals in the spectrum. Afterwards, the identification of OFDM and CDMA

signals is studied, with a method being proposed to distinguish both signals and reduce the

complexity of cyclostationary methods

Disclaimer:

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Document Control Version Details of Change Review Owner Approved Date

1.0 Initial version

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Executive Summary This document introduces and describes the main research steps taken during the first year of the project.

The document starts with an overview of the spectrum contents, illustrating the many available signals of

opportunity. Further on, simulation data is used to show that several signals can be distinguished from

each other in the spectrum, while real data is used to show the possibility of using undersampled data for

cyclostationary methods.

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Authors

Partner Name Phone / Fax / e-mail

TUT Pedro Manuel Phone: +358 40 7573348 / +351 91 6774348

Figueiredo e Silva e-mail: [email protected]

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Table of Contents

1. Introduction ......................................................................................................................................... 9

2. Signals of opportunity ....................................................................................................................... 11

2.1 Understanding the radio spectrum ............................................................................................. 11 2.1.1 Spectrum Sensing .............................................................................................................. 12 2.1.2 Cyclostationary ................................................................................................................. 12 2.1.3 Detection of OFDM and CDMA ....................................................................................... 13

2.2 Measurements for position estimation ....................................................................................... 13 2.2.1 Time of arrival .................................................................................................................. 13 2.2.2 Time difference of arrival ................................................................................................. 14 2.2.3 Round Trip Time ............................................................................................................... 14 2.2.4 Angle of arrival ................................................................................................................. 14 2.2.5 Receiver signal strength .................................................................................................... 14

2.3 Positioning algorithms ............................................................................................................... 14 2.4 Hybridisation ............................................................................................................................. 15

2.4.1 Data fusion model ............................................................................................................. 15 2.4.2 Handling measurements from different sensors ................................................................ 15 2.4.3 Application in the positioning field ................................................................................... 15

3. Contributions towards future receiver design ................................................................................ 17

3.1 Distinguishing signals in a mixture ........................................................................................... 17 3.1.1 Signal modelling ............................................................................................................... 17 3.1.2 Cyclostationary features .................................................................................................... 19 3.1.3 Cyclic frequency detector ................................................................................................. 21

4. Undersampling signals for cyclostationary analysis ....................................................................... 25

4.1.1 Measurement of IEEE 802.11g OFDM signals ................................................................. 25 4.1.2 Effect of downsampling in cyclic properties ..................................................................... 25 4.1.3 Statistical behaviour .......................................................................................................... 25 4.1.4 Most frequent cyclic frequencies ...................................................................................... 26

5. Collaboration with other project members ..................................................................................... 29

6. Conclusions ........................................................................................................................................ 31

7. References .......................................................................................................................................... 33

Appendix A – Parameters ........................................................................................................................ 41

Appendix B – MATLAB code ................................................................................................................. 43

Appendix C – Published papers .............................................................................................................. 45

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List of Acronyms and Abbreviations

Term Description

AOA Angle of arrival

CDMA Code division multiple access

CPS Cognitive positioning system

CR Cognitive radio

DVB-T Digital video broadcasting - terrestrial

FAM Fast Fourier transform accumulation method

GNSS Global navigation satellite system

GPS Global positioning service

GSM Global mobile system

HMM Hidden Markov methods

LTE Long term evolution

OFDM Orthogonal frequency-division multiplexing

PAM Pulse amplitude modulated signal

SCF Spectral correlation function

SNR Signal to noise ratio

SoO Signals of opportunity

SSCA Strip spectrum correlation algorithm

SVM Support vector machines

RSS Received signal strength

RTT Round trip time

TOA Time of arrival

TDOA Time difference of arrival

UMTS Universal mobile telecommunications system

UWB Ultra wideband

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1. Introduction

Over the last years, smartphones have become quite ubiquitous in most people’s life, especially in

developed countries [1]. It has been reported that sales of these devices have outgrew the sales of feature

phones, [2]. A big impact of these devices is that, most, if not all of them, have navigation capabilities.

Therefore, most smartphones have a GNSS chip, either for global positioning service (GPS) [3] or for

GLONASS [4]. It is probable that other systems will also be incorporated, as they become fully available,

such as the European GNSS, Galileo [5], and the Chinese GNSS, Beidou-2 [6].

Therefore, with the popularisation of smartphones, location based services have exploded in the recent

years in the most common smartphones operating systems in use, Android, iOS and Windows Phone [7]–

[9]. These services have led to a bigger demand of continuous positioning of the devices, which, from a

hardware point of view, puts a strain on the power usage. However, the environments where the mobile

users are expecting to have a position available are a major issue, since most of the times they are

navigating through urban canyons or indoor environments.

Indoor positioning is a big challenge that is drawing attention from the research and business community,

[10]–[12], as the demand for such services increases and is evaluated at 12b dollars in 2014, [13], [14].

Nevertheless, indoor positioning is already being achieved with good accuracy levels by some

commercial services, such as those provided by, Skyhook [15], Quuppa [16], BAE Systems [17], among

others. Still, the main interest, especially for mobile devices, is to avoid the integration of yet another

sensor.

For this reason, efforts have been made to develop solutions that rely on the existing infrastructures to

offer ubiquitous and seamless positioning to mobile users. This leads to the concept of signals of

opportunity (SoO), which is defined as any wireless signal that was not initially meant for positioning, but

can be exploited for that end. A few examples of these signals are IEEE 802.11 signals, commonly known

as Wi-Fi, Bluetooth, digital video broadcasting - terrestrial (DVB-T) and many of the cellular signals,

such as long term evolution (LTE), universal mobile telecommunications system (UMTS) and global

mobile system (GSM).

Over the next sections, SoO are further discussed along with known techniques to identify and detect

them in the spectrum. Besides that, this report aims to provide insight in the research done for work

package 4.2 of the MULTI-POS project1. The goal of the research is to provide at the physical layers

mechanisms to detect the presence of multiple signals in a baseband representation, without relying on the

traditional acquisition, tracking and demodulation to infer the presence of a signal. The goal is then to rely

on a signal identification block which can provide a fast understanding of the signals in the spectrum

surrounding a given device.

1 Some of the work in this document has been repurposed from [105], [107], [108]

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2. Signals of opportunity

Currently, Wi-Fi signals are the most ubiquitous signals after the cellular ones, however the later one is

available at a smaller scale, allowing for a better accuracy and precision. The good thing about this

approach is that from a hardware point of view, the required interfaces are already built and deployed in

most devices, so it does not mean an added complexity level during the design phase. However, it means

that the controlling software needs to handle fusion and data acquisition in an efficient way, otherwise the

power consumption will increase and affect the user experience. In Table 1, some of the SoO are

presented along with some of their main and novel characteristics. This information might be important

when further considering mechanisms to sense or exploit these signals, for example, a code division

multiple access (CDMA) signal is usually noise resistant, but requires a precise synchronised network,

while orthogonal frequency-division multiplexing (OFDM) does not require such a synchronised

network, but is less tolerant to noisy environments.

Table 1 – Several signals that might be considered as SoO.

Signal Frequency Multiple Access Bandwith

802.11ad 60 GHz OFDM 160 MHz

802.11ac 5.8 GHz OFDM 160 MHz

802.11n 5 GHz OFDM 40 MHz

802.11g 2.4 GHz OFDM 20 MHz

802.11a 5 GHz OFDM 20 MHz

802.11b 2.4 GHz DSSS 20 MHz

Bluetooth 2.4 GHz FHSS 1 MHz

Zigbee 2.4 GHz DSSS 5 MHz

DVB-T 40-200MHz OFDM 8 MHz

LTE 800, 900, 1800, 2600 MHz OFDM 20 MHz

UMTS 900, 2100 MHz CDMA 1.25 MHz

GMS 900, 1800 MHz TDMA 200 kHz

2.1 Understanding the radio spectrum

The idea of using SoO for positioning purposes has led the research community towards cognitive

positioning systems (CPSs). These systems are capable of understanding the radio frequency spectrum

and identifying the signals present in it. In a way, this is similar to what cognitive radios (CRs) are

proposing for the communication protocols, however the main difference is that in CR, the idea is to

optimise the spectrum efficiency. This is done by allowing radio devices to operate in a band where they

do not hold a license to operate in, as long as their operation does not interfere or degrade significantly

the operation of the service intended for that frequency band [18]. Due to their similarity, cognitive

positioning systems build on existing techniques for signal identification and detection. However, as

mentioned, the focus is now on actually determining what kind of signals are present in the spectrum.

This information can be of use to the positioning algorithms, as well as to the hardware controller, who

might decide which signals are worthy to be acquired and tracked in order to fulfil the necessary

requirements. Figure 1 depicts a possible architecture for a cognitive positioning system (bottom) along

with a traditional architecture for comparison. In this figure after all the analogue interfaces, a signal

identification block is present, which will allow for the determination of the spectrum contents. This

information is then carried over to the cognitive positioning engine, which is the entity responsible for

controlling and using this information, either for controlling the radio blocks, such as tracking and

acquisition, or other high level blocks, such as positioning or feature request to the emitter

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Figure 1 - CPS architecture versus traditional receiver architecture.

2.1.1 Spectrum Sensing

Spectrum sensing techniques are an important and useful background for cognitive positioning systems.

This field has received the attention of the research community for several years and the works done by

[19]–[21]serve as a good introduction, in the author's opinion, to this field. [19] discusses the main design

challenges and requirements of a spectrum sensing system. The work done in [20] points out all the

relevant radio spectrum and transmission opportunities, continuing further with an overview, comparison

and discussion of the most common spectrum sensing algorithms in use. Some of the algorithms

presented in [20] are further explained and presented along with other state of the art detectors in [21].

The work done in [21] is particularly recent and provides an extensive overview on the most known

spectrum sensing algorithms, such as:

Energy detector

Cyclostationary-based detectors

Higher order moment detectors

Filterbank-based detectors

Multitaper detectors

From the start, [21] describes why energy detection falls short for detection in noisy environments, where

the signal to noise ratio (SNR) of the signal of interest is too low to be recognised by the energy detector

[22]–[24]. The major drawback of the energy detector is that most of the times, the noise variance is not

known, leading to an incorrect threshold level, resulting in a false detection. This is mostly known in the

field as SNR walls, which are the SNR limit to which the detector can provide accurate detection. Also,

this is something that every detector suffers from, not only the energy detector. As mentioned in [21], the

energy detector's SNR wall will be higher than some detectors that exploit known periodicities in the

signals. Nevertheless, there are several approaches, mentioned in [25], to reduce the SNR walls for signal

detectors. Another approach to signal detection, which usually involves an increase in complexity, is the

exploitation of second order statistics. Communication signals are possible to be identified with this

statistics since they are considered coloured signals, containing a non-flat power spectrum density.

Indeed, this is caused by particular characteristics of the signals at hand and can be exploited in the

detection stage to achieve better detection performance and even circumvent the issue of the SNR wall. It

does not mean the detection will be possible at any SNR value, but only that the noise’s statistics does not

need to be properly known. In [21] the second order statistics methods are introduced using an OFDM

signal, a popular modulation method in current wireless systems. A good introduction to second order

statistics methods is also provided in [26],where it is shown how features for a simple pulse amplitude

modulated signal appear due to a simple quadratic operation. These two works pave the way to

cyclostationary-based methods, which will be discussed later in more detail.

In [21], the discussion continues with more specific detectors, that rely on particular structures of the

sample covariance matrix, blind detection, filterbank-based detectors and enters in other challenges and

design questions in the field, such as cooperative spectrum sensing. Regarding cooperative spectrum

sensing, where several sensors in different locations communicate between each other, the work done in

[27] points out the main challenges, advantages and overhead when considering such situations.

2.1.2 Cyclostationary

Cyclostationary-based methods are one of the most popular methods in the field of signal detection and

classification. This is mostly due to the fact that the increase in complexity offers a good trade-off

between detection accuracy and the decrease of the SNR wall, allowing for detection in noisy

environments. In both works presented in [26] and [28] it is possible to get a better understanding of

cyclostationary theory and how it can be used to obtain useful features for detection purposes. The work

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in [28] is quite extensive, covering most of the applications of the cyclostationary, not only in the field of

telecommunications, but also in other fields such as econometrics and biology. Regardless of that, the

cyclostationary methods are usually divided in frequency smoothing and time smoothing algorithms.

However, the most efficient algorithms are the fast Fourier transform accumulation method (FAM) and

strip spectrum correlation algorithm (SSCA), which belong to the time smoothing domain. While these

two detectors are covered in [26], the work done in [29] describes booth of them with the necessary detail

for a correct and efficient implementation. An implementation of these methods was made available in

[30] and a similar implementation is also available in MATLAB [31]. It is also of interest to point out the

work done in [32], where frequency and time smoothing algorithms are merged together in order to offer

an hybrid method, which was shown to outperform the FAM and SSCA.

2.1.3 Detection of OFDM and CDMA

The goal of these methods is to detect and classify the incoming signal. Two interesting signals to be

detected are the ones which use CDMA and OFDM modulations. These are two of the most common

modulations in use by communications systems. CDMA is heavily used by space communication systems

and is also part of older protocols of the IEEE 802.11 standard. As for OFDM, it is present in LTE and

most IEEE 802.11 signals. The cyclic properties of these signals are present in the literature, such as [33]

for OFDM and [34] for CDMA. The actual detection and classification of the signals can then be done

using support vector machines, hidden Markov methods, [35]–[39] or using other methods [40]–[42]

Most of the times these methods are applied to OFDM signals, but the same could be achieved for CDMA

signals. In [43]–[45] CDMA signals are detected using cyclostationary methods.

2.2 Measurements for position estimation

In positioning systems, the position of its users is provided through the algorithms applied to

measurements taken from the received signals. The basic blocks of such a system are illustrated in Figure

2. The first block is responsible for providing to the positioning algorithm measurements regarding the

received signals. There are several measurements that can be used, which can be more or less suitable to

the type of signal present at its input. Regardless of that, time of arrival (TOA), time difference of arrival

(TDOA), angle of arrival (AOA) and received signal strength are the most common. For example, global

navigation satellite systems rely on TOA to compute the distance of the mobile to each satellite in view.

In indoor systems, receiver signal strength (RSS) is used frequently to provide an estimate of the user

position [15], [46].

The following sections provide an overview of these four measurements, TOA, TDOA, AOA and RSS.

Figure 2 - Basic elements of a positioning system.

2.2.1 Time of arrival

TOA provides to the system the time taken by the signal to travel from the emitter to the receiver. The

distance to the emitter, R,

𝑅 = 𝑡𝑣

is the time it takes for the message to arrive times the speed of the medium is known. In satellite systems

this medium's speed is the speed of light. Therefore, synchronisation is necessary for an accurate

computation of the distance, otherwise the error will be multiplied by the speed of light, resulting in

several hundred meters of error. Mitigation of multipath is particularly important for urban jungles and

indoor scenarios [47], [48].

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2.2.2 Time difference of arrival

TDOA measures the difference in TOA, 𝑡1 − 𝑡2, from the emitter to two receivers,

𝑅

𝑣1

− 𝑅

𝑣2

= 𝑡1 − 𝑡2,

where 𝑅 is the geometrical distance and 𝑣 the speed of each medium.

The idea of TDOA is to determine the relative position of the mobile transmitter by examining the

difference in time at which the signal arrives at multiple measuring units, rather than the absolute arrival

time[49], [50]. This technique usually involves a reference signal transmitted by the receiver, but the

exact transmission time is not required unlike TOA. The timing error may be cancelled or reduced in the

time difference. However, the emitters need to be synchronised among each other [47], [48], [51].

2.2.3 Round Trip Time

Round trip time (RTT) measures the time a message takes to travel from the source to the destination and

back. It does not require synchronisation in the network, but it increases the complexity of the overall

system, since it requires each node to transmit a message and wait for its return [46], [48]. The distance,

R,

𝑅 = (𝑡𝑅𝑇 − ∆𝑡)𝑣

2

is a function of the return trip time, 𝑡𝑅𝑇, minus the time taken by the device to process the request, ∆𝑡,

times the speed of the medium, divided by two.

2.2.4 Angle of arrival

AOA is the measurement of the signal direction, through the use of antenna arrays. This technique has

been studied for many years, especially for radar and sonar technologies under military applications [52],

[53]. The advantages of this technique are that it requires a minimum of two or three units for,

respectively, 2D or 3D positioning. Besides that, there is no requirement for time synchronisation among

these units. The major drawbacks are most of the times related to algorithm and hardware complexity.

Accurate positioning also requires methods to deal with shadowing and multipath, especially for indoor

environments. The measuring aperture also has a great impact on the system's performance [47], [48],

[51], [54]

2.2.5 Receiver signal strength

RSS is a metric of the input signal power at the receiver. Sometimes this metric is obtained by translating

a vendor specific indicator into the actual received signal strength. This causes some difficulties in

methods such as fingerprinting, since two devices from different brands can report significant differences

in the signal strength at a given position. RSS can also be related to a distance by the propagation

characteristics of the signals, however accurate modelling of all the channel's effects is not a trivial

challenge [48], [55]. A basic model, [56], that relates the RSS to the distance is given by,

RSS = 20 log10 f + N log10 d + L𝑓 (n) – 28

where N is the distance power loss coefficient, f the frequency (MHz), d the separation distance (m)

between the base station and portable terminal (where d > 1 m), 𝐿𝑓 the floor penetration loss factor (dB),

therefore a function of the number of floors between base station and portable terminal (n ≥ 1).

Fingerprinting avoids the problem of multipath and propagation modelling, by building radio maps to

known location in a training phase. The information is stored in a web server and in an online phase the

user obtains the best match to the radio map uploaded. While it is simple, small changes in the

environment can lead to significant changes in the spectrum [55], [57], [58]

2.3 Positioning algorithms

A location can be obtained through the measurements provided by the previous methods, however this is

a problem that requires consideration from the positioning algorithm designer. It should take into account

the needs of the users and knowledge of the system. Therefore, the models can be separated in either

deterministic, where the problem is described by linear or possible to linearise equations or probabilistic

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models [59], which use a maximum likelihood estimator to find the maximum point of the probability

distribution function of the system's parameters [60]–[62].

2.4 Hybridisation

Providing a seamless and ubiquitous position solution means that the system must be able to handle either

indoor or outdoor scenarios. As no current system is capable of providing such a solution, merging data

from disparate sources of information is currently the best alternative available [46]. The work in [46]

also provides an extensive overview of the existing positioning technologies for indoor environments.

Also, this process of merging data allows the enhancement of current systems, as an example, GNSS

solutions are commonly enhanced by inertial measurement units, [63]–[67]. Therefore, as more SoO are

considered it is important to understand how merging of such different sources can be achieved to reach

the desirable results.

2.4.1 Data fusion model

The process of merging data from multiple sources appears in several fields of engineering and one where

it has received considerable attention is in the military field [68]–[71]. Due to this reason, the related

terminology changes among fields and has caused several overlaps in research, as pointed out in [72]. In

an attempt to make the terminology more unified, the merging of data from several sensor sources has

been defined as data fusion. This effort, done in the late 80s, by the U.S. Joint Directors of Laboratories

(JDL) created a data fusion model and lexicon in order to establish a common ground for this field. Data

fusion was initially defined by JDL as “a process dealing with the association, correlation, and

combination of data and information from single and multiple sources to achieve refined position and

identity estimates, and complete and timely assessments of situations and threats, and their significance.

The process is characterized by continuous refinements of its estimates and assessments, and the

evaluation of the need for additional sources, or modification of the process itself, to achieve improved

results” [73]. However, this definition, rather restrictive in its terms, has been recently changed to a

broader and more concise definition, accommodating the wider range of applications that occur in several

fields of engineering. Hence, “data fusion is the process of combining data to refine state estimates and

predictions” is the new definition presented in the revised JDL model [74]. Other models are also pointed

out in [69].

2.4.2 Handling measurements from different sensors

If the data in use is commensurate, meaning both sensors are measuring the same physical phenomena,

then the raw data can be directly combined [74]. Techniques for raw data fusion typically involve classic

estimation methods, such as Kalman filtering. Otherwise, when the sensor data are non-commensurate,

the data must be fused at the feature/state vector or decision level. For the non-commensurate case,

feature level fusion involves the extraction of characteristic features that are relevant to the process under

analysis. These features can then serve as an input to pattern recognition techniques, such as neural

networks, clustering algorithms or template methods. Decision level fusion means that the sensor

information is combined after each sensor has made a preliminary determination of an entity’s location,

attributes, and identity. Examples of decision-level fusion methods include weighted decision methods,

classical inference, Bayesian inference and Dempster-Shafer’s method [72], [74]. Figure 3 shows the

three recommend architectures for data fusion from several sources or sensors.

2.4.3 Application in the positioning field

In the field of positioning, [63]–[67] show some applications and methods of applying multiple sensor

data with GNSS. Most of these studies focus on GNSS and inertial sensor units, while [64] also adds

another sensor, a video camera. To merge this information, feature extraction is necessary before the

fusion occurs. In [67] an RFID sensor is added, which can also be easily incorporated by computing a

range according to the channel mode along with. The study in [66] covers the usage of a particle filter for

handling the fusion of several sensors with the possibility of having different observations models to

handle, for example, sensor failures. An overview of several techniques for vehicle navigation is provided

in [75].

The works presented in [76]–[86] provide several examples for data fusion in indoor environments. The

work in [76] presents a TDOA/RSS hybrid algorithm for localisation using ultra wide band. In [79], an

IMU and RFID measurements are fused together with a tight Kalman filter where ranges are derived for

both cases for the estimation phase. The work in [77] proposes an hybrid system for Wi-Fi and RFID

measurements, discussing its potentials and limitations. In [78] inertial data is fused with Wi-Fi data for

indoor pedestrian navigation. In [80] is presented a joint estimation of range and angle measurements for

systems using ultra wide band signals. This work is done in a real scenario and considers both line-of-

sight and non-line-of-sight. In [81] a more specific application of data fusion is applied to smartphones,

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with the objective of enabling several location based services. The same is seen in [87], where an indoor

positioning system is evaluated in a supermarket scenario. The works in [84], [85] rely on Bluetooth

hotspots to enhance the performance of Wi-Fi positioning systems. Both works see Bluetooth as a

promising way of portioning indoor spaces, improving the overall performance.

Most of the works mentioned above consider only one scenario for the positioning or navigation

application, either indoor or outdoor. However, current research is focusing more into a full system that is

able to provide a ubiquitous solution in any scenario. Valuable contributions have been made by the

works in [88]–[91]. In [88] an architecture for fusing inertial, GNSS and Wi-Fi sensors is proposed and

some preliminary results are presented. A similar architecture is proposed in [89] where a barometer is

also present to improve the GNSS solution. The work in [91] discusses existing technologies for indoor

positioning from a services point of view and how to switch between technologies when the scenarios

change. This work, shows one of the trends in the hybridisation of data, since the objective now is to

understand which sensors to fuse and in which situations they should be merged. So far, most authors

have been focused in fusing a couple of technologies together to obtain a position solution but knowing

which ones to select and when to use is still lacking more research.

Figure 3 - Data fusion architectures proposed by JDL

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3. Contributions towards future receiver design

This section summarises the main results regarding design options for future cognitive positioning

systems. The following sub-sections have also appeared in the previous mid project report.

3.1 Distinguishing signals in a mixture

Since there are plenty of SoS available to use, one of the goals of this study was to prove that it is indeed

possible to distinguish, using cyclostationary-based methods, between CDMA and OFDM signals when

mixed together in baseband. This offers a fast way to understand the spectrum contents, without the need

to wait for a decision from the dedicated hardware.

The following section presents simulation results to distinguish between CDMA and OFDM signals.

These signals are considered since they are commonly used by communication systems and recent studies

show their applicability in the positioning field, through the usage of timing-based estimators [92]–[94].

IEEE 802.11 systems are a good example of such a system, since CDMA is present in the legacy signals,

802.11b, and OFDM is used in the most recent releases, 802.11ac/g/n. Hence, both signals are generated

taking into account some recommendations from the IEEE 802.11 standard available in [95].

3.1.1 Signal modelling

The simulation consists of a mixture of two signals plus noise, z(t), denoted as,

𝑧(𝑡) = 𝑥(𝑡) + 𝑦(𝑡) + 𝑛(𝑡), (1)

where 𝑥(𝑡) is a CDMA signal (set to zero if the CDMA signal is absent), 𝑦(𝑡) an OFDM signal (set to

zero if the OFDM signal is absent) and 𝑛(𝑡) is an AWGN of double-sided power spectral density equal to

2𝑁0.

Starting with the CDMA signal, 𝑥(𝑡), is given by,

𝑥(𝑡) = {√𝐸𝑏 ∑ ∑ 𝑐𝑘(𝑛)𝑝(𝑡 − 𝑘𝑇𝑐𝑛𝐽𝑇𝑐),

𝑆𝐹

𝑘=1

+∞

−∞

0, 𝑤ℎ𝑒𝑛 𝑠𝑖𝑔𝑛𝑎𝑙 𝑖𝑠 𝑎𝑏𝑠𝑒𝑛𝑡,

(2)

where 𝐸𝑏 is the bit energy, 𝑆𝐹 is the spreading factor, 𝑐𝑘(𝑛) is the chip value (+1 or -1) for 𝑘𝑡ℎ chip

during 𝑛𝑡ℎ symbol, 𝑝(𝑡), a pulse shaping function, which is taken as a rectangular pulse with amplitude

one and width equal to the chip interval, 𝑇𝑐. This simulation considers the presence of a IEEE 802.11b

signal, which uses the 𝑆𝐹 = 11 bit Barker code as a spreading sequence, [95].

Figure 4 - CDMA power spectrum.

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As for the OFDM signal, y(t), is described by,

𝑦(𝑡) = 𝐴√𝐸0 ∑ ∑ 𝑋𝑛(𝑘)𝑒𝑗2𝜋∆𝑓𝑡ℎ(𝑡 − 𝑛𝑇𝑈

𝑁

𝑘−1

)

𝑛

, (3)

where 𝐴 = (𝑁𝑇𝑈𝐸𝑜)−1

2 is a multiplicative constant normalizing the OFDM symbol energy, 𝐸0 represents

the average energy of OFDM symbols, N is the number of subcarriers, 𝐸0 is the average energy of 16-

QAM data symbols which form the OFDM symbols, 𝑋𝑛(𝑘) is 𝑛𝑡ℎ OFDM symbol expressed as a vector

consisting of N data symbols, and ℎ(𝑡), a pulse shaping function, taken as a unity rectangular pulse on the

interval [0, 𝑇𝑈) and zero otherwise. 𝑇𝑈 is defined as the useful symbol period. The signal is further

extended through the cyclic prefix. In the frequency domain, the signal occupies frequencies in the range [−𝐵, 𝐵] MHz, where B is the one-sided bandwidth equal to 10 MHz. As a remark, this study uses pilots

boosted by 3 dB, for an easier observation of cyclostationary features. Other approaches can be taken,

such as averaging over several symbols [96], [97].

Figure 5 - OFDM power spectrum.

Figure 6 - White gaussian noise power spectrum.

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3.1.2 Cyclostationary features

A signal 𝑥(𝑡) is wide-sense cyclostationary if its time varying autocorrelation function 𝑅(𝑡, 𝜏) defined as,

𝑅(𝑡, 𝜏) = 𝐸{𝑥(𝑡)𝑥∗(𝑡 + 𝜏)}, (4)

is periodic in time, 𝑡, for each lag parameter, 𝜏. Hence, it can be represented as a Fourier series,

𝑅(𝑡, 𝜏) = ∑ 𝑅𝛾

𝛾

(𝜏)𝑒𝑗2𝜋𝛾𝑡 , (5)

where the sum is taken over integer multiples of fundamental cyclic frequency 𝛾 for which cyclic

autocorrelation function is defined as,

𝑅𝛾 = lim𝑇→∞

1

𝑇∫ 𝑅(𝑡, 𝜏)𝑒−𝑗2𝜋𝛾𝑡

𝑇2

−𝑇2

𝑑𝑡, (6)

The SCF, 𝑆𝛾(𝑓), is the Fourier transform of 𝑅𝛾 and is given as,

𝑆𝛾(𝑓) = ∫ 𝑅𝛾

ℝ

𝑒−𝑗2𝜋𝛾𝜏𝑑𝜏 (7).

In this study the fast Fourier transform accumulation method (FAM) is used to estimate the SCF of the

signals. Further information on this method can be found in [28], [29], [98], along with other methods that

should provide comparable results. A representation of the SCF in the frequency and cyclic domain is

shown in Figure 7.

Figure 7 - 𝑺𝜸(𝒇) for 𝒇𝒔 = 𝟔𝟎 𝑴𝑯𝒛, ∆𝜶 = 𝟏𝒆−𝟎𝟓𝒇𝒔, ∆𝒇 = 𝟎. 𝟏𝒇𝒔.

Periodicities in the signal, such as the symbol rate, length of the spreading sequence, among others are

responsible for the appearance of spectral lines in the SCF domain. These spectral lines are referred to as

cyclic frequencies. If the signal was purely random, as it is the case with AWGN, the value of the SCF

would be zero for every cyclic frequency, except for cyclic frequency zero. A SCF for a mixture of

CDMA and OFDM signals with noise is shown in Figure 8, where 𝛼𝑚and 𝛽𝑚 are the cyclic frequencies

for each signal, respectively, and further defined in the following paragraphs. Also, the theoretical set of

cyclic frequencies for the used CDMA and OFDM signals is shown for reference.

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In the context of this study, the cyclic frequencies of interest are those specific to CDMA and OFDM

signals. Therefore, consider all the theoretical CDMA cyclic frequencies to be contained in, 𝒜,

𝒜 = {𝛼0, 𝛼1, 𝛼−1, . . . , 𝛼𝑚, 𝛼𝑚−1 }, 𝑚 ∈ ℕ, (8)

where each cyclic frequency 𝛼0 is dependent on both the chip rate, 𝑓𝑐, and the spreading factor, 𝑆𝐹, given

by [67],

𝒜 = {𝛼0, 𝛼1, 𝛼−1, . . . , 𝛼𝑛 , 𝛼𝑛−1 }, 𝑚 𝜖 ℕ, (9)

Regarding the OFDM signal, consider its cyclic frequencies, to be contained in, ℬ,

ℬ = {𝛽0, 𝛽1, 𝛽−1, . . . , 𝛽𝑛, 𝛽𝑛−1 }, 𝑛 𝜖 ℕ, (10)

where each 𝛽𝑛 location is related to the symbol period, 𝑇𝑠𝑦𝑚𝑏𝑜𝑙[68],

∀𝛽𝑛 ∶ 𝛽𝑛 ∈ {𝑘1

𝑇𝑠𝑦𝑚𝑏𝑜𝑙

} ∧ 𝑘 ∈ ℤ, (11)

and the symbol period is given by,

𝑇𝑠𝑦𝑚𝑏𝑜𝑙 = 𝑇𝐺𝐼 + 𝑇𝑈 , (12)

where, 𝑇𝐺𝐼 , is the duration of the guard interval which is occupied by the cyclic prefix, plus the duration

of the useful symbols, 𝑇𝑈 = ∆𝑓−1, which is chosen to guarantee orthogonality of the OFDM subcarriers

for their given frequency spacing ∆𝑓.

Both 𝒜 and ℬ can be computed if the signal specific parameters are known and are a necessary input for

the proposed detector. Regarding the CDMA signal, the chip rate and spreading factor are necessary. As

for the OFDM signal, the total number of carriers and bandwidth are necessary.

Figure 8 - SCF section at frequency zero 𝑺𝜸(𝟎).

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Figure 9 - OFDM SCF section at frequency zero 𝑺𝜸(𝟎).

Figure 10 - CDMA SCF section at frequency zero 𝐒𝛄(𝟎).

3.1.3 Cyclic frequency detector

In this study, a simple method, inspired by the works in [99], [100], was developed to detect the presence

of the several cyclic frequencies at given frequencies. The algorithm works by considering a window, W,

centred at a cyclic frequency, 𝛾𝑖, from either sets 𝒜 or ℬ, as illustrated in the left side of Figure 11. The

window's size is set to,

𝑊 = [𝛾𝑖 − 𝜀, 𝛾𝑖 + 𝜀], (13)

where 𝜀 is set to a value that avoids overlapping with other cyclic frequencies of the signals being tested.

If such condition is not met, the algorithm reports a false positive. Future work might address this issue or

at least provide some insight to the correct size of the window, according to the resolution in the cyclic

domain. This resolution will impact the possible number of samples to acquire from the vicinity of

𝛾𝑖 therefore, in this study the condition was met and W is defined over the range [𝛾𝑖 − 0.0305, 𝛾𝑖 +0.0305] Hz of the absolute value of the SCF. The range is defined over the previous and next 100

frequencies in the cyclic domain, whose resolution is presented in Annex A.

Using the samples taken from 𝑊, the algorithm takes the mean, µ, and standard deviation, 𝜎, values of

this region and computes an activity indicator, 𝐼, as,

𝐼 = 𝜎

µ, (14)

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for each window centred in the cyclic frequencies under test, as seen in the right side of Figure 11.

Therefore, if a cyclic frequency is present and is contained in W, the peak value in the SCF at that

location will significantly increase the value of the standard deviation and by consequence the value of 𝐼.

Furthermore, 𝐼 is compared to 𝑉𝑡ℎ, a threshold to flag the presence or absence of a cyclic frequency.

Actually, from Figure 12 it is possible to observe that the noise distribution of the window approximates a

𝜒-square distribution. This window is taken for -3 MHz, where a cyclic frequency would appear if a

signal was present, as seen in Figure 8. From this result it is possible to obtain a constant for the ratio of

the absolute standard deviation over the mean value. This is done in a similar manner to the normal

distribution, as seen in [101], [102].

Figure 11 - Algorithm's windowing (left) and detector's diagram block (right).

Figure 12- Noise distribution for W at a particular cyclic frequency.

Decision process The detection mechanism described is used to detect the presence of certain cyclic frequencies in the sets

𝒜 or ℬ which have different cardinality. This information is then used by a K out of M detector [103] to

infer if a certain signal is present or not. However, since some of the cyclic frequencies might overlap, as

it is the case in this work, it is necessary to do the detection in two stages. Therefore, under the

hypotheses,

ℋ1.1 = {𝐶𝐷𝑀𝐴 𝑎𝑛𝑑 𝑂𝐹𝐷𝑀 𝑤𝑖𝑡ℎ 𝐴𝑊𝐺𝑁,

𝑧(𝑡) = 𝑥(𝑡) + 𝑦(𝑡) + 𝑛(𝑡), (15)

ℋ1.2 = {𝐶𝐷𝑀𝐴 𝑤𝑖𝑡ℎ 𝐴𝑊𝐺𝑁,𝑧(𝑡) = 𝑥(𝑡) + 𝑛(𝑡),

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ℋ1.3 = {𝑂𝐹𝐷𝑀 𝑤𝑖𝑡ℎ 𝐴𝑊𝐺𝑁,𝑧(𝑡) = 𝑦(𝑡) + 𝑛(𝑡),

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ℋ0 = {𝐴𝑊𝐺𝑁 𝑜𝑛𝑙𝑦,

𝑧(𝑡) = 𝑛(𝑡), (18)

the decision of whether a signal is present or not is done by following the decision process depicted in

Figure 13. The algorithm starts by testing the presence of K cyclic frequencies of CDMA. If these are

present, then it continues by testing if M OFDM cyclic frequencies are also present. If cyclic frequencies

are detected in both stages, then both signals should be present in the spectrum. The identification works

as long as the sets under consideration follow those which contain the exclusive cyclic frequencies of the

signal, otherwise it is not possible to assume the spectrum contents. This would be the case if both

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𝒜 − ℬ ∩ 𝒜, (19)

and

ℬ − ℬ ∩ 𝒜, (20)

would be zero. However, since (20) is not an empty set, CDMA is said to be present if no other cyclic

frequencies are observed in those positions.

Figure 13 - Detection procedure to signal presence of CDMA, OFDM or both.

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4. Undersampling signals for cyclostationary analysis

One of the main disadvantages of cyclostationary methods is the complexity burden associated with them,

which is also dependent on the sampling rate used at the receiver. Most of the times, this sampling rate is

high, in order to observe more easily the appearance of certain spectral lines.

Using real measurement data, as described in the following sections, a study was conducted to infer if

cyclostationary properties were still preserved in undersampled signals, sampled at rates below the

Nyquist rate. Since these properties seem to be still visible, this result means a reduction of the

complexity through usage of lower sampling frequencies.

4.1.1 Measurement of IEEE 802.11g OFDM signals

Real IEEE 802.11g OFDM signals were captured using an inexpensive (below 1000€) acquisition

hardware platform. The platform is called Universal Software Radio Peripheral (USRP) B210. Its main

components include RF integrated circuit AD9361 consisting of two channel transceiver with integrated

12 bit DACs/ADCs and Spartan 6 FPGA. Moreover, it incorporates a programmable analog filter which

is automatically sets the appropriate bandwidth for the given sampling rate, minimising the effect of

antialiasing. No additional RF hardware was used besides a standard 3 dBi omnidirectional antenna

working in 2.4 GHz band. The received signals were captured at sampling rates,

𝑓𝑠 = {2𝑓𝑁 = 40, 𝑓𝑁 = 20,1

2𝑓𝑁 = 10} 𝑀𝐻𝑧, (21)

and stored in a computer for subsequent off-line processing. The parameters used for the offline

processing are available in Table 2 and annex A.

The signals were measured in corridors and offices in Tietotalo building at Tampere University of

Technology. Individual 802.11 frames captured in those signals do not keep a constant SNR value.

However, it was roughly estimated that the SNR varies in range from 3 to 16 dB, where SNR is defined

as the ratio of powers of useful signal and noise. Figure 14 illustrates the setup used for the

measurements.

Figure 14 – Experimental setup used during the experiment.

4.1.2 Effect of downsampling in cyclic properties

In order to investigate the possibility of using downsampled signals for cyclic features detection, the input

data was split in 10 ms segments. These segments were used in the cyclostationary algorithm to

investigate the behaviour and presence of the cyclic frequencies expected from ℬ. The length of this

segment was arbitrarily chosen in order to contain useful signal at each time. Since the measurements

were made in an office environment, the high density traffic should fulfil this assumption.

Figure 15 shows an example of the SCF of the 10 ms time windows, at different sampling frequencies, 40

MHz and 10 MHz, respectively. In both cases it can be seen that the downsampling keeps the cyclic

frequencies in the expected positions, but the values are slightly changed. This is particularly visible when

comparing the values at 1 MHz. It is also possible to observe in the figure that most cyclic frequencies,

expected from ℬ, are not visible or its value too low and considered as noise.

4.1.3 Statistical behaviour

Figure 16 through Figure 18 show a box plot containing the value of I, (14), for the segment defined over

the cyclic frequencies contained in the interval (-5, 5) MHz, for sampling frequencies 40 MHz, 20 MHz

and 10 MHz, respectively. These figures allow a better understanding of how the value of I is changing

throughout time for each cyclic frequency expected from ℬ. The boxplots show the median value (central

mark in the box) and mean of I at the given cyclic frequencies, as well as the 25th

and 75th

percentiles,

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defined by the box lower and upper edges, respectively. The minimum and maximum values are

represented by the vertical lines extended from the box and remaining dots mark the outliers, defined as

the observations with values above 3σ. The threshold used for the later probabilities of appearance is also

plotted for reference.

One main conclusion from these figures is that the cyclic frequencies at integer values are the ones

causing the biggest changes in the value of I. Otherwise, the mean of I remains close to 0.5 as it has also

been observed when only noise is present at the input. It is also interesting to note that almost all the

values of I, for 10 MHz sampling frequency, are clearly above the defined the threshold. While for the

other sampling frequencies this only happens at some cyclic frequencies, such as 3 MHz. Regardless of

that, the threshold can be tweaked which will have impact on the probability of identifying the cyclic

frequencies, however the proper way to set this value is outside the scope of this paper.

Table 2 – Parameters used to analyse the acquired data.

Parameter Value

Number of random trials, T 5000

Activity threshold, 𝑽𝒕𝒉 1

Observation time 10 ms

Cyclic resolution 1𝑒−4𝑓𝑠Hz

Frequency resolution 0.1𝑓𝑠Hz

Bandwidth (two-sided), 2B 20 MHz

Modulation 16-QAM

Number of carriers, N 64

Symbol interval, 𝑻𝒔𝒚𝒎𝒃𝒐𝒍 4 𝜇s

Sub-carrier spacing, ∆𝒇 B/N = 312.5 kHz

Guard interval, 𝑻𝒈𝒊 ∆𝑓−1/4 s

First cyclic frequency, 𝜷𝟏 250 kHz

Figure 15 - 𝑺𝜷(𝟎) for measured data sampled at 𝒇𝒔 = 𝟒𝟎 MHz and 𝒇𝒔 = 𝟏𝟎 MHz.

4.1.4 Most frequent cyclic frequencies

Supporting the discussion above, the probability of appearance at integer cyclic frequencies is obtained by

counting the number of times the cyclic frequency is present in the 5000 input data windows of 10 ms

duration. In Figure 19 the probability of appearance for all the expected cyclic frequencies from ℬ is

shown. This figure shows, as expected from the previous discussion, that the cyclic frequencies with

higher probability of appearance are located at integer frequencies. Table 3 contains the probability of

appearance for these cyclic frequencies located inside the region (-5, 5) MHz.

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Figure 16 - Boxplot of the activity indicator for 𝒇𝒔 = 𝟒𝟎 MHz.

Figure 17 - Boxplot of the activity indicator for 𝒇𝒔 = 𝟐𝟎 MHz.

Figure 18 - Boxplot of the activity indicator for 𝒇𝒔=10 MHz.

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Figure 19 - Probability of appearance for signals sampled by USRP, sampled at 40MHz, 20MHz,

10MHz.

Table 3 - Probability of appearance for cyclic frequencies.

Sampling frequency 40 MHz 20 MHz 10 MHz

Cyclic frequency (MHz) Probability %

4 62 68 86

3 81 71 87

2 6 45 87

1 69 3 16

0 100 100 100

-1 68 1 52

-2 1 61 87

-3 82 74 87

-4 75 69 85

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5. Collaboration with other project members

Over the first year, several collaboration work was developed with two other fellows, Anahid Basiri

(experienced researcher) in work package 2.3 and Daniel Ondrej (early stage researcher) in work package

3.1. This collaboration has led to the publication of two papers,

[104] A. Basiri, P. Figueiredo, E. S. Lohan, P. Peltola, C. Hill, and T. Moore, “Overview of

Positioning Technologies from Fitness- to-Purpose Point of View,” in ICL-GNSS, 2014.

[105] P. Figueiredo, O. Daniel, J. Nurmi, and E.-S. Lohan, “Cyclostationary features of

downsampled 802.11g OFDM signal for cognitive positioning systems,” in ISWCS (submitted),

2014.

In the work with Anahid Basiri [104], a fitness-to-purpose study was done which aims to help system

designers in the decision of which sources of signals of opportunity to use when creating current and

future location based applications. The aim of the work is to match the user necessities to the current

technologies that are able to provide them, offering a way to select the most valuable SoO for these

necessities or requirements.

The work with Ondrej Daniel [105] allowed testing with real world data the possibility of exploiting

cyclostationary properties using undersampled signals, reducing the complexity imposed by these

algorithms. In the context of signals of opportunities, this work allows for a quick understanding of the

spectral contents at a lower complexity level.

Collaboration with Pekka Peltola (early stage researcher) in work package 3.5 started recently, with the

joint development of an Android application for acquisition of signal data. Development is undergoing in

a private repository available at [106].This repository will be open in the future. The goal of the

application is to retrieve sensor information (Wi-Fi signals, Bluetooth and mobile) from the Android

system and linux kernel.

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6. Conclusions

This report summarises the research done over the first year of the project. Over this period the main

focus was on spectrum sensing techniques, especially cyclostationary methods, to detect and distinguish

the presence of two types of signals, OFDM and CDMA. The studies led to the development of a simple

technique that allows the detection of both signals. Also, in an attempt to reduce the complexity of the

algorithms, the usage of undersampled signals was studied, pointing out some changes in the behaviour of

the cyclostationary properties, mostly on the absolute value of the cyclic frequencies, but which still

allows for their identification.

For the future there are some open issues that will be under analysis, specifically

Development of a signal of opportunity application for Android

Study on the hybridisation of several SoO, such as RFID and Wi-Fi

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Appendix A – Parameters

Table 4 - Parameters used throughout the study.

Common parameters

Number of random trials, T 5000

Activity threshold, 𝑉𝑡ℎ 1

Observation time 2 and 10 ms

Cyclic resolution 1𝑒−5𝑓𝑠 Hz

Frequency resolution 0.1𝑓𝑠 Hz

Sampling frequency, 𝑓𝑠 40 MHz

Receiver filter bandwidth, 𝐵𝑇 20 MHz

K out of M 5 cyclic frequencies

CDMA parameters

Modulation DQPSK

Chip rate 11 MHz

Data rate 2 MHz

Spreading Sequence Barker code (11 chips)

First cyclic frequency, α1 1 MHz

OFDM parameters

Modulation 16-QAM

Number of carriers, N 64

Symbol period, 𝑇𝑠𝑦𝑚𝑏𝑜𝑙 4 µs

Sub-carrier spacing, ∆𝑓 B/N = 312.5 kHz

Guard interval, 𝑇𝑔𝑖 ∆𝑓−1 /4 s

Useful symbol period, 𝑇𝑈 ∆𝑓−1 s

Pilot power boost +3 dB

Bandwidth, B 20 MHz

First cyclic frequency, β1 250 kHz

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Appendix B – MATLAB code

All the code is available at https://bitbucket.org/silva/multi-pos-wp-4.2

https://bitbucket.org/silva/multi-pos-wp-4.2

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Appendix C – Published papers

P. Figueiredo, O. Daniel, J. Nurmi, and E.-S. Lohan, “Cyclostationary features of downsampled 802.11g

OFDM signal for cognitive positioning systems,” in ISWCS, 2014.

A. Basiri, P. Figueiredo, E. S. Lohan, P. Peltola, C. Hill, and T. Moore, “Overview of Positioning

Technologies from Fitness- to-Purpose Point of View,” in ICL-GNSS, 2014.

P. Figueiredo, M. L. Rahman, G. Seco-Granados, and E.-S. Lohan, “Detection of CDMA and OFDM in a

mixed signal for cognitive positioning systems,” in (to be submitted), 2014.

M. L. Rahman, P. Figueiredo, and E.-S. Lohan, “Cyclostationarity-based spectrum sensing properties for

Signals of Opportunity,” in IEEE International Conference on Wireless and Mobile Computing, 2014.

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