Optimization of 5G New Radio for Fixed

Wireless Access

Jonathan Palm

Computer Science and Engineering, master's level

2019

Luleå University of Technology

Department of Computer Science, Electrical and Space Engineering

Optimization of 5G New Radio for Fixed

Wireless Access

Jonathan Palm

2019

Abstract

With the advent of new 5G networks, the interest in connecting house-hold to the Internet via mobile networks has increased. One such way toconnect users is using completely stationary antennas. This use-case iscalled Fixed Wireless Access (FWA) and is seen as promising, cost-efficientmeans of expanding internet connectivity.

Stationary users connected at high frequencies, such as 28 GHz, leadsto a special use-case and environment for 5G New Radio (NR). This thesisinvestigates the characteristics of these FWA deployments and the controlsignaling on the physical layer of NR. The overhead and feasibility of eachsignal is considered.

A FWA deployment in the 28 GHz band with 64 users is simulated withdifferent line-of-sight settings and receiver placements. It is concludedthat direct line-of-sight to the base station is vital for high user and cellthroughput and that there are significant drawbacks of placing the receiverindoors.

New algorithms for Channel State Information Reference Signal (CSI-RS) transmission for both beam management and link adaptation are pro-posed and evaluated. The beam management algorithms do not displayany significant performance gains over the default sweeping algorithm.Closer investigation of simulation results shows that several beams canhave almost equal signal strength with the chosen antenna set up, mini-mizing potential gains of quickly adapting to environmental changes.

Results show there are clear benefits of using an aperiodic and adaptivetransmission scheme for CSI-RS transmissions over a fixed-rate transmis-sion scheme, yielding a 7% increase in user goodput at similar levels ofoverhead.

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Acknowledgments

First, I want to thank my supervisors at Ericsson Research, Marten Ericson andKristofer Sandlund, for providing invaluable help and feedback during the thesiswork. I would also like to thank Mats Nordberg for offering me the opportunityto work on this thesis and giving me great feedback on my thesis during theentire process.

Additionally, I’d like to give thanks to Anders Landstrom for his valuableinput when discussing ideas and Jonas Pettersson for assisting with both pro-cessing simulation results and debugging. Finally I’d like to thank my supervisorat Lulea University of Technology, Evgeny Osipov, for his feedback and guid-ance.

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Contents

1 Introduction 111.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.3 Scope and Limitations . . . . . . . . . . . . . . . . . . . . . . . . 131.4 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 A Short Introduction to the Physical Layer of NR 152.1 NR Transmission structure . . . . . . . . . . . . . . . . . . . . . 152.2 Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.3 Modulation and Coding Scheme . . . . . . . . . . . . . . . . . . . 16

3 An Analysis of Reference Signal Overhead in NR 173.1 Synchronization Signal Block . . . . . . . . . . . . . . . . . . . . 183.2 Channel State Information Reference Signals . . . . . . . . . . . 21

3.2.1 CSI-RS for Link Adaptation . . . . . . . . . . . . . . . . . 223.2.2 CSI-RS for Beam Management . . . . . . . . . . . . . . . 24

3.3 Demodulation Reference Signals . . . . . . . . . . . . . . . . . . 263.4 Phase-Tracking Reference Signal . . . . . . . . . . . . . . . . . . 283.5 Tracking Reference Signal . . . . . . . . . . . . . . . . . . . . . . 283.6 Reference Signal Overhead of a FWA Deployment . . . . . . . . 30

4 Method 324.1 Simulation Assumptions and Traffic Model . . . . . . . . . . . . 324.2 Metrics and Key Performance Indicators . . . . . . . . . . . . . . 324.3 A Simplified Video Traffic Model . . . . . . . . . . . . . . . . . . 33

4.3.1 Three Traffic levels . . . . . . . . . . . . . . . . . . . . . . 344.4 Evaluating the FWA Deployment . . . . . . . . . . . . . . . . . . 354.5 CSI-RS for Link Adaptation Optimization . . . . . . . . . . . . . 36

4.5.1 CQI-based Linear Decrease . . . . . . . . . . . . . . . . . 374.5.2 CQI-based AIMD Algorithm . . . . . . . . . . . . . . . . 384.5.3 Heuristic Table-based Algorithm . . . . . . . . . . . . . . 384.5.4 Simulations and Evaluation . . . . . . . . . . . . . . . . . 39

4.6 CSI-RS for Beam Management Optimization . . . . . . . . . . . 404.6.1 The Default Beam Sweeping Algorithm . . . . . . . . . . 414.6.2 Cached Beams Only Algorithm . . . . . . . . . . . . . . . 424.6.3 Alternating Default / Cached Beam Sweeps . . . . . . . . 424.6.4 Hybrid Algorithm . . . . . . . . . . . . . . . . . . . . . . 424.6.5 Simulations and Evaluation . . . . . . . . . . . . . . . . . 43

5 Results 445.1 Performance of Different FWA Deployments . . . . . . . . . . . . 445.2 CSI-RS for Link Adaptation . . . . . . . . . . . . . . . . . . . . . 535.3 CSI-RS for Beam Management . . . . . . . . . . . . . . . . . . . 61

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6 Conclusions 68

7 Future Work 69

A Overhead of the Physical Downlink Control Channel 72

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List of Figures

1 An illustration of a NR resource grid. . . . . . . . . . . . . . . . 162 A grid demonstrating CSI-RS transmissions for two users. . . . . 233 Overhead of a beam sweep of five beams. Each color represents

a different beam. . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 DMRS and PTRS transmission in one resource block during one

slot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 TRS layout over two resource blocks and two slots. Figure is

based on [15, Fig. 8.8]. . . . . . . . . . . . . . . . . . . . . . . . . 296 The total reference signal overhead of the deployment of 64 users

connected to nine cells detailed in Section 4. . . . . . . . . . . . . 317 The user and cell positions for the FWA simulations. . . . . . . . 338 The resource utilization at different traffic loads, assuming out-

door Customer Premises Equipment (CPE) and Line of Sight(LOS) to the Base Station (BS) . . . . . . . . . . . . . . . . . . . 36

9 The changes in the RSRP of the selected beam over time for asingle user during one simulation. . . . . . . . . . . . . . . . . . . 41

10 SINR comparisons between all configurations with a high trafficload. Note the difference between indoor and outdoor users. . . . 44

11 The average video bitrate of all users with different deployments. 4512 The modulation schemes used in the deployments for the 3 Mbps

video traffic load. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4613 The radio resource (sub-band) utilization for different deployments. 4714 The percentage of users unable to receive any data on the network

using the different deployments. . . . . . . . . . . . . . . . . . . 4715 The radio resource (sub-band) utilization for the 10%-tile of users. 4816 The percentage of users reaching 95% of the requested bit rate

using the different deployments. . . . . . . . . . . . . . . . . . . . 4917 Example coverage map of outdoor CPEs using Spatial Channel

Model (SCM). The coverage map is based on the measured Ref-erence Signal Received Power (RSRP) in dB. . . . . . . . . . . . 50

18 Example coverage map of indoor CPEs using SCM. The coveragemap is based on the measured RSRP in dB. . . . . . . . . . . . . 51

19 Number of transmissions per user depending on CPE locationand LOS settings. . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

20 CDF over measured RSRP during simulations of different CPElocations and LOS settings. . . . . . . . . . . . . . . . . . . . . . 52

21 Average cell throughput during high traffic load for different CSI-RS transmission schemes. . . . . . . . . . . . . . . . . . . . . . . 53

22 Average user SINR during high traffic load between different CSI-RS for link adaptation transmission algorithms. . . . . . . . . . . 54

23 Average video rates for all CSI-RS transmission algorithms on ahigh traffic load. . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

24 Experienced video rates for the users in the 10th percentile usingdifferent CSI-RS transmission algorithms. . . . . . . . . . . . . . 56

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25 Percent of users achieving 95% of requested video rate duringhigh traffic load (see Table 8) using different CSI-RS transmissionalgorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

26 Comparison of average FTP rates between different CSI-RS trans-mission algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . 58

27 Comparison of FTP rates for users in the 10th percentile betweendifferent CSI-RS transmission algorithms. . . . . . . . . . . . . . 59

28 Total overhead of CSI-RS for link adaptation. . . . . . . . . . . . 5929 Average user goodput of both FTP and video traffic for all CSI-

RS for link adaptation algorithms on a high traffic load. . . . . . 6030 Average user goodput between both video and FTP for the users

with a high traffic load and alternating sweep intervals. . . . . . 6131 Average FTP rates for the users with a high traffic load for dif-

ferent sweep intervals. . . . . . . . . . . . . . . . . . . . . . . . . 6232 Average cell throughput during high traffic load for different

sweep intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6333 Average downlink SINR with different beam sweeping algorithms. 6434 Average video rates for the users with a high traffic load for dif-

ferent beam sweeping algorithms. . . . . . . . . . . . . . . . . . 6535 Percentage of users reaching 95% of the requested bit rate for dif-

ferent sweeping intervals. Simulations are done using high traffic(see Table 8). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

36 Overhead of CSI-RS transmissions for beam management. . . . . 6637 Average RSRP of each beam in the base station’s beam grid for

one user. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

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List of Tables

1 The results of the study on reference signal overhead. Testabletells if optimization is possible to test in the simulator. Theoverhead listed in this table means the percentage of transmittedresource elements consumed by the reference signal in question. 17

2 The theoretical Synchronization Signal Block (SSB) overhead for60 and 120 kHz numerologies with burst sets of 8 or 64. Availablebandwidth is selected to demonstrate upper and lower bound ofSSB overhead. The overhead is the percent of all transmittedresource elements. . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3 The theoretical SSB overhead for 60 and 120 kHz numerologieswith burst sets of 8 or 64. Here it is assumed data transfer maynot take place in the slots used for SSB transmissions, but twoSSBs can be assumed to be transmitted in the same slot. Theoverhead is the percent of all transmitted resource elements. . . . 21

4 The overhead of CSI-RS transmissions for link adaptation. Thevalues in the table are per user and is calculated using Equation 2. 24

5 The per user overhead of CSI-RS transmissions for beam man-agement. The values in the table are calculated using Equation 3.The overhead is in percent of transmitted resource elements in acell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

6 The overhead for TRS signaling as calculated in Equation 4.Tracking Reference Signal (TRS) is assumed to use the denserformat and occupy 2 slots. . . . . . . . . . . . . . . . . . . . . . . 30

7 Simulation assumptions for the FWA deployment. . . . . . . . . 348 The different traffic settings for the simplified video model. Each

number on the FTP Rate setting denotes a separate FTP stream. 359 The CSI-RS transmission intervals selected for the table-based

algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4010 The average video rates and cell throughputs for different deploy-

ments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

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Glossary

beamforming the means of using multiple antenna elements to increase thetransmitted power towards a certain direction [15, Sec. 11]. 14, 16, 21

busy hour see peak hour . 12

coding scheme how information is coded with parity bits, for more informa-tion see [15, Sec. 9.2]. 15

frame 10 ms of transmissions, further divided into 10 subframes. 14

goodput useful throughput not counting any protocol overheads. 5, 31, 43,58–60

modulation scheme how data is encoded for a transmission. 15, 23

multipath signals taking multiple paths to the receiver. 20, 21, 39, 40

numerology see subcarrier spacing . 14, 19, 28, 29

peak hour the busiest hour of internet traffic in the span of a day. 10

reference signal signals which does not carry data for higher layers. 16, 17,20, 21, 26

resource block a group of twelve contiguous subcarriers in the frequency do-main. 21, 25, 27, 29

resource element one subcarrier in the frequency domain during the durationof a symbol in the time domain. 6, 17, 19–21, 27

slot a set of OFDM symbols in the time domain and is normally 14 symbols.6, 14, 18–25, 27–29, 41, 51, 60, 66

sub-band a fixed subset of the bandwidth. 4, 45–47

subcarrier a small portion of the bandwidth, size decided by subcarrier spac-ing . 14, 17, 18, 21, 27

subcarrier spacing the distance between neighboring subcarriers in the fre-quency domain. 14

subframe 110 of a frame, further divided into a number of slots depending on

numerology. 14

symbol the time domain duration of the smallest element of transmission, theduration depending on the current numerology . 14, 17, 24–28, 44

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Acronyms

3GPP 3rd Generation Partnership Project. 10

AIMD Additive Increase / Multiplicative Decrease. 37, 51–56, 58, 65, 66

BS Base Station. 4, 10, 11, 20, 21, 23, 24, 29, 34, 35, 37, 39–41, 45, 48, 49

CCE Control Channel Element. 66, 69, 70

CORESET Control Resource Set. 21, 69

CPE Customer Premises Equipment. 4, 10, 12, 34, 35, 43, 45, 48–50, 65

CQI Channel-Quality Indicator. 36, 37, 66

CSI Channel State Information. 11–13, 21, 35, 36

CSI-RS Channel State Information Reference Signal. 1, 4–6, 17, 20–23, 25,27, 30, 31, 36–39, 41, 51–58, 64–66

DAC Digital-to-Analog Converter. 14

DCI Downlink Control Information. 21

DMRS Demodulation Reference Signal. 4, 17, 21, 25–27, 30, 65, 69

FTP File Transfer Protocol. 5, 31, 34, 55–58, 60

FTTH Fiber-to-the-Home. 10, 11

FWA Fixed Wireless Access. 1, 6, 10–13, 15–23, 26, 29, 33, 35, 36, 39–41, 49,65, 66, 70

HARQ Hybrid Automatic Re-request. 45

KPI Key Performance Indicator. 31, 35, 42, 43, 59

LOS Line of Sight. 4, 11, 34–36, 43, 45, 48–50, 65

LTE Long Term Evolution. 10, 11, 13

MCS Modulation and Coding Scheme. 16, 17

MIMO Multiple Input Multiple Output. 11, 13

MU-MIMO Multi User MIMO. 12, 13

NLOS No Line of Sight. 11, 35, 45, 48

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NR New Radio. 1, 10, 11, 13–18, 20, 25, 27–29, 31, 65, 66

OFDM Orthogonal Frequency-Division Multiplexing. 14, 17, 25–28, 44, 69

PDCCH Physical Downlink Control Channel. 21, 65, 66, 69, 70

PDSCH Physical Downlink Shared Channel. 25, 26, 34

PTRS Phase-Tracking Reference Signal. 4, 17, 26, 27, 30, 65

QPSK Quadrature Phase-Shift Keying. 15

RAN Radio Access Network. 11, 12, 16, 31, 34, 65, 66

RB Resource Block. 21, 23, 27, 35, 69

RE Resource Element. 14, 16, 17

REG Resource Element Group. 69, 70

RSRP Reference Signal Received Power. 4, 5, 35, 40, 45, 48–50, 64

SCM Spatial Channel Model. 4, 43, 45, 48, 49, 65

SINR Signal to Interference and Noise Ratio. 4, 5, 32, 43, 52, 60, 62

SSB Synchronization Signal Block. 6, 17–21, 23, 29, 39, 52, 55

SU-MIMO Single User MIMO. 13

TCP Transmission Control Protocol. 34, 66

TRS Tracking Reference Signal. 4, 6, 17, 27–30, 65, 69

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1 Introduction

An increasing number of people are connected to the Internet, with as many as 1billion fixed broadband subscriptions and 5 billion mobile broadband subscrip-tions [1]. These users have an ever-increasing demand in traffic, with estimationsof over 100 Exabytes of video streamed per month in 2018 and over 300 Exabytesper month by 2022. [2]

Currently, most users in the developed world are using some form of wiredinternet connection to their homes. As customers consume more data and puthigher demands on their Internet service providers, old wireline technologies,such as copper, become unable to keep up. While the infrastructure is slowlyupgrading to modern technologies like optical fiber, this deployment can takeyears, if not decades.

While it is very beneficial to upgrade the core network with fiber, whereeach fiber can serve multiple users, it can become prohibitively expensive toinstall Fiber-to-the-Home (FTTH). The costs become especially high in placeswhere the necessary infrastructure is yet to be deployed. A more cost-effectivealternative could be to use wireless communications for the last hop. [3]

Earlier mobile network technologies, such as 3G, WiMAX and Long TermEvolution (LTE) have been considered for FWA in the past. However, theirthroughput and latency has had a hard time competing with fiber wirelinesolutions. [4]

The next generation of mobile networks, 5G, and its new air interface NewRadio (NR) is developed by the 3rd Generation Partnership Project (3GPP)and features improved capacity and latency compared with LTE and earliertechnologies. [3]

While the earlier technologies could mostly be seen as an alternative tofiber only where the necessary infrastructure is missing, the situation couldchange. With the higher capacity and lower latency of NR, it just may performwell enough to be a suitable, more cost-effective, competitor to fiber in somedeployments. [4]

Some challenges with large-scale FWA deployments remain, despite thehigher capacity offered by NR. Radio networks generally face performance degra-dation when under high load and must be accounted for when considering anFWA deployment in a large area. [4]

The performance degradation of wireless networks impacts how an FWAdeployment should be configured [5] [4]. Since users are expected to use anFWA solution in a similar way to fixed broadband, it becomes important thatthe network can keep up with user demand, even during peak hour traffic.

The capacity problem further increases as users demand an increasing amountof data, most of it being video. For some users, FWA might be expected to alsodeliver linear TV [4]. Additionally, 5G NR contains signaling related to usermobility and widely varying channel conditions, occupying the same radio spec-trum as the actual data transfers [6, Sec. 7.3]. Since the Customer PremisesEquipment (CPE) is expected to remained in a fixed position and directed to-wards the BS, such mobility functions are rarely going to be used.

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This work intends to provide insights the NR Radio Access Network (RAN)and investigate the different reference signals used and the expected overhead.Optimization opportunities for these reference signals will be explored once theoverhead has been determined. Algorithms will then be developed and detailedfor signals which could conceivably be optimized.

1.1 Related Work

Optimization of radio access networks in general has been an important researchtopic for many years. In particular, the quest for higher capacity is what havelead to the developments of the last few generations of mobile networks.

The concept of Fixed Wireless Access has already been studied in greatdetail. The studies have been mostly driven by network operators due to itscommercial significance, which have lead to pre-commercial trials of 5G. [7]

In [7], a 5G trial deployment was proposed. It featured dual connectivitywith LTE, as 5G standalone was not specified in 3GPP Release 15. It was alsopointed out that the 26/28 GHz band would likely require the user to have Lineof Sight (LOS), or close to LOS, to the BS in order to be effective. Decouplingthe uplink from the downlink, moving the uplink to a lower band, was alsoproposed due to expected problems with uplink coverage.

A study of pre-standard 5G networks for FWA and their financial viabilityalso found that proper line of sight to the BS is almost a necessity at higherfrequencies. A cell radius of 300 meters was shown to produce difficulties toprovide all users with sufficient coverage if there was any foliage. Penetrationlosses due to building materials was found to be between 20 and 60 dB. Thestudy also showed that 30–50 users would have to be served at each site forFWA to remain financially viable. [8]

Several studies have been done in order to evaluate and improve the prop-agation losses and penetration losses [9] [10]. In [10], the effects of a passiverepeater was investigated to remove the unfavorable No Line of Sight (NLOS)conditions of millimeter wave propagation.

A lot of the publicly available research has been done on cost comparisons ofdifferent backhaul technologies and other deployment aspects. One such paperinvestigated the commercial viability of FWA when compared to FTTH. It wasfound that FWA becomes increasingly viable with higher fiber prices, but maynot be a cost-efficient alternative for some deployments. [3]

The subject of channel state information in NR has also been studied. In[11], the authors investigated the use of a higher-resolution Channel State In-formation (CSI) reporting scheme for users with low mobility using Multi-UserMIMO. The article also discussed the importance of beamforming along withthe difficulties using digital beamforming at high frequencies. It was shown thatbeams would have to be narrow at high frequencies and frequent beam changeswould have to be expected if optimal performance was to be obtained. [11]

System-level design of FWA as an alternative to fixed broadband has beeninvestigated in [12]. The study displays the problems posed in the 28 GHz spec-trum when it comes to foliage and such. The work shows the very modest gains

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multi-panel transmission can give without employing Multi User MIMO (MU-MIMO). However, the work does not cover beam selection and dynamic char-acteristics of the environment. Furthermore, the work employs a ”full buffer”traffic model for comparisons. [12]

In [13], a new algorithm was proposed for doing precoder search in NR.Additionally, the different resolution CSI schemes of NR, type 1 and type 2,were compared against each other. It was found that the high quality type 2CSI does provide benefits when the spatial correlation of the channel is low.

The reporting overhead of channel state information has also been studiedin great detail. In [14], it was investigated whether prediction could be used toreduce the uplink signaling related to reporting channel state information. Itwas found that the uplink signaling could be successfully reduced by over 90%for slow moving users.

1.2 Objectives

Current research either considers FWA deployments or the RAN as a whole.While some of these studies, such as [14], takes mobility into account, there isa distinct lack of published work on improving the RAN for FWA.

The objective of this master’s thesis is to investigate FWA in more detail.Aspects such as how the nodes should be deployed for best performance is in-vestigated, as well as the prospect of tweaking current radio resource algorithmsfor best performance.

More specifically, the evaluation of FWA deployments is done by modelinga suburban environment where the CPE are stationary. As a second step, thecontrol signals and the overhead they pose are considered. The overhead ofeach signal is evaluated analytically. Finally, algorithms that utilizes the FWAproperties are developed and evaluated. This leads to these three main questionsto be answered:

1. What is the performance for a FWA deployment using the 28 GHz band?

2. What overhead is posed by signaling in the physical layer of NR?

3. Can characteristics of FWA be used to reduce signaling overhead and/orincrease performance?

1.3 Scope and Limitations

Video traffic currently makes up more than half of all internet traffic. Fur-thermore, it has been predicted that video will account for more than 80% ofinternet traffic for both consumers and businesses by 2022 [2]. These traffictrends clearly defines the common ’busy hour’ scenario for home internet users,and by extension, FWA users. The traffic is expected to be very downlink-heavy[2]. Therefore, the optimization work is to be constrained to the downlink, wherethe vast majority of traffic will end up.

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While the 5G standard encompasses both LTE and NR, only the latterwill be the focus of the thesis. Additionally, the optimization work relies on asimulator to evaluate the results. Therefore, optimizations of reference signalsin the NR specification that the simulator isn’t capable of accurately modelingwill be discussed on a theoretical level, but no improvements will be proposedor implemented. This study will be limited to the deployment of FWA in the28 GHz band, since it seems to be the frequency range most likely to be used.

Studies have already been done on different Multiple Input Multiple Output(MIMO) settings and the benefits of using MU-MIMO over Single User MIMO(SU-MIMO) [12]. Also, MU-MIMO becomes more difficult to use in simulations.As such, the simulations will be performed with SU-MIMO only.

It also has been shown that there may be a benefit of using the more high-resolution type 2 CSI feedback in FWA deployments, especially in MU-MIMOscenarios. Therefore, the performance difference between CSI report resolutionwill not be investigated. [13]

1.4 Thesis Structure

First, the reader is provided with a short introduction of NR in Section 2:A Short Introduction to the Physical Layer of NR. Then, the eachreference signal in NR downlink will be considered in Section 3: An Analysisof Reference Signal Overhead in NR. Conclusions will be made aboutwhich reference signals are appropriate for further optimization work.

The simulation environment and traffic model will be detailed in Section 4:Method. The chapter will also cover the proposed algorithms for optimizingreference signal transmissions and how they will be evaluated.

The deployment simulation, along with the reference signal algorithms inthe Method section will then be simulated and displayed in Section 5: Results.

Finally, the future work will be discussed and conclusions will be made inSection 6: Conclusions.

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2 A Short Introduction to the Physical Layer ofNR

This section is dedicated to providing the reader with some insights into howNR works on the physical layer. The concepts presented here will be used inother sections of the thesis. A more in-depth introduction to NR can be foundin [15].

First, the concept of Orthogonal Frequency-Division Multiplexing (OFDM)is introduced along with definitions of the physical resources used in NR. Thenconcept of beamforming for high frequencies is introduced to the reader. Finally,there is a short section on modulation and coding in NR.

2.1 NR Transmission structure

NR uses OFDM for its transmissions. Each transmission is divided in the timedomain into frames, subframes, slots and symbols. Each frame lasts 10 millisec-onds, further divided into 10 subframes of 1 millisecond each. Subframes arethen divided into slots. The number of slots and the slot duration depend onthe numerology of the transmission. [15, Sec. 5.4]

For instance, at 15 kHz subcarrier spacing (numerology), a subframe containsa single slot. With a numerology of 120 kHz, the same subframe would containeight slots. Regardless of numerology, a slot contains 14 OFDM symbols. [15,Fig. 7.1]

In the frequency domain, transmissions can occur on different subcarriersin order to provide more capacity. The available bandwidth is divided intosubcarriers. The exact number of subcarriers available depends on the subcarrierspacing and the available bandwidth. For instance, 400 MHz bandwidth with120 kHz subcarrier spacing would have 3300 available subcarriers. [15, Sec. 7.3]

NR defines a RE, or Resource Element, which is one subcarrier for theduration of one OFDM symbol [6, Sec. 4.4.3]. Subcarriers are also grouped intoResource Blocks, or RBs. Each RB consists of 12 subcarriers [6, Sec. 4.4.4.1].

The subcarriers and the symbols defined above forms what is called a re-source grid [6, Sec. 4.4.2]. An illustration of a resource grid can be found inFigure 1.

2.2 Beamforming

The antenna array in NR base stations can form directed beams to reduceinterference and increase the received power for the user. This practice is calledbeamforming. [15, Sec. 11]

The beamforming can be done either before or after the Digital-to-AnalogConverter (DAC) in the transmitter. Beamforming performed before the DACis called digital beamforming and analog if performed after. The main differencebetween the two is that it isn’t possible to transmit to more than a single user inthe same symbol when analog beamforming is applied. This makes it impossibleto do any frequency duplexing between different users, effectively giving any

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Figure 1: An illustration of a NR resource grid.

single user the full bandwidth during each symbol the user is scheduled in. [15,Sec. 12]

Despite the limitations of analog beamforming, it is expected to be moreprevalent than digital beamforming for higher frequencies, such as those usedin an FWA deployment.

2.3 Modulation and Coding Scheme

Radio conditions may vary drastically between users. Because the reception ofsignals may be more difficult during some conditions, it is advantageous to beable to encode transmissions differently depending on the recipient.

NR supports the use of different modulation schemes. This can be any-thing from two bits per transmitted symbol with Quadrature Phase-Shift Key-ing (QPSK) to eight bits per symbol with 256QAM (Quadrature AmplitudeModulation) [6, Tab. 6.3.1.2-1].

To further improve performance in noisy channels, it is possible to definedifferent coding schemes. A higher coding rate implies a higher ratio of infor-mation bits, thus improving efficiency. The upside with adapting modulationschemes and coding rates is that it becomes possible to ensure high bit rates andefficiency, while not allowing error rates to become excessive. [16, Sec. 5.2.2.1]

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3 An Analysis of Reference Signal Overhead inNR

In order to successfully provide optimizations for FWA, it is necessary to do athorough study of the reference signals used by the NR RAN. Each subject foroptimization found is first discussed theoretically, then it will be determined ifit can be simulated by the models available and what room there is for opti-mization. Algorithms for reference signals which can be simulated will then bepresented in Section 4 and simulation results will be evaluated in Section 5.

A summary of the findings of this section can be found in Table 1. The over-head detailed in Table 1 represents a percentage of all transmitted Resource El-ements (REs). Whether testing optimizations of those signals is feasible withinthe scope of the thesis is detailed in the column labeled testable.

Table 1: The results of the study on reference signal overhead. Testable tellsif optimization is possible to test in the simulator. The overhead listed in thistable means the percentage of transmitted resource elements consumed by thereference signal in question.

Reference Signal Overhead TestableCSI-RS (beam management) see Table 5 yesCSI-RS (link adaptation) see Table 4 yesDMRS (single slot) 3.44% noDMRS (double slot) 7.14% noPTRS (every other slot) 1.79% noPTRS (every fourth slot) 0.60% noSSB see Table 3 yesTRS 0.09% no

Since FWA is highly focused on high carrier frequencies with higher penetra-tion losses, beamformings becomes a necessity. Beamforming involves directingthe transmission power into a certain direction, instead of radiating the signalsover the entire cell. Users will then receive their transmissions on the beammost suited for them. This increases the received power for the user, whilesimultaneously reducing the interference for other users. [17]

Users are expected to move around in a mobile network. User mobility willalter the user’s angle with the base station, resulting in poor coverage from thepreviously selected beam. Therefore, there needs to be some form of signalingbetween the user and the base station to ensure both parties are aware of thesechanging conditions. The problem with these reference signals is that theyoccupy the same radio resources as actual data transmissions [6, Sec. 7.1.2].

Frequent transmissions of these reference signals adds overhead to data trans-fer. The knowledge provided by the channel state reports from users will helpthe base station select a proper Modulation and Coding Scheme (MCS) for thetransmissions. Using the proper MCS improves performance as more bits canbe sent on the same radio resources. Choosing a too low MCS would result in

17

lower performance, while a too high MCS would result in an increased amountof retransmissions. [16, Sec. 5.2.2.1]

However, frequent transmissions of some of the reference signals may not becompletely necessary. As the FWA user is expected to be stationary at all timesand have a more stable radio environment, some of this signaling may prove tobe superfluous. Each reference signal for the downlink will be listed below andthen considered in their own section.

The reference signals, as defined in [15, Sec. 9.11], are the following:

• Channel State Information Reference Signal (CSI-RS)

• Demodulation Reference Signal (DMRS)

• Tracking Reference Signal (TRS)

• Phase-Tracking Reference Signal (PTRS)

• Synchronization Signals.

Each of the signals listed above will be investigated in their own sections.First, the synchronization signal will be covered in Section 3.1. CSI-RS for bothlink adaptation and beam management is investigated in Section 3.2. Overheadsof Demodulation Reference Signal (DMRS), Phase-Tracking Reference Signal(PTRS) and TRS are covered in Sections 3.3, 3.4 and 3.5, respectively.

However, NR also features other control signals, such as L1/L2 signaling [15,p. 166]. A brief overview of the downlink control signals detailed in [15, Sec. 10]can be found in Appendix A.

3.1 Synchronization Signal Block

Synchronization blocks are vital for initial access to a system. They allow devicesto discern important system information, which in turn would allow a device toperform the initial access routine. These blocks can be sent with any intervalfrom 5 to 160 milliseconds, but according to the current release 15 standard adevice can only be expected to listen for a maximum of 20 milliseconds for aSynchronization Signal Block (SSB) [15, p. 314].

Each SSB takes up 240 subcarriers in the frequency domain during 4 OFDMsymbols in the time domain. The actual transmission 576 resource elements,which is less than the allocated 240 · 4 = 960 resource elements. However, itcannot be assumed that the unused REs could be used for something else. [15,p. 312]

The exact overhead of SSB transmission becomes dependent on system band-width and something called Synchronization Signal (SS) burst set. The SSB hasa fixed frequency size in the frequency domain, always occupying 240 subcar-riers. Its overhead may be small on systems with large bandwidth, while it isimpossible to transmit the SSB if the bandwidth is less than 240 subcarriers.The burst set can consist of up to 4 Synchronization Signal (SS) blocks in the

18

time domain for low frequencies, 8 for both high and low frequencies and up to64 for high frequencies. [15, p. 316]

Since FWA is expected to operate in high frequencies, it is reasonable to as-sume that the burst set can contain anything between 8 and 64 SSBs, dependingon the beam grid. Given the assumptions above, the formula for calculating theSSB overhead is the following:

OSSB =fSSB Burst Set · nSSBs per Burst Set · nREs per SSB

nsubcarriers · nOFDM symbols per second, (1)

where fSSB Burst Set = 1/tBurst Set and tBurst Set is the interval between burstset transmissions in seconds, nREs per SSB = 960, nSSBs per Burst Set is anywherebetween 8 and 64, with nsubcarriers and nOFDM symbols per second being dependenton numerology and available bandwidth.

The overhead described in Equation 1 becomes dependent on a variety ofsettings. Table 2 details the overhead for SSB transmission on a variety ofbandwidths for what can be expected to be the most common numerologies forNR in the 28 GHz band: 60 kHZ and 120 kHZ. The maximum bandwidth ofNR is 3300 subcarriers [6, Sec. 4.4.2]. Additionally, SSB transmission requiresa bandwidth of at least 240 subcarriers, as mentioned above.

Equation 1 only holds true if it is possible for the system to send data onthe same slot as an SSB. If that is not the case, then the full bandwidth duringnSSBs per Burst Set/2 slots would need to be considered occupied [15, Fig. 16.3]. Ifthat’s the case, then the overhead becomes significantly larger as demonstratedin Table 3.

19

Table 2: The theoretical SSB overhead for 60 and 120 kHz numerologies withburst sets of 8 or 64. Available bandwidth is selected to demonstrate upper andlower bound of SSB overhead. The overhead is the percent of all transmittedresource elements.

Numerology(kHz)

Bandwidth(subcarriers)

Burst Interval(ms)

SSBsper Burst

Overhead(%)

60 240 5 8 11.4360 3300 5 8 0.8360 240 5 64 91.4360 3300 5 64 6.6560 240 20 8 2.8660 3300 20 8 0.2160 240 20 64 22.8560 3300 20 64 1.6660 240 160 8 0.3660 3300 160 8 0.0360 240 160 64 2.8560 3300 160 64 0.20

120 240 5 8 5.00120 3300 5 8 0.42120 240 5 64 45.72120 3300 5 64 3.32120 240 20 8 1.43120 3300 20 8 0.10120 240 20 64 11.43120 3300 20 64 0.83120 240 160 8 0.18120 3300 160 8 0.01120 240 160 64 1.43120 3300 160 64 0.10

As Table 2 clearly shows, the overhead can be high for large burst sets. Itbecomes even more evident when a lower numerology or bandwidth is availableto the system. Even at high numerologies, but with modest bandwidths, sendinga burst set of 64 SSBs every 20 milliseconds can impose a noticeable performancepenalty.

If the entire slot would be occupied by SSB transmissions, then the overheadquickly becomes prohibitive. With a SS burst interval of 20 milliseconds, thepenalty is still at 2.50% at 120 kHz numerology as seen in Table 3. Sending dataand SSBs on the same slot becomes vital to keep the overhead at manageablelevels.

For FWA, the devices can potentially be set up to work with any intervalbetween SSB transmissions. Configuring the users to listen for a full 160 mil-

20

Table 3: The theoretical SSB overhead for 60 and 120 kHz numerologies withburst sets of 8 or 64. Here it is assumed data transfer may not take placein the slots used for SSB transmissions, but two SSBs can be assumed to betransmitted in the same slot. The overhead is the percent of all transmittedresource elements.

Numerology(kHz)

Burst Interval(ms)

SSBsper Burst

Overhead(%)

60 5 8 20.0060 5 64 160.0060 20 8 5.0060 20 64 40.0060 160 8 0.6360 160 64 5.00

120 5 8 10.00120 5 64 80.00120 20 8 2.50120 20 64 20.00120 160 8 0.31120 160 64 2.50

liseconds could allow a system to transmit fewer SSBs than it would otherwisehave to. However, such a change would lead to longer initial access times, asa device could potentially have to wait long before they receive vital systeminformation.

Optimizing SSB transmissions becomes unfeasible if other, non-FWA, de-vices are expected to connect to the same network. Having too low SSB period-icity could lead to some devices not finding the network, as there’s no guaranteean SSB transmission would occur during a monitoring occasion. Instead, itwould be beneficial to only send burst sets every 20 milliseconds and ensure theSSBs can be transmitted over a beam sweep using smaller burst sets.

3.2 Channel State Information Reference Signals

With the high frequencies of NR for FWA, it becomes important for both theBS and the user to know their radio environments. This knowledge makes itpossible for both parties to compensate for changes in the radio environmentincluding interference, multipath propagation, and penetration loss.

In order to accomplish this, the BS transmits a known reference signal tothe user known as the CSI-RS. It is then up to the user to measure this signaland report its channel properties back to the BS, thus allowing both parties tomaintain channel knowledge.

In addition to the use detailed above, which can be seen as the use of CSI-RS for link adaptation, the reference signal can also be used for beam selection.

21

This means the BS transmits CSI-RS for different beams in order to let devicesmeasure the best beam. However, how these ’beam sweeps’ should be done isnot specified in the standard. Instead, it is up to the implementer to choose thebeam sweeping procedure. [15, p. 243]

Therefore, each usage of the CSI-RS will be covered in separate sections.CSI-RS for link adaptation is investigated in Section 3.2.1 and CSI-RS for beammanagement in Section 3.2.2.

3.2.1 CSI-RS for Link Adaptation

Accurate and up to date CSI is important due to the reasons specified above,but becomes especially important for highly mobile users. These users mayexperience vastly different and rapidly changing radio conditions, especially asthey may move in and out of line of sight to the BS.

The situation is expected to be completely different for the FWA scenario.Channel conditions are reasonably stable with completely stationary users. Withlimited changes in multipath propagation, most channel variations should comefrom interference from other cells or users.

How often CSI transmissions are supposed to be scheduled is up to the BS.The only real restrictions of when a CSI-RS can be transmitted is that it willnot occupy slots used during DMRS, SSB, or Control Resource Set (CORESET)transmissions. While CSI-RS is defined per-device, nothing prevents severaldevices to share the transmitted reference signal. Each CSI-RS occupies a singleresource element per port. [15, pp. 133–147]

CSI-RS can cover as little as a single subcarrier in the frequency domainor as much as the entire available bandwidth (or active bandwidth part). Thereference signal can be repeated every resource blocks or every other ResourceBlock (RB) within the bandwidth allocated for it. An example of transmissionsof 2-port and 8-port CSI-RS can be seen in Figure 2. [15, pp. 133–147]

Considering the time domain, CSI-RS can be periodic, semi-persistent orcompletely aperiodic. For periodic CSI-RS, anything between every 4th to every640th slot is acceptable. With a semi-persistent configuration, the periodictransmissions can be toggled on and off by higher layers, but otherwise functionsas periodic CSI-RS. Aperiodic CSI-RS has no periodicity at all. Instead, it issignaled by the Downlink Control Information (DCI) in the Physical DownlinkControl Channel (PDCCH). [15, Sec. 8.2.3]

Three assumptions needs to be made when calculating the potential over-head for CSI-RS. First, it will be assumed that even aperiodic CSI-RS willnot be transmitted at a higher frequency than every 4th slot, the highest fre-quency allowed for periodic transmissions. Second, analog beamforming willbe assumed, as it is expected to be most common for 28 GHz deployments, asused in this thesis. Analog beamforming prevents the BS to transmit to anyother user in the same symbol. The final assumption is that one CSI-RS re-source is only intended for a single user, thus the calculated overhead will be peruser on a single cell. If some users are to share CSI-RS resources, then furtherassumptions would have to be made about their selected beam.

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Figure 2: A grid demonstrating CSI-RS transmissions for two users.

The complete per-user overhead detailed in Table 4 is calculated as follows:

OCSI-RS =nCSI-RS ports · ρ

nREs per RB · nSymbols per slot ·∆t, (2)

where ρ is the density ( 11 or 1

2 [6, Tab. 7.4.1.5.3-1]) and ∆t is the time betweenCSI-RS in slots.

As Table 4 clearly shows, the difference in CSI-RS overhead for link adap-tation can be vastly different depending on periodicity and system design. IfCSI-RS has to be sent every slot, the overhead can become as high, but almostnegligible if sent every 640 slots.

Therefore, minimizing the CSI-RS transmissions can have a great impact onperformance. Luckily, the channel is expected to remain relatively static for theFWA scenario. It is likely that the CSI-RS transmissions can be reduced to freeup resources for data transmissions.

However, such optimizations would have to be done with care. If the channel

23

Table 4: The overhead of CSI-RS transmissions for link adaptation. The valuesin the table are per user and is calculated using Equation 2.

Periodicity(slots)

CSI-RSports

Density 1/2Overhead

(%)

Density 1/1Overhead

(%)1 1 0.30 0.601 2 0.60 1.191 4 1.19 2.384 1 0.08 0.154 2 0.15 0.304 4 0.30 0.60

640 1 4.69 · 10−4 9.37 · 10−4

640 2 9.37 · 10−4 0.0019640 4 0.0019 0.0037

state information becomes to inaccurate, the system performance is bound tosuffer, as poorly chosen modulation schemes could result in unnecessary retrans-missions if overestimated or low bit rates if underestimated. Since there’s a lotthat can be done about CSI-RS in NR, special algorithms to reduce transmissionrates in an FWA scenario will be covered in Section 4.5.

Additionally, this analysis only covers the CSI-RS overhead for the purposesof link adaptation. The overhead of CSI-RS for beam management will bepresented in Section 3.2.2 below.

3.2.2 CSI-RS for Beam Management

The BS regularly sends out either SSB or CSI-RS on different beams to enableusers to measure which beam is the most suitable for reception [15, Sec. 12.2.1].At higher frequencies, analog or hybrid beamforming is used, meaning that itwon’t be possible to transmit to different users during the same symbol. Thechoice of beamforming also has an impact on how beam selection is made [15,Sec. 12].

In the case of analog beamforming, beam measurements are carried out bythe process of beam sweeping. The process consists of the BS sending outreference signals from one beam at a time, allowing the users to measure andreport back the results to the base station. Sending SSBs for this purpose wouldbe very expensive, as mentioned in Section 3.1. CSI-RS can be used instead.[15, Sec. 12]

As data cannot the multiplexed with the beam sweep, the CSI-RS used forbeam sweeping must be considered to cover the entire bandwidth for overheadcalculations. Therefore, Equation 2 does not hold for calculating the overheadof CSI-RS for beam management. The overhead of a beam sweep in a singleRB during one slot can be found in Figure 3, where the greyed out resourceelements are used for neither sweeping nor data.

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How beam sweeping should be done is entirely up to the BS and is notmentioned in the standard. The overhead per user in a cell can be calculatedas the following:

Obm =nbeams

tsweep · nsymbols per slot, (3)

where nbeams is the number of beams tested at each occasion, tsweep is thetime between sweeps in slots, and nsymbols per slot is the number of symbols perslot (14 by default). Whether Equation 3 refers to the total overhead of beamsweeping or per user in a cell, once again, depends on how it is configured inthe BS. The overhead of sweeping by Equation 3 for common sweep intervalscan be found in Table 5. For this thesis, optimizations will be made assuminga sweeping algorithm detailed in Section 4.6.1 and that sweeping is performedon a per-user basis.

Figure 3: Overhead of a beam sweep of five beams. Each color represents adifferent beam.

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Table 5: The per user overhead of CSI-RS transmissions for beam management.The values in the table are calculated using Equation 3. The overhead is inpercent of transmitted resource elements in a cell

Sweep Interval(slots)

Beamsper

Sweep

Overhead(%)

10 4 2.8610 5 3.5750 4 0.5750 5 0.71

100 4 0.29100 5 0.36500 4 0.06500 5 0.07

1000 4 0.031000 5 0.04

3.3 Demodulation Reference Signals

An important reference signal for NR is the DMRS. It is transmitted on thePhysical Downlink Shared Channel (PDSCH) to enable devices to receive in-formation properly. It takes half the bandwidth of a given resource block andcovers 1 or 2 OFDM symbols. The signal is usually repeated at least once withina slot for redundancy. [15, Sec. 9.11.1]

DMRS can occur in one of several places of a resource block, with two kinds ofDMRS mappings. The first mapping, type A, is usually placed in slots where thetransmission in question would occupy the majority of that slot. It is generallyplaced in either symbols 2 or 3 to make room for other control information, butstill early enough to aid with channel estimation for the current transmission.An example of a single symbol type A DMRS transmission is seen in Figure 4.[15, Sec. 9.11.1]

The other type of DMRS, type B, would instead be placed at the beginning ofthe data transmission. It is mainly used in situations where a transmission onlycover a small part of a slot. Since it covers at least one symbol, the overhead ofDMRS can be high for type B transmissions.

Consider DMRS type A. This overhead means about 114 for single slot DMRS

and 17 for double-slot DMRS of each transmission will be lost to overhead. This

is in addition to control elements occupying slot 1 and possibly slot 2.Since DMRS cannot be saved or interpolated between slots, the least amount

of overhead caused by the signal would be half the bandwidth of a single slot[15, Sec. 9.11.1]. This would be 1

28 (or 114 ) of the entire resource block in this

ideal scenario. This would correspond to 114 −

128 ≈ 3.44% gains for single slot

DMRS and 17 −

114 ≈ 7.14% for double slot DMRS.

With type 2 DMRS, the overhead can be much higher. As the transmission

26

Figure 4: DMRS and PTRS transmission in one resource block during one slot.

may span as little as 3 symbols, the overhead could be as much as 16 with single-

symbol DMRS. If the DMRS is repeated, there are cases when the overhead canbe as much as 20% of the entire transmission, covering half the bandwidth of 4out of 10 OFDM symbols. [15, fig. 9.16, p. 175]

However, the DMRS is there for a reason. If less DMRS are sent throughPDSCH, the remaining data becomes harder for the user to decode, especiallyif there are rapid variations in the channel.

This optimization could very well work within a FWA situation, as the rela-tively stable channel could warrant sending DMRS in the sparsest way possible.But the current simulator cannot model the DMRS as anything other than over-head. This means any removal of DMRS would lead to a performance increase.In a real world scenario, the same removal of this reference signal could easilyresult in a noticeable performance degradation due to difficulties in decoding.

The limitations of the simulator makes it very difficult experimentally eval-uate the gains of this optimization. As any gains demonstrated in the simulatorwould be unlikely to reflect real world performance, this optimization will not

27

be investigated further in this thesis.

3.4 Phase-Tracking Reference Signal

The Phase-Tracking Reference signal can be considered an extension of theDMRS. It is possible that there’s a significant phase variation during the trans-mission of a slot, especially at high frequencies. In that case, the DMRS mightnot be enough for the receiver to properly decode the entire transmission.

As the PTRS is meant to supplement the DMRS, it is never sent with (ordirectly after) one of those signals. Instead, it is sent between DMRS occasions.What makes the PTRS different from the DMRS is how it takes much less band-width, occupying a single subcarrier, but is repeated more frequently. PTRScan be repeated either every second OFDM symbol or every fourth OFDM sym-bol after a DMRS. The transmission of PTRS in one resource block during onesymbol is seen in Figure 4. [15, Sec. 9.11.3]

The overhead of this signal is low due to how sparse it is. The maximumoverhead would be if the PTRS would be transmitted every second symbol,taking up a total of six resource elements (the seventh isn’t necessary due toDMRS being present) in a RB, or 3.57% of the resources. The sparser trans-mission rate of every fourth symbol would lead to a mere 1.19% overhead in aresource block. To make the overhead even smaller, the PTRS is only sent ineither every other or every fourth RB. Being incredibly sparse, the overhead ofthe PTRS becomes 1.79% at most. [15, pp. 180–182]

Because of limitations similar to how DMRS is implemented in the simula-tor, it would not be feasible to try and limit the transmissions of PTRS. Anyreduction in PTRS transmissions would result in a performance increase, but itis unlikely that will reflect a real-world scenario.

3.5 Tracking Reference Signal

The Tracking Reference Signal, or TRS, is meant to aid devices in decodingtransmissions, in a similar way to the DMRS. This reference signal is neededbecause of how oscillators work. Small imperfections can pull devices out of syncwith the network. This can be either in frequency, in time, or both. Shouldthe device drift too far from the network in either time or frequency, decodingreceived signals becomes impossible. [15, Sec. 8.1.7]

In order to address this, the TRS is transmitted on a regular basis. Commonconfigurations are every 10, 20, 40 or 80 milliseconds. The signal, represented asspecific CSI-RS configurations in NR, can take one of two forms. The first formis to send the signal as two single symbol CSI-RS in each of two consecutiveslots. The second form is to send two CSI-RS within one slot. Regardless ofform, the symbol can only be repeated every fourth subcarrier [6, Tab. 7.4.1.5.3-1] and is repeated three times in every resource block. The transmission patternis illustrated in Figure 5. [15, Sec. 8.1.7]

The symbol being very sparse implies a low overhead. Transmitted togetherwith data, the overhead is minimal, taking up at most 1

4 of the total bandwidth

28

Figure 5: TRS layout over two resource blocks and two slots. Figure is basedon [15, Fig. 8.8].

of two or four OFDM symbols every 10/20/40/80 milliseconds. With the differ-ent numerologies in NR, the total overhead will be completely dependent on thechosen numerology, TRS size and the TRS periodicity. The overhead of TRS is

OTRS =2NTRS

4 · fsymbol · TTRS, (4)

where NTRS is how many slots are occupied by the TRS signal, TTRS is timebetween TRS transmissions and fsymbol is the symbol frequency defined by thenumerology. fsymbol can be defined as 1000

Tslotwith Tslot being the slot duration in

milliseconds. Tslot can range anywhere from 0.125 milliseconds to 1 millisecond[15, Fig. 7.2]. The overhead can be seen in Table 6.

As Table 4 displays, the overhead is very small at high numerologies. How-ever, high periodicity of the TRS signals does have a noticeable overhead at thelowest numerology.

29

Table 6: The overhead for TRS signaling as calculated in Equation 4. TRS isassumed to use the denser format and occupy 2 slots.

Numerology(kHz)

TRSperiodicity

(ms)Overhead (%)

15 10 0.7115 20 0.3615 40 0.1815 80 0.0930 10 0.3630 20 0.1730 40 0.0930 80 0.0460 10 0.1860 20 0.0960 40 0.0460 80 0.02

120 10 0.09120 20 0.04120 40 0.02120 80 0.01

For FWA, a high numerology is likely used to increase data rates. This willgreatly limit the overhead posed by TRS, if it proves necessary. Ideally, the usershould only receive signals from the transmitting BS. It might be possible forthe user to not desynchronize from the BS in this environment, even with fewor no transmissions of TRS.

Being intrinsically linked with how hardware is implemented, the removal ofTRS does not have any negative impact on decoding transmissions in a simulatedenvironment. Instead, any removal of TRS will result in a performance gain.Therefore, it will not be possible to evaluate any real-world gains of removingthis signal.

3.6 Reference Signal Overhead of a FWA Deployment

Earlier parts of this section investigated the overhead for each individual ref-erence signal in NR. Some overheads are per user in a cell, while others maybe per transmitted resource block during a slot. This section intends to give aclearer picture of the overhead of a FWA deployment.

These calculations assume the same deployment detailed in Section 4, for atotal of 64 users and 9 cells. A resource usage of 100% is assumed. The totaloverhead is summarized in Figure 6

SSB transmissions is done with a burst set size of eight SSBs and is as-sumed to be transmitted every 20 milliseconds. The SSB overhead is 2.50% as

30

calculated in Table 2.The CSI-RS for link adaptation overhead is 0.84% in total by Equation 2,

assuming 2-port CSI-RS transmitted to each user every 10 slots. Beam sweepingis done every 40 slots, resulting in an overhead of 0.89% per user in a cell byEquation 3 with 5 beams per sweep, which results in a total overhead of 6.34%.

Single symbol DMRS and the denser format of PTRS is assumed to be usedin every slot. The overhead is therefore 7.14% by Section 3.3 and 1.79% bySection 3.4 for DMRS and PTRS, respectively. TRS is assumed to be enabledand sent every 20 milliseconds to each user, yielding an overhead of 0.09% asdetailed in Table 4, which results in 0.64% overhead for all users.

Figure 6: The total reference signal overhead of the deployment of 64 usersconnected to nine cells detailed in Section 4.

31

4 Method

With the overhead of each reference signal calculated in Section 3, it is foundthat the reference signal with most room for optimization is CSI-RS. First, thesimulation assumptions are detailed and then compared to the earlier studiesdetailed in Section 1.1. After that, the algorithms for optimizations of CSI-RSfor both link adaptation and beam management are discussed in Sections 4.5and 4.6, respectively.

4.1 Simulation Assumptions and Traffic Model

A proprietary simulator from Ericsson will be used to implement and evaluatethe performance of the Fixed Wireless Access scenarios. The simulator is a state-of-the-art system-level simulator, featuring detailed models. It will be used tosimulate the entire network stack, from the application-level video streams orFTP downloads, to the transfer over the NR radio channel.

All simulations are done on a scenario striving to emulate a rural area withlarge distances between ’users’. Each ’user’ in the simulations is meant to repre-sent the traffic of a household. A total of 64 users are placed in a eight by eightgrid layout, with 75 meters between the households in one direction and 85 inthe other direction. These buildings are served by three cellular sites containingthree cells each for a total of nine cells. Each of the cells at the sites cover 120degrees. The deployment can be seen in Figure 7. Additionally, the simulationarea is wrapped, meaning the cell is repeated in each direction.

The 3GPP Urban Macro scenario is chosen for the simulations, as the ”Sub-urban Micro” model used in [12] was not adopted by the 3GPP in [18]. Adifferent traffic model from the ”full buffer” model used in [12] is presented inSection 4.3 to better reflect the expected Internet traffic of households. Thesimulation parameters are presented in Table 7.

Each simulation is run multiple times with different seeds for random num-ber generation. Results are based on the mean values obtained from thesesimulations.

4.2 Metrics and Key Performance Indicators

In order to evaluate the performance of an FWA system, some Key PerformanceIndicators (KPIs) must be established. Only downlink data is considered dueto the limitations discussed in Section 1.3. The most important KPIs have beenidentified as the following:

1. FTP rate as defined in [19, Sec. 6.1.7]

2. User goodput, which is application level throughput as defined in [20].Here, retransmissions in the RAN also are also deducted from the goodput.

3. Percentage of satisfied users, where a ”satisfied user” is defined here as auser reaching 95% of their requested video rate.

32

Figure 7: The user and cell positions for the FWA simulations.

4. Cell throughput, which is defined as throughput at layer 3 and above fora cell in this thesis.

5. Signal to Interference and Noise Ratio (SINR), due to its link to channelcapacity by the Shannon-Hartley theorem.

4.3 A Simplified Video Traffic Model

The majority of Internet traffic today is video, as mentioned in Section 1.3.While it is certainly possible to simulate video downloads in the simulator,using video traffic would result in a drastic increase in simulation time.

Instead of using proper video downloads, the traffic model has been changedto be more lightweight in the simulator, while still providing video-like proper-ties. No explicit playout buffer model is used and each 1-second video segmentis downloaded on-demand. No rate adaptation is performed for the video to ac-commodate the available network resources. Instead, a minimum bitrate for thevideos is defined and segments are set to be downloaded once every second. Inorder to reduce huge traffic bursts the segment size of the received video objectsis adapted to be transmitted over 400 milliseconds at the least with a transmis-sion rate of 10000 segments per second. This gives a maximum throughput of10.4 = 2.5 times the requested bitrate.

33

Table 7: Simulation assumptions for the FWA deployment.

Parameter Value

Carrier Frequency 28 GHzBandwidth 40 MHz (both DL and UL)Numerology 120 kHz subcarrier spacing

Cell Layout3 sites, 3 sectors per site166 meters cell radius,hexagonal cell grid

BS AntennaCross poles, 3GPP 36.873 antenna pattern,8x8 elements, mechanical tilt 7.5°

Beam Layout 4x8 beamsUE Antenna One omni cross pole (2 RX)Scenario SCM with Urban Macro

Physical Channel ModelingDetailed PDSCH,ideal PDCCH and uplink

Maximum BS Transmit Power 1.3 W

Beam SelectionAccording to algorithm in Section 4.6.1,beam sweeps every 40 slots

SchedulingRound robin,at most 1 PDSCH user per slot

CSI Mode (Link Adaptation)CSI-RS configured as aperiodicrequested every 20 slots if user has data

PDCCH Overhead 2 symbolsMultipath Speed 0.01 m/sModulation Schemes QPSK, 16QAM, 64QAM and 256QAMTraffic Model Simplified video model, see Section 4.3Simulation Time 10 seconds

The benefits of using the simplified video model are twofold. The removal ofbuffers makes it work more like a live stream or non-multicast TV than a bufferedmotion picture, placing additional demands on the network. The removal ofrate adaptation improves simulation times while also allowing simulations withvariable the cell load. With rate adaptation, there would be no way to haveconfigurable traffic loads on the cells without changing the number of concurrentusers.

4.3.1 Three Traffic levels

The expected use-case of the Internet in the future is mainly downloading and/orstreaming video. A significant load during peak hour usage can be expected.Imagine a suburb or an urban neighborhood in the evening, everyone has comehome from work and decide to watch a live stream or linear TV through theirFWA connection.

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It is for this peak hour scenario the network should be dimensioned for.Therefore, it is also where the optimizations should be made. As RAN per-formance decreases with load, it becomes unattractive to have the network at100% resource usage on the PDSCH [4]. Three traffic models are developedto simulate the network at different loads. The traffic models are developedassuming a good radio environment, all antennas located outdoors with LOS tothe BS.

Each of the traffic settings will be detailed below and a summary can befound in Table 8. A ’high’ load is set to about 80%. This leads to the firsttraffic scenario which is called high traffic from here on.

Table 8: The different traffic settings for the simplified video model. Eachnumber on the FTP Rate setting denotes a separate FTP stream.

SettingVideo Rate

(Mbps)FTP Rate

(Mbps)low 0.6 N/Amedium 3 N/Ahigh 5 3.00, 1.25 and 0.75

The high traffic model demands 5 Megabits per second of video traffic whilesimultaneously downloading files through three FTP-streams. The 5 Megabitsper second video is taken from Netflix’s recommendations for high definitionvideo streaming [21]. Both the video segments and the file downloads are re-quested each second. Each of the FTP downloads are of different sizes, withthe largest being 3 Megabits, the next at 1.25 Megabits and the smallest being0.75 Megabits.

The problem with using File Transfer Protocol (FTP) traffic is the protocol’suse of TCP, which is really good at utilizing the available network resources.This poses a problem when trying to control the network load. Therefore, noFTP traffic will be used for the medium and low settings. The resource usageof each traffic model from a simulation can be found in Figure 8.

The medium settings uses Netflix recommendations for SD-video, which is3 Megabits per second [21]. Because of the reasons discussed above, no FTPstreams will be used. The same restrictions on FTP traffic applies to low set-tings. The video rate was chosen as 0.6 megabits per second to keep the resourceusage at the desired level.

4.4 Evaluating the FWA Deployment

The simulator is configured with the parameters detailed in Table 7 and thetraffic levels varied between each simulation according to Table 8. Three differ-ent LOS settings are tested with both indoor and outdoor CPE for a total of 6settings to compare against each other. The LOS settings are the following:

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Figure 8: The resource utilization at different traffic loads, assuming outdoorCPE and LOS to the BS

• User has Line of Sight (LOS) to the BS

• User has No Line of Sight (NLOS) to the BS

• LOS/NLOS is determined by SCM [18, Sec. 7.4.2].

The simulations will not only be evaluated by the KPIs defined in Section 4.2,but also on these additional criteria:

1. Number of transmissions per user (i.e. how many RBs are consumed toprovide service to the user)

2. The RSRP of the selected cell, i.e. the general coverage at the user’sposition

3. The distribution of modulations between all transmissions.

4.5 CSI-RS for Link Adaptation Optimization

It has been shown in [11] that correct CSI acquisition is important for userperformance. This becomes especially important with narrow beams and highuser mobility, as users experience changes in their radio environment due tomovement.

FWA can be expected to be deployed in the millimeter wave bands, thusrequiring beamforming. However, user mobility can be expected to be very

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close to zero. Therefore, the channel conditions may be assumed to remainrelatively stable.

Channel conditions may still be very different between users. Some usersmay have a lot of interference from neighboring cells and non-FWA users, whileothers may experience difficult LOS conditions and bad multipath environments.Those users are likely to require faster updates of CSI than those with almostperfect conditions.

The above implies that a single transmission frequency scheme is unlikelyto be optimal for all users. Therefore, algorithms that attempts to adapt theCSI-RS transmission rate for each user will be presented below.

4.5.1 CQI-based Linear Decrease

Instead of doing a decrease of CSI-RS transmissions for all users, it could bebeneficial to allow users with good channel conditions to receive CSI-RS less fre-quently than those worse off. One such measure of experienced channel qualityis the reported Channel-Quality Indicator (CQI). The CQI is a number rangingfrom 0 to 15, giving a measure of the experienced channel conditions. The CQIcan be reported either on each individual sub-band or the average across the en-tire wideband [16, Sec. 5.2.1.1]. This algorithm will only consider the widebandCQI.

A simple linear algorithm is developed for using the CQI as the way todetermine the appropriate CSI-RS transmission frequency. The full details ofthe algorithm can be found in Algorithm 1.

Algorithm 1 Linear CSI-RS Periodicity by CQI

Input: maxCsiRsPeriodicity, minCsiRsPeriodicity, where 640 ≥maxCsiRsPeriodicity > minCsiRsPeriodicity ≥ 4

Input: cqiHistory as a list of historical CQI-values for the current userInput: maximumCqiHistorySize is the required size of the CQI-history and

must be greater than 0Input: cqiLevels is the number of discrete CQI-levels and no member ofcqiHistory is greater than cqiLevels

Output: interval is the calculated offset in slots until the next CSI-RS trans-missionif |cqiHistory| ≤ maximumCqiHistorySize then

return defaultCsiRsPeriodicityend ifinterval← avg(cqiHistory) ·maxCsiRsPeriodicity/cqiLevelsif interval < minCqiHistorySize then

period← minCqiHistorySizeend if

Algorithm 1’s run time is linear with the respect to each user’s CQI-history.This allows for a time complexity of Θ (|cqiHistory|), and a maximum timecomplexity of Θ (maxCqiHistorySize). Initially, maxCqiHistorySize is set to

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5. Regarding the performance of Algorithm 1, it will never have a lower overheadthan fixed-rate CSI-RS transmissions with the same maxCsiRsPeriodicity, asit would have a higher overhead unless all users have a CQI of 15.

4.5.2 CQI-based AIMD Algorithm

While the linear algorithm may provide improvements, it has some potentialflaws. For instance, at high maxCsiRsPeriodicity even users with poor qualitywill have long times between CSI-RS transmissions due to the interval beingevenly divided between CQI values.

An Additive Increase / Multiplicative Decrease (AIMD) algorithm is pro-posed here instead. The main idea is to try reducing the transmission frequencyof CSI-RS by a small amount each time. If no CQI decrease is detected, the inter-val between transmissions will just keep increasing up to maxCsiRsPeriodicity.When a CQI lower than the highest recorded in the user’s history is encountered,the CSI-RS transmission rate is reduced by a pre-set factor.

The complete algorithm is detailed in Algorithm 2. The additive increaseupStep is arbitrarily chosen as 4 and the multiplicative decrease downFactor ischosen as 0.5 with a cqiHistory of 500 values and a tolerance of 0.

Algorithm 2 has the same asymptotic complexity per user as Algorithm 1, asboth depend on the size of cqiHistory. However, the history size will generallybe higher for the AIMD algorithm, leading to higher amount of processing atthe BS.

4.5.3 Heuristic Table-based Algorithm

This algorithm takes a different approach than the others by not calculating theappropriate CSI-RS intervals on the fly. Instead, the best CSI-RS periodicityfor each CQI from the monotonic baseline transmission scheme is recorded intoa table before the simulation starts. This is done by looking at the average ofthe results of 13 previous simulations. The entry for each CQI level is populatedby the median CSI-RS interval for users with that CQI level as its highest. Abase station could use user statistics to build its own continually updated tableif this algorithm is to be implemented in a real system.

Once the table is populated, the current CQI is looked up in the table and thepre-recorded interval is chosen for the next CSI-RS transmission. The chosentable can be found in Table 9. Unlike other algorithms, there is no difference intransmissions using the table depending on the maximum transmission interval.

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Algorithm 2 AIMD algorithm for CSI-RS Periodicity

Input: 640 ≥ maxCsiRsPeriodicity > minCsiRsPeriodicity ≥ 4Input: cqiHistory is a list of historical CQI-values for the current userInput: maximumCqiHistorySize is the required size of the CQI-history and

must be greater than 0Input: cqiLevels is the number of discrete CQI-levels and no member ofcqiHistory is greater than cqiLevels

Input: upStep is the upwards step taken when no CQI-decrease is detected,upStep should be a integer value greater than 0

Input: downFactor the factor to multiply the periodicity with when CQI be-comes worse. downFactor must be between 0 and 1

Input: tolerance is the CQI change tolerance between samples beforedownFactor is applied. tolerance should be greater than 0

Input: currentCqi the latest CQI sampleOutput: interval is the calculated offset in slots until the next CSI-RS trans-

missionif |cqiHistory| ≤ maximumCqiHistorySize then

return defaultCsiRsPeriodicityend ifif max(cqiHistory)− currentCqi > tolerance then

period← period · downFactorelse

period← period+ upStepend ifperiod← avg(cqiHistory) ·maxCsiRsPeriodicity/cqiLevelsif period < minCqiHistorySize then

period← minCqiHistorySizeend if

4.5.4 Simulations and Evaluation

Evaluation of these algorithms is done using the simulation parameters in Ta-ble 7. The changes are that the maximum interval between CSI-RS transmis-sions is varied in the simulator, but the traffic is fixed to a high setting. Themaximum transmission interval will not be varied for the table-based algorithmintroduced in Section 4.5.3, since it has no effect on the transmission rate of thealgorithm. Each of the algorithms detailed above will be implemented in thesimulator and run side-by-side, using the same random seeds. The algorithmsare then evaluated against each other using the KPIs defined in Section 4.2.

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Table 9: The CSI-RS transmission intervals selected for the table-based algo-rithm.

CQI CSI-RS Interval1 102 103 104 105 106 107 108 109 10

10 2011 2012 2013 2014 2015 20

4.6 CSI-RS for Beam Management Optimization

Currently, it is specified that a base station should transmit reference signals,such as SSB or CSI-RS, from different beams for users to measure and reporton. This behavior is known as beam sweeping, where users get a chance toadjust the beam best suited for them. Each user is allowed to report back thestate of up to four beams at each reporting occasion. [15, p. 244]

Which beams should be sent to the user at each occasion is not specified bythe standard. Instead, it is up to the BS to determine how beams should besent to each user. How the default sweeping setting works for these simulationswill be detailed in Section 4.6.1.

Testing beams in this predetermined pattern has its benefits, especially formobile users. However, that might not be the case for FWA users. Beingcompletely stationary, FWA users are hypothetically very likely to stick to onlya couple of beams.

Having a small set of good candidate beams could limit the amount of beamsneeded in the sweeping procedures. Lower overhead without a performancepenalty could likely be obtained by sweeping only that subset of beams, sinceno ’unnecessary’ sweeps would be performed. Having that smaller subset ofcandidate beams would also likely improve performance at comparable sweepintervals, since the user would likely find the optimal beam sooner, allowingthe user to maintain good performance if there was to be any changes in theenvironment.

Changes to the physical environment, interference from other users and cells,along with other changes to fading or multipath properties are probably the most

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likely reasons for beam changes. The changes stated above are not expected tobe minimal. Looking at Figure 9, the received power of a user’s selected beamvaries a lot over time, depending on the simulated multipath speed. Since usersare expected to remain stationary, a relatively low multipath speed of 0.01 me-ters per second is selected. Even under the assumption of a slow multipathspeed, changes in the environment are likely to prove troublesome when opti-mizing under the assumption of a static radio environment, despite a small setof beams likely to be optimal at any given time.

Figure 9: The changes in the RSRP of the selected beam over time for a singleuser during one simulation.

A few algorithms are tested for FWA users given the assumption that theradio channel remain relatively static. Because of the set of ”useful” beamsmay be assumed to be small, a cache is used as a basis for all algorithms. First,the default sweeping algorithm is detailed. Then the concept of caching beamsselected by users is discussed. Finally, two additional algorithms which combinesaspects of both the cached algorithm and the default algorithm is detailed.

4.6.1 The Default Beam Sweeping Algorithm

The baseline algorithm, which the other algorithms are based on, works by firstdividing the beam grid into four equally sized quadrants. Then, during eachsweep occasion, up to one beam from each quadrant is selected along with theuser’s currently selected beam. The beams from each quadrants are selected in apre-determined and repeating manner. The users then reports back the resultsto the BS. The beams in each quadrant is chosen according to a predetermined

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pattern. If the user’s currently selected beam is among the four chosen by thealgorithm, it is not transmitted twice.

The algorithm detailed above leads to four or five beams being tested duringeach sweep occasion. As mentioned in Section 3.2.2, CSI-RS for beam sweepingcannot be multiplexed with data. This means that one symbol is effectivelyconsumed by each tested beam.

4.6.2 Cached Beams Only Algorithm

This algorithm relies on the assumption that the set of beams selected by aFWA user are expected to be relatively small due to the stationary natureof such users. The changes to the radio environment is expected to come frominterference by other cells and users along with changes to the local environment.

A cache of ”known good beams” is implemented to take advantage of thisassumption. First, a simulation is performed using the baseline algorithm with ahigh sweep frequency (every 10 slots). Once the simulation has finished, the bestbeam at the end of that simulation is saved at the BS along with all previouslyselected beams for each user. The above is repeated before each simulation,using the same random seed to ensure that a poorly made calibration won’taffect the end result.

Only the beams in the cache are considered for future beam sweeps, to amaximum of five beams transmitted at once (including the currently selectedbeam). This implies the same sweeping overhead as the baseline algorithm inthe worst case. However, since the set of candidate beams is much smaller,the odds of finding a better beam than the current should be higher than thebaseline during any one sweep occasion. In theory, the user should respondfaster to changes in channel conditions, thus experiencing a small performancegain.

4.6.3 Alternating Default / Cached Beam Sweeps

This algorithm combines aspects of both the cached only algorithm and thedefault one. Instead of only sweeping beams found in the cache, this algorithmalternates between the two schemes between each sweep occasion. Because ofthis, the alternating algorithm is expected to perform better than the defaultduring simulations. In other situations, it should perform better than the cachedalgorithm in order to account for situations where the best beam might not bein the cache.

4.6.4 Hybrid Algorithm

Similar to the alternating algorithm, this algorithm combines aspects of boththe default algorithm and the cached algorithm. What makes this algorithmdifferent from the alternating default / cached beam sweep algorithm is howthe sweeping occurs. Instead of sending sweeps for cached beams and otherbeams at different occasions, this algorithm sends two cached beams, along with

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two beams from different quadrants defined in the default algorithm, and thecurrently selected beam at each occasion. Five beams will always be tested ineach occasion, as any duplicates of the currently selected beam will be replacedby one from the regular sweep order.

This algorithm should have properties similar to that of the alternatingdefault / cached algorithm. However, it might behave a bit differently since ittransmits from both schemes at each occasion. Due to the algorithm’s propertyof always testing five beams, the overhead should be higher than all the otheralgorithms.

4.6.5 Simulations and Evaluation

The simulator is set up so that the beam sweeping interval is varied betweeneach simulation, but otherwise uses the parameters detailed in Table 7. A highload will be selected for these simulations. Each of the algorithms detailed abovewill be implemented in the simulator and be run with the same random seeds.Then the algorithms will be compared against each other according to the KPIsdefined in Section 4.2.

According to the hypothesis in the beginning of Section 4.6, changes in signalstrength of the beams over time are expected. Therefore, the best beam for someusers should change over time. The cached algorithms should therefore exhibitbetter performance than the default, since users with a cache should find theoptimal beam faster than those without.

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5 Results

5.1 Performance of Different FWA Deployments

This section covers various deployment aspects of Fixed Wireless Access. Theimpact of CPE placement and LOS settings on different requested traffic loadsis presented and evaluated.

Three different scenarios are set up for both indoor and outdoor CPEs, fea-turing the propagation settings described in Section 4.4. Signal to Interferenceand Noise Ratio (SINR), cell throughput, and user goodput are the importantKey Performance Indicator, as discussed in Section 4.2. The difference in SINRbetween each deployment can be seen in Figure 10. The rather drastic differencein SINR between indoor and outdoor CPEs should be noted.

Figure 10: SINR comparisons between all configurations with a high traffic load.Note the difference between indoor and outdoor users.

The experienced video rates for each user is similar between each deploymentwith requested traffic of 0.6 megabits per second, as seen in Figure 11. Outdoorusers are seen performing better than indoor users for all requested video rates.The difference in rates diverges as the requested load increase. Simulations notfeaturing outdoor CPEs with LOS settings set to full LOS or as determined bySCM displays only a modest increase, or a decrease, in average video rates asthe requested rate increased.

Used modulation schemes also differs vastly between the deployments. Asseen in Figure 12, worse channel conditions leads to the use of a more robust

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Figure 11: The average video bitrate of all users with different deployments.

modulation scheme. This leads to fewer bits being encoded on each OFDMsymbol. Fewer bits per symbol implies a higher amount of required symbols,thus radio resources, for each downlink transmission.

Serving the users who cannot maintain a high modulation scheme thus con-sumes additional resources, as discussed above. The difference between user andcell throughputs for different configurations can be found in Table 10. The figureclearly shows how a good deployment increases both user and cell throughput.It should be noted that each video segment is sent over approximately 400 mil-liseconds every second. This limits the throughput to 2.5 times the requestedrate.

Table 10: The average video rates and cell throughputs for different deploy-ments.

Throughput (Mbps)Deployment Low Medium High

User Cell User Cell User CellIndoor LOS 1.1 9.0 3.6 20.7 3.4 33.5Indoor NLOS 0.3 2.7 1.1 6.1 1.0 10.0Indoor SCM 0.4 3.5 1.6 8.6 1.9 15.9Outdoor LOS 1.5 10.7 7.3 28.0 8.4 71.5Outdoor NLOS 1.5 10.7 5.8 27.9 6.0 53.3Outdoor SCM 1.5 10.7 6.2 28.0 7.2 59.1

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Figure 12: The modulation schemes used in the deployments for the 3 Mbpsvideo traffic load.

Additionally, the cost of serving users with poor coverage increases. This isdisplayed in Figure 13 for indoor users with LOS to the BS. Looking at Table 10and Figure 13, it clearly shows that the resource usage remains high for the userswith indoor CPEs, while the throughput is low.

To make matters worse, not all users are able to connect to the BS. Due topoor signal environment, some users with indoor CPEs are completely unableto receive data on the network. The percentages of users without reception ispresented in Figure 14. Notice that no users with outdoor CPEs are withoutreception. This also results in the sub-band utilization becoming lower, as somecells will be left empty when fewer users are able to connect at all. The describedbehavior is shown in Figure 15 and explains the low resource usage of indoorNLOS and SCM users in Figure 13.

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Figure 13: The radio resource (sub-band) utilization for different deployments.

Figure 14: The percentage of users unable to receive any data on the networkusing the different deployments.

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Figure 15: The radio resource (sub-band) utilization for the 10%-tile of users.

As shown in Figure 16, the amount of users reaching the requested bitratedecreases as the traffic load increases. The amount of users able to maintaineven 0.6 Mbps video becomes small with indoor CPEs, where not even 25% ofusers can achieve a steady stream of 0.6 Mbps live video. Outdoor users farebetter, readily achieving the requested rates for low and medium traffic. It isonly at high traffic where where the network is under sufficient load for outdoorusers to experience a noticeable performance impact.

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Figure 16: The percentage of users reaching 95% of the requested bit rate usingthe different deployments.

The low video rates of users with indoor CPEs becomes evident by lookingat the coverage map in Figure 18 and comparing it with Figure 17. It clearlyshows how indoor losses become quite significant with a 28 GHz deployment.Looking at the average number of HARQ transmissions per user in Figure 19,indoor users are found to require more transmissions than outdoor users.

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Figure 17: Example coverage map of outdoor CPEs using SCM. The coveragemap is based on the measured RSRP in dB.

Differences between RSRP of indoor and outdoor users can be found inFigure 20. Here, it is shown that the worst performing users with outdoor CPE,i.e. users with NLOS to the BS, still out-perform over 30% of users with indoorCPE and direct LOS.

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Figure 18: Example coverage map of indoor CPEs using SCM. The coveragemap is based on the measured RSRP in dB.

Additionally, it becomes clear from Figure 16 that very few users are unableto get the requested 5 megabits per second when they have LOS to the BS.However, it might be too optimistic to hope that all users will be able to maintainLOS at all times. Therefore, outdoor CPEs will be used with SCM determiningtheir LOS when evaluating the algorithms described in Section 4. Having theLOS settings determined by SCM will also put the network under sufficient stressto ensure not all users will receive the requested bitrates, making improvementsmade by the algorithms more pronounced.

Given the results shown above, it is shown that outdoor CPE becomes anecessity for basic coverage. The large performance effects of LOS conditionsmotivates careful antenna placements for FWA deployments.

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Figure 19: Number of transmissions per user depending on CPE location andLOS settings.

Figure 20: CDF over measured RSRP during simulations of different CPE lo-cations and LOS settings.

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5.2 CSI-RS for Link Adaptation

Each of the algorithms detailed in Section 4.5 are tested in simulations, usingthe simulation parameters listed in Table 7. The maximum interval betweenCSI-RS transmissions is varied between each simulation. The algorithms arethen compared to the baseline scenario where the CSI-RS for link adaptationis transmitted constantly at the maximum interval set for that simulation. Atotal of 18 simulations with different random seeds are averaged to produce theresults within this section. The interval surrounding each point shows the 95%confidence interval, assuming a Gaussian distribution of simulation results.

Considering the total cell throughput detailed in Figure 21, all algorithmsout-perform fixed-rate transmissions as the maximum transmission interval in-creases. The cell-level performance difference between the algorithms is minimalfor short CSI-RS transmission intervals. Both the linear and AIMD algorithmdisplays improvements as the maximum transmission interval increases, for amaximum gain of 4.8% over fixed-rate transmissions. The AIMD algorithmdisplays an optimum for a transmission interval of 160 slots, while the linearalgorithm keeps improving. Fixed-rate transmission of CSI-RS sees a clear per-formance degradation for higher intervals. The table-based algorithm is onlyrepresented by a single point in Figures 21 - 29, as the maximum transmissioninterval does not affect how it transmits CSI-RS.

Figure 21: Average cell throughput during high traffic load for different CSI-RStransmission schemes.

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Figure 22 shows the average SINR also improves with the AIMD algorithmcompared with the others. Once again, the maximum is found for an intervalof 160 slots with the AIMD algorithm. While the gain remains at only 0.5 dB,it still a clear improvement. As seen in Figure 22, there’s no clear benefit toSINR using the linear algorithm over fixed-rate transmissions for an intervalof 320 slots. Intervals shorter than 50 slots shows no clear winner among thealgorithms. Since SSB transmissions takes place every 20 milliseconds, or 160slots, it may possibly reduce the performance of fixed-rate transmissions at thesame interval due to scheduling. The table-based algorithm performed the worst(by a small amount) in the SINR measurements.

Figure 22: Average user SINR during high traffic load between different CSI-RSfor link adaptation transmission algorithms.

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The video rates for the average user remains high for both the baseline sce-nario and all algorithms when the maximum transmission interval is at 50 slotsand below. The AIMD algorithm has the highest reported performance over allsimulations with a throughput of above 6 Megabits per second and a CSI-RS in-terval of 160 slots as shown in Figure 23. The table-based and linear algorithmsperforms slightly worse at each measurement. Fixed-rate transmissions displaysa downward trend similar to the cell throughput measurements in Figure 21.

Figure 23: Average video rates for all CSI-RS transmission algorithms on a hightraffic load.

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Things look the same for users with the worst performance. Figure 24 de-tails the bit rate for the 10th percentile of users, where similar trends can beobserved as for the average user. However, the differences between algorithmsare marginal for these users. The benefit for users in poor coverage, but notnecessarily in the 10th percentile, is further exemplified in Figure 25, where itshows that a higher amount of users are able to reach 95% of their requestedvideo rates when using one of the algorithms. Fixed-rate transmissions willhave 58% satisfied users at the largest interval due to the very high load onthe network, but the AIMD algorithm show improvement and allows 62% ofusers to reach the target bitrate. The linear algorithm performs similarly to theAIMD algorithm at the highest interval, with both algorithms outperformingthe baseline and table-based algorithms.

Figure 24: Experienced video rates for the users in the 10th percentile usingdifferent CSI-RS transmission algorithms.

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Figure 25: Percent of users achieving 95% of requested video rate during hightraffic load (see Table 8) using different CSI-RS transmission algorithms.

FTP rates behave slightly differently than video rates for different CSI-RSintervals as seen in Figures 26 and 27. No algorithm provides any substantialbenefits over the over other for the lowest transmission intervals. The AIMDalgorithm starts performing better than the others as the transmission intervalincreases, as seen in Figure 26. Fixed-rate transmission and the linear algorithmfollows similar trends as the AIMD algorithm, but performs slightly worse. Thesmall dip in bit rates for fixed-rate transmissions at 160 slots may be causedby the SSB transmissions scheduled at the same interval. The table-based al-gorithm performs about the same as fixed-rate transmissions at the highestsweeping interval for both the average and 10th percentile of users. Using theAIMD algorithm also shows a modest gain for the users with the worst coverage.

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Figure 26: Comparison of average FTP rates between different CSI-RS trans-mission algorithms.

Finally, Figure 28 shows the total overhead of CSI-RS transmissions for linkadaptation purposes. Here it can be seen that no algorithm has a lower overheadthan fixed-rate transmissions with the same maximum CSI-RS interval. Thisis evident from the design of the algorithms, allowing more frequent CSI-RStransmissions for users with worse channel conditions.

However, the AIMD algorithm can be seen as utilizing the CSI-RS transmis-sions the best. As seen in Figure 28, simulations using fixed-rate transmissionsof CSI-RS has an overhead of about 0.18% when the maximum transmissioninterval is set to 50 slots. For the best performing algorithm, the overheadis also about 0.18%, but at a maximum interval of 320 slots between CSI-RStransmissions.

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Figure 27: Comparison of FTP rates for users in the 10th percentile betweendifferent CSI-RS transmission algorithms.

Figure 28: Total overhead of CSI-RS for link adaptation.

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As seen in Figure 29, the AIMD algorithm has a goodput of over 9 megabitsper second at 320 slots between CSI-RS transmissions, while the baseline hasa goodput of only 8.4 megabits per second at 50 slots between transmissions.This results in a 7% increase in performance at the same overhead.

Figure 29: Average user goodput of both FTP and video traffic for all CSI-RSfor link adaptation algorithms on a high traffic load.

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5.3 CSI-RS for Beam Management

The algorithms found in Section 4.6 are simulated using the parameters foundin Table 7, varying the beam sweep interval between simulations. A total of 23simulations are averaged for each data point in Figures 30 - 36. The bars onFigures 30 - 36 displays the 95% confidence interval of the value, assuming aGaussian distribution of simulation results.

Simulations show that beam selection algorithms introduced in Section 4.6have minor effects on performance. KPIs such as user goodput exhibits a differ-ence of 1.47% in performance with the algorithms, as can be seen in Figure 30.Only a marginal improvement is observed by sweeping only cached beams at thesmallest sweeping interval, with no clear winner among algorithms for higherintervals. The only observable trend is that using any of the algorithms yieldsa minimal improvement over the default.

Figure 30: Average user goodput between both video and FTP for the userswith a high traffic load and alternating sweep intervals.

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The same trend as user goodput is also displayed in Figure 31 with FTPperformance. Any of the algorithms shows about the same small improvementin the range of 1.5% over the default, with performance being slightly erraticfor the hybrid algorithm. No cached algorithm was significantly better than theother for this metric.

Figure 31: Average FTP rates for the users with a high traffic load for differentsweep intervals.

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Cell throughput also improves by a marginal amount as seen in Figure 32.Sweeping only cached values provided a benefit for the system-level throughputat smaller sweeping intervals, the other algorithms performing worse but con-verging as the interval increases. The default algorithm shows a slight downwardtrend with larger intervals not observed with the other algorithms, performingabout 1% worse at an interval of 1080 slots.

Figure 32: Average cell throughput during high traffic load for different sweepintervals.

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Some very modest gains can also be found with SINR as seen in Figure 33,where a downward trend is observed for the default algorithm but not in theothers. However, the gains remain under 2%. No cached algorithm demonstratesany gains over the others.

Figure 33: Average downlink SINR with different beam sweeping algorithms.

Video rates for the average users seen in Figure 34 seem to show no significantimprovements regardless of chosen algorithm. Sweeping only the cached beamsgives an insignificant improvement for the lowest intervals, but all algorithmshave converged at an interval of 480 slots. No cached algorithm significantlyincreases the percentage of users reaching 95% of the requested video rates bymore than about 1.5% at the highest sweep interval, which is one more satisfieduser at best with the chosen deployment. While no cached algorithm performsbetter than the other, a downward trend could be found for the default algorithmin Figure 35.

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Figure 34: Average video rates for the users with a high traffic load for differentbeam sweeping algorithms.

There is no significant reduction in overhead from using most of these al-gorithms, each algorithm following the same trends as the default. The hybridalgorithm always has a slightly higher overhead than the default algorithm, asseen in Figure 36. This is because the default algorithm may elect to only sendfour beams if the currently selected beam would be chosen by the sweep order,while the hybrid will always send five beams. The cached and alternating algo-rithms may have a cache smaller than four beams. In that case the entire cachewill be swept at each occasion, leading to a lower overhead. Other algorithmsalways kept a marginally smaller overhead for each sweep interval, the differencebecoming inconsequentially small at the longest intervals.

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Figure 35: Percentage of users reaching 95% of the requested bit rate for differentsweeping intervals. Simulations are done using high traffic (see Table 8).

Figure 36: Overhead of CSI-RS transmissions for beam management.

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It can be concluded that very minor gains are made using the caching al-gorithms. The small improvements shown by the results are not large enoughto be considered significant. Looking at the transmitted beam grid depicted inFigure 37, it can also be shown that several beams are very close in averagesignal strength, likely minimizing the importance of quickly adapting to smallchanges to the environment.

Figure 37: Average RSRP of each beam in the base station’s beam grid for oneuser.

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6 Conclusions

The 5G Radio Access Network is investigated in order to determine which partscould be optimized for Fixed Wireless Access (FWA) deployments in the 28 GHzband. More specifically, each reference signal is first considered and its overheadanalytically determined. Optimizations are proposed and then evaluated in astate-of-the-art system-level simulator.

The initial investigation concludes with the expected overheads of each ofthe layer 1 signals and is presented in Table 1. It is found that while there mightbe room for optimizations of Demodulation Reference Signal, Phase-TrackingReference Signal and Tracking Reference Signal, there isn’t a good way to modelthe effects of reducing or removing those signals in the simulator. Therefore,those signals are not considered for optimizations.

Additionally, the overhead of the Physical Downlink Control Channel isinvestigated. The overhead is found to be relatively low at 0.16% of transmittedresource blocks for the expected bandwidths of FWA deployed in the 28 GHzband.

The synchronization signals are found to be unmodifiable, as any changes tothose signals would give non-FWA users difficulties connecting to the network.This limits the thesis to focus on the Channel State Information Reference Signal(CSI-RS).

The CSI-RS has two main uses in NR: link adaptation and beam manage-ment. Algorithms for both cases are proposed in Section 4 along with simulationparameters for a FWA deployment.

Deployment aspects of FWA are considered, such as indoor and outdoorCustomer Premises Equipment (CPE), along with different line of sight prop-erties to the base station. Each of these deployments are tested and evaluatedin the simulator to test their performance characteristics.

It is concluded that outdoor CPE is vital for coverage and performance inthe 28 GHz band. Line of sight to the base station also has a large impact onperformance, like earlier studies have shown. Outdoor CPE and Line of Sightproperties as decided by the Spatial Channel Model is chosen for evaluation ofthe algorithms.

Algorithms utilizing FWA properties for transmission of CSI-RS for bothbeam management and link adaptation are introduced. The algorithms aresimulated and compared against each other and the baseline.

Simulations show that optimizing the beam sweeping procedure by imple-menting a cache results in marginal performance gains. A user could haveroughly the same reception power from many beams at once, minimizing thegains of quickly adapting to changes due to environmental factors.

Optimizations of CSI-RS for link adaptation transmissions show improve-ments over the default with the Additive Increase / Multiplicative Decrease(AIMD) algorithm. The total performance gains with the AIMD algorithm is7% over the baseline at the same overhead.

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7 Future Work

The parameters used in the algorithms presented in this thesis were selectedas ”reasonable defaults”. Here, further work could be made to optimize thealgorithm parameters, especially those for the AIMD algorithm used for CSI-RS optimizations. The algorithm currently uses a large history size. As thecomputation time increases with the size of the saved history of CQI values,reducing it might save some precious resources.

While presented in Appendix A, the concept of either compressing or oth-erwise reducing PDCCH transmissions could give some performance improve-ments. However, as the investigation in the appendix concludes, the potentialgains would likely fall below 1%, even if the PDCCH would require many ControlChannel Elements (CCEs) per occasion.

While caching a set of ”known good beams” proved ineffective when selectingwhich beams to use for sweeping during a user session with the selected intervals,caching beams might still have a use for FWA. Beams may sometimes fail, theycould get blocked or otherwise become unsuitable for a user. Having a listof previously selected beams might make it possible to speed up the recoveryprocedure. Since a small downward trend was observed for sweeping withoutthe cache, but not using any of the algorithms, it might be worth investigatingthe use of a cache for sweeping intervals much greater than 1000 slots.

This thesis only touched on the physical layer of the NR RAN, where thedifferences between regular use and FWA deployments might be considered thegreatest. However, that doesn’t mean there is nothing to be done at higherlayers. It could be possible to adapt TCP congestion windows or some otherhigher-layer parameter to improve performance in the FWA use-case.

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References

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_rev27Nov2018.xls, 11 2018. Accessed: 2019-05-10.

[2] Cisco, “Cisco visual networking index: Forecast and trends, 2017–2022,”tech. rep., Cisco, 170 West Tasman Drive, San Jose, CA 95134-1706, UnitedStates, February 2019.

[3] W. Xie, N. Mao, and K. Rundberget, “Cost comparisons of backhaul trans-port technologies for 5g fixed wireless access,” in 2018 IEEE 5G WorldForum (5GWF), pp. 159–163, July 2018.

[4] Ericsson, “Fixed wireless access handbook extracted version.” https:

//www.ericsson.com/en/portfolio/networks/network-solutions/

fixed-wireless-access, 2018. Accessed: 2019-05-14.

[5] B. B laszczyszyn, M. Jovanovicy, and M. K. Karray, “How user throughputdepends on the traffic demand in large cellular networks,” in 2014 12thInternational Symposium on Modeling and Optimization in Mobile, Ad Hoc,and Wireless Networks (WiOpt), pp. 611–619, May 2014.

[6] 3GPP, “NR; Physical channels and modulation (Release 15),” TechnicalSpecification (TS) 38.211, 3rd Generation Partnership Project (3GPP), 032019. Version 15.5.0.

[7] E. Oproiu, I. Gimiga, and I. Marghescu, “5g fixed wireless access-mobile op-erator perspective,” in 2018 International Conference on Communications(COMM), pp. 357–360, June 2018.

[8] D. Soldani, P. Airas, T. Hoglund, H. Rasanen, and D. Debrecht, “5g to thehome,” in 2017 IEEE 85th Vehicular Technology Conference (VTC Spring),pp. 1–5, June 2017.

[9] B. Skubic, M. Fiorani, S. Tombaz, A. Furuskar, J. Martensson, andP. Monti, “Optical transport solutions for 5g fixed wireless access [invited],”IEEE/OSA Journal of Optical Communications and Networking, vol. 9,pp. D10–D18, Sep. 2017.

[10] D. Ha, D. Choi, H. Kim, J. Kum, J. Lee, and Y. Lee, “Passive repeaterfor removal of blind spot in nlos path for 5g fixed wireless access (fwa)system,” in 2017 IEEE International Symposium on Antennas and Propa-gation USNC/URSI National Radio Science Meeting, pp. 2049–2050, July2017.

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[11] E. Onggosanusi, M. S. Rahman, L. Guo, Y. Kwak, H. Noh, Y. Kim,S. Faxer, M. Harrison, M. Frenne, S. Grant, R. Chen, R. Tamrakar, anda. Q. Gao, “Modular and high-resolution channel state information andbeam management for 5g new radio,” IEEE Communications Magazine,vol. 56, pp. 48–55, March 2018.

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[13] B. Mondal, V. Sergeev, A. Sengupta, and A. Davydov, “5g-nr (new ra-dio) csi computation algorithm and performance,” in 2018 52nd AsilomarConference on Signals, Systems, and Computers, pp. 1068–1071, Oct 2018.

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[15] E. Dahlman, S. Parkvall, and J. Skold, 5G NR: The Next Generation Wire-less Technology. Academic Press, 2018.

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[17] S. Kutty and D. Sen, “Beamforming for millimeter wave communications:An inclusive survey,” IEEE Communications Surveys Tutorials, vol. 18,pp. 949–973, Secondquarter 2016.

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A Overhead of the Physical Downlink ControlChannel

The PDCCH is used to send vital control information to user. Without thiscontrol information, the user would not know when it is allowed to transmit norwhen to expect data reception. [15, p. 183]

However, the PDCCH can have a certain overhead, since this control infor-mation is transmitted in the same resource elements used for data transmission.Needless to say, all such control information cannot be removed. However, theoverhead of PDCCH is still interesting, since it might be possible to reduce itsoverhead by other means, such as improved scheduling.

PDCCH is transmitted in the form of Control Resource Sets, or CORESETs.Each CORESET may take up to three OFDM symbols and can span up to theentire bandwidth, in multiples of six resource blocks. The CORESETs consistsof multiple CCEs. Each CCE in turn consists of six Resource Element Groups(REGs). Then each REG refers to one RB for the duration of one OFDMsymbol. [15, Sec. 10.1.2]

It is through decoding CCEs on a CORESET that a device can retrieve itsPDCCH. The PDCCH can be encoded on 1, 2, 4, 8 or 16 CCEs. The payload ofthe PDCCH is called the Downlink Control Information, or DCI. [15, Sec. 10.1.3]

The DCI then informs the decoder about which OFDM symbols on whichresource blocks it may transmit or receive data. It is possible to address anythingfrom a single resource block to the entire bandwidth during up to one slot withdownlink scheduling DCI. [15, pp. 204–206]

Limitations in the DCI makes it possible to calculate the expected overheadof the downlink. Assuming a best case scenario, it would be impossible toaddress more than the entire bandwidth during one slot for a UE given theDCI. The formula for calculating the PDCCH overhead would be the following:

OPDCCH =6 · nCCEs

nscheduled RBs · nscheduled symbols + 6 · nCCEs, (5)

since there’s always 6 resource blocks in a CCE. It must be that nscheduled RBs ≤BW , where BW is the bandwidth in resource blocks, and nscheduled symbols ≤ 14due to the NR slot structure. Equation 5 assumes there’s no DMRS, TRS,CORESET or other overhead in the scheduled data transmission. Real-lifescenarios would likely see a slightly higher overhead than detailed here.

NR has a maximum bandwidth of 3300 subcarriers or 275 resource blocks[15, Sec. 5.4]. Therefore, best case overhead for scheduling a single slot is ob-tained for selecting nscheduled RBs = 275, nscheduled symbols = 14 and nCCEs = 1.Inserting the values into Equation 5, the best case overhead for PDCCH wouldbe OPDCCH,best-case ≈ 0.16%. In reality, there would be fewer available resourceelements for data transmission within the scheduled blocks, making a 0, 16%overhead optimistic.

Considering the worst case scenario, it could be possible to have very fewOFDM symbols available during a single resource block. As few as three

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symbols would be a realistic scenario. This would give a worst case perfor-mance of OPDCCH,worst-case,3 symbols ≈ 66, 67% for a three symbol transmissionor OPDCCH,worst-case,1 slot = 30% for a full slot transmission, still assuming aCCE size of one REG.

With the large differences between worst-case and best-case scenarios, itclearly shows that proper scheduling of downlink assignments becomes impor-tant for network performance. In the context of FWA, the relatively unchangingnature of the network might allow for specialized scheduling during peak hourtraffic.

Allowing such scheduling for FWA users makes sense. A semi-static downlinkassignment could aid in assuring service quality while also removing some of thePDCCH overhead. One potential issue with semi-static scheduling is under-utilization of network resources. This issue would arise as soon as there isn’tany data to receive during one of these static assignments. Not having any datato receive during an assignment essentially wastes the allocated resources.

The expected case for FWA is to have deployments with large availablebandwidth. Due to the somewhat lower latency requirements of regular internetconnectivity compared to other 5G use cases, it is unlikely that transmissionssmaller than a single slot will be in use. For those situations, the overhead ofPDCCH approaches its lower bound.

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