E-Book
119,99 €

Design and Optimization for 5G Wireless Communications E-Book

Haesik Kim

0,0

119,99 €

Sammeln Sie Punkte in unserem Gutscheinprogramm und kaufen Sie E-Books und Hörbücher mit bis zu 100% Rabatt.

Mehr erfahren.

Herausgeber: John Wiley & Sons
Kategorie: Wissenschaft und neue Technologien
Serie: Wiley - IEEE
Sprache: Englisch

Beschreibung

This book offers a technical background to the design and optimization of wireless communication systems, covering optimization algorithms for wireless and 5G communication systems design. The book introduces the design and optimization systems which target capacity, latency, and connection density; including Enhanced Mobile Broadband Communication (eMBB), Ultra-Reliable and Low Latency Communication (URLL), and Massive Machine Type Communication (mMTC). The book is organized into two distinct parts: Part I, mathematical methods and optimization algorithms for wireless communications are introduced, providing the reader with the required mathematical background. In Part II, 5G communication systems are designed and optimized using the mathematical methods and optimization algorithms.

Details

Sie lesen das E-Book in den Legimi-Apps auf:

Android

iOS

von Legimi
zertifizierten E-Readern

Seitenzahl: 604

Veröffentlichungsjahr: 2020

Bewertungen

0,0

Rezensionen(0 Rezensionen)

Leseprobe

Cover

Preface

List of Abbreviations

Part I: Mathematical Methods and Optimization Theories for Wireless Communications

1 Historical Sketch of Cellular Communications and Networks

1.1 Evolution of Cellular Communications and Networks

1.2 Evolution to 5G Networks

References

2 5G Wireless Communication System Parameters and Requirements

2.1 5G Requirements

2.2 Trade‐off of 5G System Metrics

References

3 Mathematical Methods for Wireless Communications

3.1 Signal Spaces

3.2 Approximation and Estimation in Signal Spaces

3.3 Matrix Factorization

References

4 Mathematical Optimization Techniques for Wireless Communications

4.1 Introduction

4.2 Mathematical Modeling and Optimization Process

4.3 Linear Programming

4.4 Convex Optimization

4.5 Gradient Descent Method

References

5 Machine Learning

5.1 Artificial Intelligence, Machine Learning, and Deep Learning

5.2 Supervised and Unsupervised Learning

5.3 Reinforcement Learning

References

Part II: Design and Optimization for 5G Wireless Communications and Networks

6 Design Principles for 5G Communications and Networks

6.1 New Design Approaches and Key Challenges of 5G Communications and Networks

6.2 5G New Radio

6.3 5G Key Enabling Techniques

References

7 Enhanced Mobile Broadband Communication Systems*

7.1 Introduction

7.2 Design Approaches of eMBB Systems

7.3 MIMO

7.4 5G Multiple Access Techniques

7.5 5G Channel Coding and Modulation

Problems

References

8 Ultra‐Reliable and Low Latency Communication Systems

8.1 Design Approaches of URLLC Systems

8.2 Short Packet Transmission

8.3 Latency Analysis

8.4 Multi‐Access Edge Computing

References

9 Massive Machine Type Communication Systems

9.1 Introduction

9.2 Design Approaches of mMTC Systems

9.3 Robust Optimization

9.4 Power Control and Management

9.5 Wireless Sensor Networks

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Technical specifications of 1G cellular systems.

Table 1.2 Technical specifications of 2G cellular systems.

Table 1.3 Technical specifications of 3G cellular systems.

Table 1.4 Technical specifications of 4G cellular systems.

Chapter 2

Table 2.1 Traffic characteristics for mMTC city scenario [2].

Table 2.2 5G eMBB requirements [1].

Chapter 3

Table p3.1 Measurement data of signal strength, distance from base station (B...

Table p3.2 Joint probability mass function of

and

Chapter 4

Table p4.1 Initial simplex tableau for Example 4.5.

Table p4.2 First selection of pivot column and row.

Table p4.3 Second simplex tableau.

Table p4.4 Third simplex tableau.

Table p4.5 Final simplex tableau.

Table p4.6 Initial simplex tableau for Example 4.6.

Table p4.7 First selection of pivot column and row.

Table p4.8 Simplex tableau 2.

Table p4.9 Second selection of pivot column and row.

Table p4.10 Simplex tableau 3.

Table p4.11 Third selection of pivot column and row.

Table p4.12 Final simplex tableau.

Chapter 5

Table 5.1 Comparison of supervised learning and unsupervised learning.

Table p5.1 The first iteration for Example 5.3.

Table p5.2 The second iteration for Example 5.3.

Table p5.3 The third iteration for Example 5.3.

Table p5.4 The fourth iteration for Example 5.3.

Table p5.5 The first iteration for 5.4.

Table p5.6 The second iteration for 5.4.

Table 5.2 Comparison of TD learning, Q learning, and SARSA.

Chapter 6

Table 6.1 5G spectrum usages.

Table 6.2 5G NR numerology.

Table 6.3 5G NR RB configuration.

Table 6.4 5G NR logical, transport, and physical channels.

Table 6.5 5G NR physical signals.

Table 6.6 5G NR physical layer.

Chapter 7

Table 7.1 Approaches for increasing network throughput.

Table p7.1 OFDM system parameters.

Table p7.2 Simulation configuration for turbo codes.

Table p7.3 Simulation parameters for (8176, 7156) LDPC code.

Chapter 8

Table 8.1 Approaches for reducing the latency.

Table 8.2 User plane latency calculations for 5G and 4G FDD.

Table 8.3 User plane downlink latency analysis with 0% HARQ BLER [13].

Table 8.4 User plane uplink latency analysis with 0% HARQ BLER [13].

Table 8.5 User plane downlink latency analysis with 10% HARQ BLER [13].

Table 8.6 User plane uplink latency analysis with 0% HARQ BLER [13].

Table 8.7 Control plane latency calculation (Steps 1–10) of 4G FDD, based on ...

Table 8.8 Control plane latency calculation (Steps 11–17) of 4G FDD [13].

Table 8.9 Handover latency calculation based on Figure 8.12b [13].

Chapter 9

Table 9.1 Features of 3GPP IoT standards.

Table 9.2 NB‐IoT signals and channels.

Table 9.3 mMTC design approaches.

Table 9.4 Tractable robust counterparts of uncertain linear optimization prob...

Table 9.5 Data fusion techniques.

Table p9.1 Likelihood matrices of two sensors.

Table p9.2 Posterior probabilities of two sensors.

List of Illustrations

Chapter 1

Figure 1.1 Timeline of 3GPP 5G developments.

Figure 1.2 Evolution of cellular systems.

Chapter 2

Figure 2.1 5G requirements [8].

Chapter 3

Figure 3.1 Shannon's communication architecture [1].

Figure p3.1 Geometric interpretation of

−

/2,

/2, and (

−

)/2...

Figure 3.2 Properties of linear transformation: (a) multiplicativity and (b)...

Figure 3.3 Example of projection.

Figure 3.4 Approximation problem in

ℝ

Figure 3.5 Comparison of the variance of the error term

Figure p3.2 Comparison of the linear model and paired data points.

Figure p3.3 Comparison of the linear model and paired data points.

Figure 3.6 System model based on a discrete channel [4].

Figure 3.7 Optimum receiver model using Bayesian formula: (a) transmission a...

Figure p3.4 System model for Example 3.19.

Figure 3.8 ML vs MAP: (a) MAP = ML; and (b) MAP ≠ ML.

Figure 3.9 Concept of Householder transformation.

Chapter 4

Figure 4.1 Quadratic optimization.

Figure 4.2 Examples of possible optimum values when the problem is bounded: ...

Figure 4.3 Examples of possible optimum values when the problem is unbounded...

Figure 4.4 Examples of global and local minima and maxima when the problem i...

Figure p4.1 A simple model of signal‐to‐interference in two base stations.

Figure 4.5 The shaded area shows the feasible region in the linear programmi...

Figure p4.2 The shaded area shows the feasible region in Example 4.3.

Figure p4.3 The shaded area shows the feasible region in Example 4.4.

Figure 4.6 Three different cases: (a) a single optimal solution; (b) an infi...

Figure 4.7 Examples of (a) a convex function

(

)

; and (b) a non‐convex fun...

Figure 4.8 Two approaches to solving optimization problems.

Figure 4.9 Approach of the interior point method.

Figure p4.4 For Example 4.7,

(

) when

= 1, 5, and 20.

Figure p4.5 Iteration of the gradient descent method.

Figure 4.10 Approach of the projected gradient method.

Figure p4.6 Scatterplot of the dataset for Example 4.11.

Figure p4.7 Objective function for Example 4.11.

Figure p4.8 Linear regression by gradient descent method for Example 4.11.

Chapter 5

Figure 5.1 Relationship between AI, machine learning and deep learning.

Figure 5.2 (a) Classification for supervised learning, and (b) clustering fo...

Figure 5.3 Hyperplane to classify training samples.

Figure 5.4 Examples of (a) a linearly separable sample, and (b) and (c) not ...

Figure 5.5 Example of hyperplane decision in multidimensional space.

Figure 5.6 Maximum margin and support vectors.

Figure 5.7 Trade‐off between margin and misclassified points.

Figure 5.8 Example of a non‐linear classification in (a) one dimension and (...

Figure p5.1 Dataset for Example 5.1.

Figure p5.2p5.2 Classifier for the Example 5.1 dataset.

Figure p5.3 Dataset for Example 5.2.

Figure p5.4 Classifier for the Example 5.2 dataset.

Figure 5.9 Examples of clustering using (a) Euclidean distance and (b) nonli...

Figure 5.10 Examples of (a) partitioning clustering and (b) hierarchical clu...

Figure p5.5 The 120 two‐dimensional data points for Example 5.5.

Figure p5.6 The first iteration for Example 5.5.

Figure p5.7 The second iteration for Example 5.5.

Figure p5.8 The third iteration for Example 5.5.

Figure p5.9 The fourth and final iteration for Example 5.5.

Figure 5.11 The interaction process of reinforcement learning.

Figure 5.12 Example of a Markov decision process (MDP).

Figure 5.13 Comparison of Q learning and SARSA [15].

Figure p5.10 Gridworld for Example 5.6.

Figure p5.11 For Example 5.6, state values (a) at (2,4) and (b) after the fi...

Figure p5.12 Gridworld for Example 5.7.

Figure p5.13 Initial condition of

(

)

for Example 5.7.

Figure p5.14 Q value updates when moving right for Example 5.7.

Figure p5.15 Gridworld for 5.8.

Figure p5.16 Optimal path for 5.8.

Figure p5.17 Gridworld example for Problem 5.20.

Chapter 6

Figure 6.1 5G numerology and slot length.

Figure 6.2 5G network slicing architecture.

Figure 6.3 Functional split between NG‐RAN and 5GC.

Figure 6.4 5G NR network architecture.

Figure 6.5 (a) 5G NSA and (b) SA.

Figure 6.6 Examples of (a) 5G NR slots and (b) a flexible configuration.

Figure 6.7 5G NR resource grid and resource blocks.

Figure 6.8 Example of 5G BWPs.

Figure 6.9 5G channel types.

Figure 6.10 5G SS/PBCH block configuration.

Figure 6.11 5G channel mapping for (a) downlink and (b) uplink.

Figure 6.12 5G protocol stack for (a) user plane and (b) control plane.

Figure 6.13 UE state machine and transitions in NR and between NR and E‐UTRA...

Figure 6.14 Example of 5G packet segmentation and reassembly.

Figure 6.15 5G NR physical channel processing for PDSCH.

Figure 6.16 5G NR physical channel processing for PDCCH.

Figure 6.17 Example of PDCCH CORESET.

Figure 6.18 5G NR physical channel processing for PUSCH.

Figure 6.19 5G initial access procedure and beam management.

Figure 6.20 OFDM‐based 5G waveform.

Figure 6.21 Comparison of (a) OMA and (b) NOMA.

Figure 6.22 Polar encoding for 5G NR.

Figure 6.23 LDPC encoding for 5G NR.

Figure 6.24 Antenna array architecture for (a) digital beamforming, (b) anal...

Figure 6.25 3GPP network slicing architecture.

Figure 6.26 Comparison of (a) MEC and (b) fog computing.

Figure 6.27 MEC deployment in a 5G network.

Chapter 7

Figure 7.1 Euler diagram for P, NP, NP complete, and NP hard problems [1].

Figure 7.2 Bandwidth vs throughput.

Figure 7.3 Spectral efficiency comparison of different MIMO techniques.

Figure 7.4 Point‐to‐point MIMO channel.

Figure 7.5 Point‐to‐point MIMO channel conversion through SVD.

Figure p7.1 Capacity of 2 × 2 MIMO channel for Example 7.2.

Figure 7.6

MIMO channel for space–time block coding.

Figure p7.2 Performance comparison of 2 × 2 and 2 × 1 Alamouti scheme and si...

Figure p7.3 Performance comparison of 4 × 1 OSTBC and single antenna system....

Figure 7.7 Space–time trellis encoder.

Figure p7.4 Example of STTC encoder.

Figure p7.5 Trellis diagram for STTC with four states, QPSK and two transmit...

Figure p7.6 QPSK signal constellation and mapping.

Figure p7.7 Paths diverging at time

and remerging at time

Figure 7.8 D‐BLAST and V‐BLAST transmitter and data sequences.

Figure 7.9 MIMO detection algorithms.

Figure 7.10 MIMO system for spatial multiplexing.

Figure p7.8 2 × 2 MIMO system for spatial multiplexing.

Figure p7.9 Performance comparison of MIMO detection algorithms (ML, MMSE‐SI...

Figure p7.10 Performance comparison of massive MIMO (transmit antennas = 32,...

Figure 7.11 Spectrum comparison of (a) OFDM and (b) FBMC with eight subcarri...

Figure 7.12 5G multiple access techniques.

Figure 7.13 FDM with three carriers.

Figure 7.14 OFDM with three subcarriers.

Figure 7.15 OFDM transmitter with

parallel data sequences.

Figure 7.16 OFDM transmitter using IFFT/IDFT.

Figure 7.17 Up‐conversion from the baseband signal to the passband signal.

Figure 7.18 Down‐conversion from the passband signal to the baseband signal....

Figure 7.19 Conventional OFDM‐based communication system.

Figure p7.11 Subcarrier allocation in the OFDM symbol for Example 7.8.

Figure 7.20 FBMC transmitter and receiver.

Figure 7.21 GFDM transmitter and receiver.

Figure 7.22 UFMC transmitter and receiver.

Figure p7.12 Comparison of (a) OFDM with 200 subcarriers and (b) UFMC with 5...

Figure 7.23 Example of a Tanner graph.

Figure 7.24 Tanner graph example for LDPC decoding.

Figure 7.25 Two messages in the Tanner graph.

Figure 7.26 Examples of (a) bit node and (b) check node message passing in t...

Figure 7.27 (a) Original turbo encoder and (b) duo‐binary turbo encoder.

Figure p7.13 Turbo code BER performances with frame size (a) 1024, (b) 2048,...

Figure p7.14 FER performance of duo‐binary turbo codes, for Example 7.13.

Figure 7.28 Tanner graph and parity check matrix of (a) binary LDPC code and...

Figure p7.15 BER performance of (8176, 7156) LDPC code for Example 7.14.

Figure p7.16 Spectral efficiency comparison of non‐binary (2,4) LDPC codes o...

Figure p7.17 Throughputs of ACM for Example 7.16.

Chapter 8

Figure 8.1 Trade‐off of error probability and code rate.

Figure 8.2 Vertical and horizontal asymptotic view.

Figure 8.3 Communication model.

Figure 8.4 Normal approximation as a function of the block length.

Figure 8.5 Converse bound and achievability bound (≈ normal approximation)....

Figure p8.1 Converse bound and achievability bound (≈ normal approximation)....

Figure 8.6 Optimal error probability in terms of the information bits.

Figure 8.7 Memoryless block fading model.

Figure 8.8 Delay model for (a) uplink and (b) downlink.

Figure 8.9 HARQ latency model for FDD.

Figure 8.10 HARQ latency model for (a) TDD downlink and (b) uplink.

Figure 8.11 Delay model for the control plane from idle state to connected s...

Figure 8.12 4G handover procedure: (a) preparation, (b) execution, and (c) c...

Figure 8.13 The hourglass model of layered system architecture.

Figure 8.14 MEC system model for task scheduling.

Chapter 9

Figure 9.1 (a) Downlink and (b) uplink frame structure of NB‐IoT.

Figure 9.2 NB‐IoT connection establishment.

Figure 9.3 Robust feasible set vs nominal feasible set.

Figure 9.4 The concept of the uncertainty set.

Figure p9.1 The constraints of the problem in Example 9.1.

Figure p9.2 The ellipsoidal uncertainty set for Example 9.2.

Figure 9.5 The shaded area satisfying the first constraint.

Figure 9.6 The shaded area satisfying the second constraint.

Figure 9.7 The shaded area satisfying both constraints.

Figure 9.8 The shaded areas satisfying (a) the first constraint and (b) the ...

Figure 9.9 Transmit and receive beamforming.

Figure 9.10 JDL data fusion model.

Figure 9.11 Architectures of data fusion: (a) centralized architecture, (b) ...

Figure 9.12 Examples of (a) directed and (b) undirected graphs.

Figure p9.3 Graph model for the sensor network in Example 9.6.

Figure p9.4 Evolution of the states in Example 9.6.

Figure 9.13 Sensor network with parallel topology.

Figure 9.14 System model for distributed estimation.

Guide

Cover

Table of Contents

Begin Reading

Pages

xii

xiii

xiv

xvi

xvii

xviii

xix

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

195

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

397

398

399

400

401

Design and Optimization for 5G Wireless Communications

Haesik Kim

VTT Oulu, Finland

Copyright

This edition first published 2020

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http //www.wiley.com/go/permissions.

The right of Haesik Kim to be identified as the author of this work has been asserted in accordance with law.

Registered Offices

John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

Editorial Office

The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of Warranty

While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication Data applied for

ISBN HB: 9781119494553

Cover Design: Wiley

Cover Images: © sutadimage/Shutterstock

To my wife Hyeeun,

daughter Naul,

son Hanul

and

mother Hyungsuk

Preface

From 1G to 4G cellular networks, the main target of development was system capacity improvement. Thus, the current cellular systems have very efficient system architectures in terms of system capacity. However, it is not an optimal solution in terms of other system parameters (latency, energy efficiency, connection density, etc.). 5G systems have ambitious goals, and 5G applications cover various areas such as eHealth, factory automation, automated vehicles, critical communication, and so on. In recent mobile communications and networks events, leading mobile phone vendors and network equipment vendors have exhibited more than smartphones and networks. Connected and automated vehicles, smart cities, drones, and factory automations were highlighted, and they are highly related to latency, energy efficiency, mobility, and connection density. Thus, 5G systems no longer focus on system capacity only. Many other system parameters should be improved significantly. 5G applications can be classified into (i) enhanced mobile broadband communication (eMBB), (ii) ultra‐reliable and low latency communication (URLLC), and (iii) massive machine type communication (mMTC). Their target system parameters are different in accordance with use cases. The key metrics of eMBB, URLL, and mMTC are system capacity, latency, and connection density, respectively. They also have different system requirements and architectures. In this book, we analyze and design 5G communication and network systems from a different perspective. We introduce mathematical tools and find an optimal, suboptimal or tradeoff point to meet the system requirements.

There is a big gap between theoretical design and practical implementation. Countless papers are published every year to optimize wireless communication systems in academia, but their practical use is very limited in industry. The reasons why they have a big gap can be summarized as simple system models, limited target parameters, and lack of a holistic design. First, optimization algorithms are applied under simple system models. The simple system models sometimes include unrealistic system parameters such as perfect channel state information, limited numbers of users, no interferences, and so on. They allow optimization algorithms to solve the problem nicely, but they are far from practical solutions. Secondly, each optimization algorithm targets only one system parameter (for example, energy efficiency) while other system parameters (for example, system throughput, latency, complexity, and so on.) are not close to an optimal solution, and are sometimes even worse. Thirdly, one optimization algorithm is applied to a small part or component of a communication architecture and it finds an optimal solution. From a holistic point of view, the solution is not optimal. For example, although we design an energy‐efficient multicarrier modulation scheme and achieve significant energy savings, the other parameters might be worse and bring a higher energy consumption to another component. The architecture design is highly related to many other components of communications and networks. Sometimes there is a trade‐off relationship and sometime there is no optimal point. One decision in one design step is highly related to another decision in the next design step. It is very difficult to optimize many metrics such as complexity, system capacity, latency, energy efficiency, connection density, and flexibility. Thus, a wireless communication system designer makes a decision subjectively and empirically. It is a big challenge to reduce the gap between theoretical design and practical implementation.

This book introduces mathematical methods and optimization algorithms for wireless communications and networks and helps audiences find an optimal, suboptimal or tradeoff solution for each communication problem using the optimization algorithms. By this approach, audiences can understand how to obtain a solution under specific conditions and realize the limit of the solution.

This book is not a math book, and we skip the proofs of mathematical formulae and algorithms. This book focuses on design and optimization for 5G communication systems including eMBB, URLLC, and mMTC. The organization of the book is as follows: in Part I, mathematical methods and optimization algorithms for wireless communications are introduced. It will provide audiences with a mathematical background including approximation theory, LS estimation, MMSE estimation, ML and MAP estimation, matrix factorization, linear programming, convex optimization, gradient descent method, supervised and unsupervised learning, reinforcement learning, and so on. In Part II, 5G communication systems are designed and optimized using the mathematical methods and optimization algorithms. For example, the key metric of URLLC is latency. The latency is highly related to many PHY/MAC/network layer parameters such as frame size, transmit time interval, hybrid automatic repeat request (HARQ) processing time, round trip time, discontinuous reception, and so on. We look into them to minimize the latency. In addition, we design some key components using the optimization algorithms. It covers 5G NR, multiple input multiple output (MIMO), 5G waveforms (OFDMA, FBMC, GFDM, and UFMC), low‐density parity‐check (LDPC), short packet transmission theory, latency analysis of 4G and 5G networks, MEC optimizations, robust optimization, power control and management, wireless sensor networks, and so on. The main purpose of this book is to introduce mathematical methods and optimization algorithms and design 5G communication systems (eMBB, URLLC, mMTC) with a different perspective.

I am pleased to acknowledge the support of the VTT Technical Research Centre of Finland and John Wiley & Sons, and also the valuable discussion of my colleagues and experts in EU projects Flex5Gware, 5G‐Enhance, and 5G‐HEART. I am grateful for the support of my family and friends.

Haesik KimVTT Oulu, Finland

List of Abbreviations

first generation

second generation

third generation

3GPP

Third Generation Partnership Project

fourth generation

fifth generation

5GC

5G core

ACK

acknowledge

ACLR

adjacent channel leakage ratio

ACM

adaptive coding and modulation

ADSL

asymmetric digital subscriber line

artificial intelligence

AMF

access and mobility management function

AMPS

Advanced Mobile Phone Service

APP

a posteriori probability

augmented reality

ARFCN

Absolute Radio Frequency Channel Number

ARO

adjustable robust optimization

ARQ

automatic repeat request

access stratum

AWGN

additive white Gaussian noise

BBU

baseband unit

BCCH

broadcast control channel

BCH

broadcast channel

BER

bit error rate

BLER

block error ratio

BMSE

Bayesian mean squared error

belief propagation

BPSK

binary phase shift keying

BWP

bandwidth part

CapEx

capital expenditure

CBG

code block group

CCCH

common control channel

CCE

control channel element

CCSDS

Consultative Committee for Space Data Systems

cdf

cumulative distribution function

CDMA

code‐division multiple access

CINR

carrier‐to‐interference plus noise ratio

core network

CORESET

configurable control resource set

convex optimization problems

cyclic prefix

CPU

central processing unit

C‐plane

control‐plane

CQI

channel quality indicator

CQP

convex quadratic programming

C‐RAN

cloud radio access network

CRC

cyclic redundancy check

C‐RNTI

cell radio network temporary identifier

CRSC

circular recursive systematic constituent

CSI

channel state information

CSI‐RS

channel state information reference signal

CSIT

channel state information at transmitter

CSS

chirp spread spectrum

D2D

device‐to‐device

DARPA

Defense Advanced Research Projects Agency

D‐BLAST

Diagonal Bell Laboratories Layered Space–Time

DCCH

dedicated control channel

DCI

downlink control information

DFT

discrete Fourier transform

downlink

DL‐SCH

downlink shared channel

DMC

discrete memoryless channel

DMRS

demodulation reference signal

DNS

domain name service

DRB

data radio bearer

DRX

discontinuous reception

DSN

distributed sensor network

DSSS

direct sequence spreading spectrum

DTCH

dedicated traffic channel

E2E

end‐to‐end

EC‐GSM‐IoT

extended coverage global system for mobile communications IoT

E‐DCH

enhance dedicated channel

EDGE

Enhanced Data rates for GSM Evolution

eGPRS

enhanced general packet radio service

eMBB

enhanced mobile broadband communication

eMTC

enhanced machine‐type communication

eNB

evolved Node B

EPC

enhanced packet core

ETSI

European Telecommunications Standard Institute

EV‐DO

Evolution, Data Only

false alarm

FBMC

filter bank multicarrier

FDD

frequency division duplexing

FDM

frequency division multiplexing

FDMA

frequency division multiple access

FD‐MIMO

full‐dimension MIMO

FER

frame error rate

FFT

fast Fourier transform

frequency modulation

FONC

first‐order necessary condition

Galois Field

GFDM

generalized frequency division multiplexing

GMSK

Gaussian minimum shift keying

gNB

next‐generation NodeB

GPO

generalized precoded OFDMA

GPRS

general packet radio services

GSM

global system for mobile communications

HARQ

hybrid automatic repeat request

HSCSD

high‐speed circuit‐switched data

HSDPA

high speed downlink packet access

HSPA

high‐speed packet access

HSUPA

high‐speed uplink packet access

ICI

inter‐carrier interference

IDFT

inverse discrete Fourier transform

IFFT

inverse fast Fourier transform

IoT

Internet of Things

IPM

interior point method

ISI

inter‐symbol interference

ITU

International Telecommunication Union

ITU‐R

ITU's Radiocommunication Sector

KKT

Karush–Kuhn–Tucker

KPI

key performance indicator

LDC

linear dispersion code

LDPC

low‐density parity‐check

LIDAR

Light Detection and Ranging

LoRa

long range

linear programming

LPWAN

lower power wide area network

least squares

LTE

Long Term Evolution

lower upper

M2M

machine‐to‐machine

MAC

medium access control

MAP

maximum a posteriori

MCG

master cell group

missed detection

MDP

Markov decision problem/process

MEC

multi‐access edge computing

matched filter

MIB

master information block

MIMO

multiple input multiple output

maximum likelihood

MME

mobility management entity

MMS

multimedia messaging services

MMSE

minimum mean‐squared error

mMTC

massive machine type communication

mmWAVE

millimetre wave

MNO

mobile network operators

MRC

maximum ratio combining

MRT

maximum ratio transmission

MSE

mean square error

MVNO

mobile virtual network operators

MVU

minimum variance unbiased

NACK

negative acknowledge

NAS

non‐access stratum

NAT

network address translation

NB‐IoT

narrowband IoT

NB‐PCID

narrowband physical cell identity

NEF

network exposure function

NFV

network functions virtualization

NGMN

Next Generation Mobile Network

NG‐RAN

next generation RAN

NMT

Nordic Mobile Telephone

Node B

base station

NOMA

nonorthogonal multiple access

nondeterministic polynomial

NPBCH

narrowband physical broadcast channel

NPDCCH

narrowband physical downlink control channel

NPDSCH

narrowband physical downlink shared channel

NPRACH

narrowband physical random access channel

NPSS

narrowband primary synchronization signal

NPUSCH

narrowband physical uplink shared channel

new radio

NRS

narrowband reference signal

NSA

non‐standalone

NSSI

network slice subnet instance

NSSS

narrowband secondary synchronization signal

NTT

Nippon Telegraph and Telephone

OFDM

orthogonal frequency division multiplexing

OFDMA

orthogonal frequency division multiple access

OMA

orthogonal multiple access

OOBE

out‐of‐band emission

OpEx

operational expenditure

OQAM

offset quadrature amplitude modulation

OSTBC

orthogonal space–time block code

OTT

over‐the‐top

PAPR

peak‐to‐average power ratio

PBCH

physical broadcast channel

PCCH

paging control channel

PCH

paging channel

PDCCH

physical downlink control channel

PDCP

packet data convergence protocol

pdf

probability density function

PDN‐GW

packet data network gateway

PDSCH

physical downlink shared channel

PDU

protocol data unit

PEP

pairwise error probability

PHY

physical

pmf

probability mass function

PPN

polyphase network

PRACH

physical random access channel

PRB

physical resource block

PSM

power‐saving mode

PSS

primary synchronization signal

PSTN

public switched telephone network

PTRS

phase tracking reference signal

PUCCH

physical uplink control channel

PUSCH

physical uplink shared channel

QAM

quadrature amplitude modulation

QCQP

quadratically constrained quadratic program

QFI

QoS flow ID

QoS

quality of service

quadratic programming

QPSK

quadrature phase shift keying

RACH

random access channel

RAN

radio access network

resource block

REG

resource element group

radio frequency

reinforcement learning

RLC

radio link control

robust optimization

RRC

radio resource control

RRU

remote radio unit

Reed‐Solomon

RTT

round trip time

standalone

SARSA

state‐action‐reward‐state‐action

SC‐CPS

single carrier circularly pulse shaped

SC‐FDM

single carrier frequency division multiplexing

SCG

secondary cell group

SDAP

service data adaption protocol

SDL

supplemental downlink

SDMA

space division multiple access

SDN

software defined networking

SDP

semidefinite programming

SDR

semidefinite relaxation

SDU

service data unit

standard error

SGW

serving gateway

SIC

successive interference cancellation

SINR

signal‐to‐interference‐plus‐noise ratio

SIR

signal‐to‐interference ratio

SMDP

semi‐Markov decision problem

SMF

session management function

SMS

short messaging service

sequence number

SNR

signal‐to‐noise ratio

SOCP

second‐order cone programming

SONC

second‐order necessary condition

SOSC

second‐order sufficient condition

SRS

sounding reference signal

SSB

synchronization signal block

SSE

sum of the squared errors

SSQ

sum of squares

SSS

secondary synchronization signal

STBC

space–time block code

STSK

space–time shift keying

STTC

space–time trellis code

SVD

singular value decomposition

SVM

support vector machine

SUMT

sequential unconstrained minimization technique

TCP

transmission control protocol

TCM

trellis‐coded modulation

temporal difference

TDD

time division duplexing

TDMA

time division multiple access

transmission mode

transport network

TRxP

transmission reception point

TTI

transmission time interval

user equipment

UFMC

universal filtered multicarrier

UHD

ultra‐high definition

uplink

UL‐SCH

uplink shared channel

UMTS

Universal Mobile Telecommunications Service

UPF

user plane function

U‐plane

user‐plane

URLLC

ultra‐reliable and low latency communication

UTRAN

UMTS Terrestrial Radio Access Network

V‐BLAST

Vertical Bell Laboratories Layered Space–Time

VLSI

very large‐scale integration

VoIP

Voice over Internet Protocol

virtual reality

WAP

wireless application protocol

WGN

white Gaussian noise

WSN

wireless sensor network

zero forcing

zero padding

Part IMathematical Methods and Optimization Theories for Wireless Communications

1Historical Sketch of Cellular Communications and Networks

Cellular communication and network systems have changed rapidly over the past four decades and have adopted new technologies. The cellular communication and network industry has evolved from the first generation (1G) to the fifth generation (5G). The term “generation” is based on the 3GPP standard group's releases. In this chapter, we look into the evolution of cellular communications and networks in terms of technology enhancement, cost reduction, and use case expansion.

1.1 Evolution of Cellular Communications and Networks

Mobile phones have now become essential devices to people in their day‐to‐day lives. Their history began in the early 1900s. The predecessors of cellular systems were actually two‐way radio systems for ships and trains. In 1906, a Canadian‐born inventor Reginald Fessenden made the first two‐way voice transmission using amplitude modulation. In 1926, the German National Railway (Deutsche Reichsbahn) provided first‐class passengers with mobile telephony services on the train route between Berlin and Hamburg. After World War II, the developments for portable‐size devices accelerated in many countries. At this stage, the mobile devices were not based on a cellular concept and did not need base stations. They supported only a few users and were very expensive. Thus, those mobile devices are regarded as the “zero generation” (0G).

In 1973, Martin Cooper and John F. Mitchell of Motorola demonstrated the first public mobile phone call using a device weighing 1.1 kg [1]. In 1979, Nippon Telegraph and Telephone (NTT) deployed the first commercial cellular network in Tokyo, Japan. In 1981, the Nordic Mobile Telephone (NMT) group launched the first mobile phone network supporting international roaming among Finland, Sweden, Norway, and Denmark. Two types of NMT are NMT‐450 (450 MHz frequency bands) and NMT‐900 (900 MHz frequency bands). NMT‐900 had more channels than NMT‐450. In 1983, Advanced Mobile Phone Service (AMPS) was launched in Chicago, USA, using the Motorola DynaTAC 8000x mobile phone. The DynaTAC was the pocket‐sized phone supporting about 30 minutes talk time. It was a significant improvement and heralded a new era of cellular phones. This was the first generation (1G) cellular system supporting voice calls and using analogue technology. The technical specifications of 1G systems are summarized in Table 1.1.

Table 1.1 Technical specifications of 1G cellular systems.

NMT (NMT‐450 and NMT‐900)

AMPS

Frequency band (MHz)

463 to 468 (Rx) and 453 to 458 (Tx) in NMT‐450 standard, 935 to 960 (Rx) and 890 to 915 (Tx) in NMT‐900 standard

824 to 849(Tx) and 869 to 894 (Rx)

Channel bandwidth

25 kHz in NMT‐450 standard, 12.5 kHz in NMT‐900 standard

30 kHz

Multiple access scheme

FDMA

Duplex scheme

FDD

No. of channels

200 in NMT‐450 and 1999 in NMT‐900

832 in AMPS and 2496 in Narrow band AMPS

Modulation

Frequency modulation

(

)

Frequency modulation (FM)

Number of users per channel

One

Base station antenna

Omni‐directional

Switch type

Circuit switching

Data rate

2.4 ∼ 14.4 kbps

The 1G analogue system established the foundation of cellular networks and adopted key techniques such as frequency reuse, licensed spectrum and coordinated mobile network. The cellular concept [2] allows us to overcome many problems such as coverage, power consumption, user capacity, interference, and so on. The frequency reuse is a key idea of the cellular concept. Neighboring cells operate on different frequencies. Thus, the interference can be reduced and cell capacity can be increased. In addition, the mobile operator holds licensed spectrum for exclusive use and coordinates the call for seamless access. However, the 1G analogue system had the limitation of capacity because the frequency division multiple access (FDMA) system is inefficient. The FDMA of 1G systems supports only one user per channel. The 1G device was heavy, with a high energy consumption, and high cost.

In 1991, the second generation (2G) of cellular systems was commercially launched in Finland. The 2G systems can be divided into the global system for mobile communications (GSM) using time division multiple access (TDMA) technology, and IS‐95 (or cdmaOne) using code‐division multiple access (CDMA) technology. GSM is very widely deployed in all countries. About 80% of all 2G subscribers around the world used GSM [3]. IS‐95 is deployed in the US and parts of Asia. About 17% of all 2G subscribers around the world used IS‐95 [3]. In addition, TDMA‐based IS‐136 was developed as an AMPS evolution in the US but migrated to GSM. The 2G digital systems are voice‐oriented systems supporting voice‐mail and short messaging service (SMS). The GSM is based on a TDMA technique that support eight users per 200 kHz frequency band by assigning different time slots for each user. As a modulation technique of the GSM system, Gaussian minimum shift keying (GMSK) is adopted. It allows the GSM system to have a constant envelope property, providing low power consumption. The technical specifications for 2G systems are summarized in Table 1.2.

Table 1.2 Technical specifications of 2G cellular systems.

GSM

IS‐95

Frequency bands

850/900 MHz, 1.8/1.9 GHz

850 MHz/1.9 GHz

Channel bandwidth

200 kHz

1.25 MHz

Multiple access scheme

TDMA/FDMA

CDMA

Duplex scheme

FDD

Frame duration

20 ms

4.6 ms

Modulation

GMSK

BPSK

Modulation efficiency (bps/Hz)

1.35

Spectrum efficiency (conversation/cell/MHz)

12.1 ∼ 45.1

5.0 ∼ 6.6

Switch type

Circuit switching for voice and packet switching for data

The main disadvantages of the 1G systems were low capacity, high‐energy consumption, and heavy and high‐cost handsets. In 2G systems, the capacity problem was solved by voice compressing techniques and TDMA/CDMA techniques. The high energy consumption problem was solved by the lower radio power emission of the digital system. The heavy and high‐cost handset problem was solved by low digital component cost and size. In addition, simple encryption was used in 2G systems. However, the 2G system still requires a large frequency spacing to reduce interference and does not support soft‐handover. Most importantly, there were market requirements relating to data services such as real‐time news, stock information, weather, location, and so on. The 2G system could not satisfy them and evolved to 2.5G systems. Thus, the general packet radio services (GPRS) appeared in the market and allowed limited web browsing and multimedia services such as wireless application protocol (WAP), multimedia messaging services (MMS), and email access. The main difference between 2G systems and 2.5G systems is the switching method. The 2G systems are designed for voice services in a circuit‐switched network. However, the 2.5G systems are designed to support data services so it partially implements a packet‐switched network. The reason why we call this system 2.5G is that it was not a major change but an upgrade over existing 2G infrastructure. It required some modification of base stations and mobile phones. Besides GPRS, there were Enhanced Data rates for GSM Evolution (EDGE) and high‐speed circuit‐switched data (HSCSD) as an evolution of TDMA systems, and IS‐95B as an evolution of CDMA systems. However, the 2.5G systems were not deployed widely due to the following limitations:

(i) The actual data rate was much lower than advertised. The maximum data rate of 172.2 kbps could be achieved when a single user takes all radio resources (8 time‐slots) without any error protection. However, a mobile operator should provide a subscriber with enough radio resources. Thus, the actual data rate was about 30–40 kbps.

(ii) Transit delays occurred. The GPRS data packets arrived at one destination from many different places. It caused packet loss or corruption over the radio links.

(iii) Applications were limited. It supported many applications such as email, internet access, location‐based services, and so on. Traditional web browsers support access to full websites with high‐resolution images, video, and lots of information. However, WAP scaled this down and supported a small‐size image and text‐based website. It did not meet the market requirement, and thus many mobile operators waited for the next generation (3G).

NTT Docomo launched the first pre‐commercial 3G network in 1998, and then deployed the first commercial 3G network based on W‐CDMA technology in Japan in October 2001. SK Telecom commercially launched the first 3G network based on CDMA200 technology in South Korea in January 2002. 3G systems provide us with much higher data rates, better voice quality and multimedia services. In order to achieve a global interoperability of mobile networks, the International Telecommunication Union (ITU) identified a global frequency band in the 2 GHz range and invited proposals for IMT‐2000 to meet high data rate requirements: 2 Mbps for fixed users, 284 kbps for pedestrians, and 144 kbps for vehicular environments. The 3G services include global roaming, high‐quality voice calls, location‐based services, video conferencing, video on demand, online banking and so on. The ITU approved several proposals for IMT‐2000. Two major proposals were the Universal Mobile Telecommunications Service (UMTS), also called W‐CDMA, by the Third Generation Partnership Project (3GPP) standard (GSM camp), and the CDMA2000 by the 3GPP2 standard (IS‐95 camp). They both selected CDMA as the multiple access technique because of multiple benefits: (i) more efficient spectrum use; (ii) increased system capacity; and (iii) better security. There are many similarities between the two systems: direct sequence spreading spectrum (DSSS) multiple access, orthogonal code channelization, random access, power control scheme, rake receivers, soft handover, voice decoder, and so on. The technical specifications of 3G systems are summarized in Table 1.3.

Table 1.3 Technical specifications of 3G cellular systems.

UMTS (3 GPP Release 99)

CDMA2000 (1x)

Frequency bands

850/900 MHz, 1.8/1.9/2.1 GHz

450/850 MHz 1.7/1.9/2.1 GHz

Channel bandwidth

5 MHz

1.25 MHz

Multiple access scheme

CDMA

Duplex scheme

FDD/TDD

FDD

Data modulation

DSSS, QPSK

DSSS, BPSK/QPSK

Peak data rate

384∼2048 kbps

307 kbps

Chip rate

3.84 Mcps

1.2288 Mcps

Frame length

5 ms (signaling), 20, 40, 80 ms physical layer frames

10 ms for physical layer, 10, 20, 40, and 80 ms for transport layer

Channel coding

Convolutional and turbo code

Network synchronization

Synchronous/asynchronous

Synchronous

Core network

GSM‐MAP

ANSI‐41

The UMTS was originally developed by the European Telecommunications Standard Institute (ETSI). However, the seven telecommunications standard development organizations (ETSI, ARIB, ATIS, CCSA, TSDSI, TTA, and TTC) built a partnership known as the 3GPP, and the 3GPP completed the UMTS standards as the evolution of GSM in 1999. The UMTS architecture supporting backward compatibility with GSM and GPRS architecture is composed of (i) a core network (CN) with functions of switching, routing, and subscriber management, (ii) UMTS Terrestrial Radio Access Network (UTRAN) connecting mobile phones to the public switched telephone network (PSTN) and packet networks, and (iii) user equipment (UE) such as mobile phones and any handheld devices. The CDMA2000 1× implies the same bandwidth (1.25 MHz) as the 2G (IS‐95). The data rate of CDMA2000 1× has been increased but it could not meet the 3G requirements of the ITU. Thus, it was evolved to CDMA200 EV‐DO (Evolution, Data Only) in October 2000. As the name EV‐DO implies, it supports data only. It provides up to 2.4 Mbps downlink data rate and up to 153 kbs uplink data rate, and includes new techniques such as adaptive coding and modulation, data optimized channel, and opportunistic scheduling. In the late 1990s, the data usage pattern was asymmetric. The higher data rates are required in downlink to access the internet, download a huge file and use video‐on‐demand services. In order to respond to market demands for much higher data rates, the next evolution is high‐speed packet access (HSPA) as 3.5G systems by the 3GPP. In the HSPA family, high‐speed downlink packet access (HSDPA) was introduced in 3GPP Release 5 in 2002. The HSDPA supported up to 14.4 Mbps peak data rate theoretically, but the typical user data rate was 500 kbps to 2 Mbps. The HSDPA adopted new advanced techniques (hybrid automatic repeat request [HARQ], link adaptation, fast dynamic scheduling) to deliver higher data rates and more capacity [4]. The HARQ improved the performance by reducing the retransmission rate. There are two types of HARQ: chase combining HARQ, and Incremental redundancy HARQ. The chase combining HARQ is regards as repetition coding. The retransmission includes the same information and redundancy. The receiver combines the received bits with the same bits from the previous transmission using maximum ratio combining (MRC). In contrast, the incremental redundancy HARQ uses multiple different sets of coded bits. They are transmitted in different channels and the receiver obtains additional information. Link adaptation techniques help to increase system throughput. The UE of HSDPA reports a channel quality indicator (CQI) to a base station (NodeB). Depending on this channel state information, the base station varies the modulation order and coding rate per user and frame. The fast dynamic scheduler provides us with better radio resource utilization by exploiting the diversity of channels and allocating more radio resource to a user whose channel condition is favorable. High‐speed uplink packet access (HSUPA) was introduced in 3GPP Release 6 in 2004. The HSUPA supports up to 5.76 Mbps peak data rate theoretically, but a typical user data rate was 500 kbps to 1 Mbps. This high data rate allows us to use more applications such as Voice over Internet Protocol (VoIP). The HSUPA added the enhance dedicated channel (E‐DCH) to UMTS and included new features such as a shorter transmission time interval (TTI). After that, evolved HSPA (HSPA+) was introduced in 3GPP Release 7. It provides us with high data rates (up to 42.2 Mbps in downlink and up to 22 Mbps in the uplink) and includes new techniques (high order modulation, 2 × 2 multiple input multiple output [MIMO]). The high order modulation such as 64QAM allows us to improve by about 50% more data transmission in the packets. The MIMO plays a key role in HSPA and beyond. About 25% average cell throughput gain is achieved by a HSPA+MIMO solution when compared with a single antenna system [5].

The driving force for 4G systems comes from market needs. Although 3G systems improved significantly over 2G systems, the main function was still voice communications on circuit switch systems. People preferred to use broadband data services offered by wired communication systems (ADSL, cable modem, and so on) and short‐range wireless communication systems (WiFi) because the mobile data service by cellular systems was much more expensive than ADSL and WiFi. Thus, 4G systems were developed on a new network architecture. Voice services and data services are no longer separated. All IP core networks of 4G systems support both voice service and high‐speed data services including multimedia services, mobile TV, video conferencing, and so on. Another driving force is popularization of the smartphone. Unlike a traditional cellular phone, smartphones have a full keyboard, large display, touch screen, cameras, video recorder, GPS navigation, microphones, and many sensors (accelerometer, gyroscope, magnetometer, light sensor, proximity sensor, barometer, thermometer, fingerprint sensor, etc.). Smartphones are powerful mobile devices as much as a laptop. Thus, data services became more important than voice services. In 2008, ITU defined requirements of IMT‐Advanced (4G systems) including 100 Mbps for vehicular environments and 1 Gbps for fixed users or pedestrians. However, two major proposals, mobile WiMAX and LTE, known as 4G systems, do not fulfill the requirements of IMT‐Advanced. Nevertheless, they were approved as 4G systems. After that, both systems gradually improved and met many parts of the requirements. In terms of technology, they both have many similarities such as all IP networks, orthogonal frequency division multiple access (OFDMA) based multiple access schemes, MIMOs, and so on. On the other hand, the differences are (i) compatibility: LTE is compatible with 3G, but WiMAX does not support coexistence of WiMAX and 3G; (ii) mobility support: LTE supports up to 450 km/h, but WiMAX supports up to 120 km/h; (iii) frame duration: LTE has 10 ms frame duration but WiMAX frame duration is 5 ms; (iv) channel bandwidth: LTE channel bandwidth is from 1.4 to 20 MHz but WiMAX uses from 5 to 10 MHz; and so on. The technical specifications of 4G systems are summarized in Table 1.4.

Table 1.4 Technical specifications of 4G cellular systems.

LTE (3 GPP Release 8)

Mobile WiMAX (IEEE 802.16e‐2005)

Frequency bands

700 MHz, 1.7/2.1 GHz, 2.6 GHz, 1.5 GHz

2.3 GHz, 2.6 GHz, and 3.5 GHz

Channel bandwidth

Scalable, 1.4, 3, 5, 10, 15, 20 MHz

Scalable, 5, 7, 8.75, 10 MHz

Data rate

150 Mbps (DL)/75 Mbps (UL)

46 Mbps(DL)/7 Mbps (UL)

Frame size

1 ms (sub‐frame)

5 ms (frame)

Multiple access scheme

OFDMA (DL)/SC‐FDMA (UL)

OFDMA

Duplex scheme

FDD/TDD

FFT size

128, 256, 512, 1024, 1536, 2048

128, 512, 1024, 2048

OFDMA symbol duration

71.8, 71.3, 83.2, and 166.6 μs

102.9 μs

Cyclic prefix

1/4, 1/8, 1/16 and 1/32

Normal, Extended

Modulation

QPSK, 16QAM, 64QAM

Subcarrier spacing

7.5, 15 kHz

10.9375 kHz

Channel coding

Convolutional coding, Convolutional turbo coding

MIMO

Multilayer precoded spatial multiplexing, space frequency block coding

Beamforming, space time coding and spatial multiplexing

Networks

All IP EUTRAN network, two‐tier architecture (EUTRAN and EPC)

All IP network, two‐tier architecture (ASN and CSN)

1.2 Evolution to 5G Networks

The 3GPP continuously evolves for more data capacity. Capacity of cellular systems has been improved by three different approaches: bandwidth increase, spectral efficiency increase, and frequency reuse. According to Shannon's capacity formula [6] and extended capacity for MIMO channels [7], channel capacity can be simply expressed as follows:

(1.1)

where C, W, n, S/N are channel capacity, bandwidth, number of antennae and signal‐to‐noise ratio, respectively. The first approach (bandwidth increase) is to increase W by more bandwidth by regulation, carrier aggregation technique, and cognitive radio. The second approach (spectral efficiency increase) is to increase n by MIMO techniques and S/N by interference mitigation techniques, error correction coding, traffic adaptation, and so on. The third approach (frequency reuse) is to increase the number of cells or sections by cell sectorization and femto cells. The channel capacity in Equation (1.1) can be modified as follows:

(1.2)

Based on those approaches, cellular systems improved system capacity significantly. Now, we prepare for the 5G era and expect life enhancement, such as can be achieved by new features of 5G systems such as the Internet of Things (IoT), public safety, proximity service, vehicular communications, terrestrial TV, Gbps mobility, and so on. In order to achieve the goals for 5G, the standard bodies set high requirements, and industry and academia stay in line with them. From 1G to 4G, the main target metric was system capacity. However, 5G systems focus not only on system capacity but also latency, connection density and energy efficiency. The 5G applications can be classified into three main communication applications: (i) enhanced mobile broadband communication (eMBB); (ii) ultra‐reliable and low latency communication (URLLC), and (iii) massive machine type communication (mMTC). The ITU defined 5G as IMT‐2020 in 2015. The ITU's Radiocommunication Sector (ITU‐R) Working Party 5D has the leading role and plans to deploy the 5G system in 2020 [8]. The 3GPP also plans to enhance the LTE system and meet the 5G requirements of ITU [9]. The 5G target of the 3GPP is to (i) improve LTE capacity and performance, and (ii) address a new business segment. The 3GPP standards keep expanding their platform to new 5G services while improving their system performance to meet ambitious 5G requirements. The initial features were completed in September 2016 and the broader framework was finalized in June 2017 (Release 14).

As we can observe in Figure 1.1, 3GPP 5G Phase 1/Release 15 is planned to complete in September 2018. Key features of Release 15 will be forward compatibility with previous versions, eMBB, roaming, charging, network sharing, QoS control, and so on. 3GPP 5G Phase 2/Release 16 is planned to complete in March 2020. Key features of Release 16 will be compatibility with non‐3GPP access, IoT, satellite support, URLLC, 5G media for virtual reality, and so on. Cellular systems have evolved approximately every 20 years, as shown in Figure 1.2. We expect 5G to revolutionize our day‐to‐day life and various industries (telecommunications, transportation, public safety, healthcare, manufacturing, media, etc.) in the near future. In [10], ITU‐R summarizes 5G use cases and applications: transportation, public safety, utilities, remote control, healthcare, education, Smart cities, wearables, Smart homes, agriculture, and enhanced multimedia. Among them, people pay attention to five key vertical sectors: automated driving, factory automation, smartgrids, eHealth, and augmented reality. In the next chapter, we look into their applications, system parameters and requirements. To sum up, 1G laid the foundation for mobile telephony, 2G popularized mobile telephony, 3G expanded mobile services from voice to data, 4G builds all IP core networks and achieves mobile broadband, and 5G includes new wireless features such as eMBB, URLL, and mMTC.

Figure 1.1 Timeline of 3GPP 5G developments.

Figure 1.2 Evolution of cellular systems.

References

Edward C. Niehenke, “Wireless Communications: Present and Future: Introduction to Focused Issue Articles”, IEEE Microwave Magazine 15, 2, 16–35 2014.

MacDonald, V.H. (1979). The cellular concept.

Bell System Technical Journal

58 (1): 15–42.

GSM Association. (2010). GSM World Statistics.

https://web.archive.org/web/20100521013451/http://www.gsmworld.com/newsroom/market-data/market_data_summary.htm

Holma, H., Toskala, A., Ranta‐aho, K, Pirskanen, J. High‐Speed Packet Access Evolution in 3GPP Release 7. IEEE Communications Magazine 45, 12, 29–35 2007.

GSM Association. (2010). MIMO in HSPA: the Real‐World Impact.

https://www.gsma.com/spectrum/wp-content/uploads/2012/03/umtsmimofinal.pdf

Shannon, C.E. (1948). A mathematical theory of communication.

Bell System Technical Journal

27: 379, 623–423, 656.

Tse, D. and Viswanath, P. (2006).

Fundamentals of Wireless Communication

. Cambridge University Press.

International Telecommunication Union. (not dated).

http://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2020/Pages/default.aspx

3GPP (not dated). 3GPP standard systems heading into the 5G era.

http://www.3gpp.org/news-events/3gpp-news/1614-sa_5g

International Telecommunication Union. (2015). Recommendation ITU‐R M.2083‐0, IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond.

https://www.itu.int/rec/R-REC-M.2083-0-201509-I/en

25G Wireless Communication System Parameters and Requirements

From 1G to 4G, the cellular systems have been developed to meet high system capacity requirements and high data rates, and then provide us with high‐quality voice services and high‐throughput data services. In 5G wireless communication systems, a paradigm shift is required as target applications are diversified. In order to support various services and use cases, the various metrics (data rate, system capacity, mobility, latency, reliability, coverage, energy efficiency, connection density, CapEx and OpEx, accessibility, flexibility, security, quality of service, etc.) need to be improved significantly and new technological developments are required. In this chapter, we look into 5G key performance indicators (KPIs) and requirements and their relationships, and also introduce key enabling technologies and approaches.

2.1 5G Requirements

The 4G system provides a broadband service to people, and mobile broadband services are now popular in many countries. However, people want significant improvements in networks and mobile devices and expect better and various services. Basically, a user would require better battery life, higher user‐experienced data rates, seamless user experience, better mobility, lower cost, and so on. 5G networks should improve scalability, capacity, flexibility, energy efficiency, coverage, security, compatibility, and cost efficiency. As we discussed in Chapter 1, the ITU‐R Working Party 5D has a leading role in 5G system developments and defines the 5G minimum technical performance requirements [1]. Based on those criteria [1], ITU‐R will accept the candidate standards for IMT‐2020 as 5G. According to three usage scenarios (enhanced mobile broadband communication [eMBB], ultra‐reliable and low latency communication [URLLC], and massive machine type communication [mMTC]), key requirements are defined.

The mMTC is a very important driver of 5G systems. In order to provide a subscriber with mMTC services in a dense area, device density is a key performance indicator. The estimated device density and traffic characteristics to support 10 000 households/km2 in a city are summarized in Table 2.1 [2].

Table 2.1 Traffic characteristics for mMTC city scenario [2].

Typical message size (bytes)

Message interval

Device density (per km

)

Water meters

100

12 h

10 000

Electricity meters

100

24 h

10 000

Gas meters

100

30 min

10 000

Vending machines

150

24 h

150

Bike fleet management

150

30 min

200

Pay‐as‐you‐drive

150

10 min

2250

As we can see in Table 2.1, the message size is small but device density is high in the city scenario. Thus, the connection density of mMTC is a key requirement. ITU‐R specifies a required connection density of 1 M devices/km2, greater than 99% grade of service, and less than 10 seconds latency [1

Tausende von E-Books und Hörbücher

Ihre Zahl wächst ständig und Sie haben eine Fixpreisgarantie.

Sie haben über uns geschrieben: