Next Generation Multiple Access E-Book
111,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Highly comprehensive resource investigating how next-generation multiple access (NGMA) relates to unrestricted global connection, business requirements, and sustainable wireless networks Next Generation Multiple Access is a comprehensive, state-of-the-art, and approachable guide to the fundamentals and applications of next-generation multiple access (NGMA) schemes, guiding the future development of industries, government requirements, and military utilization of multiple access systems for wireless communication systems and providing various application scenarios to fit practical case studies. The scope and depth of this book are balanced for both beginners to advanced users. Additional references are provided for readers who wish to learn more details about certain subjects. Applications of NGMA outside of communications, including data and computing assisted by machine learning, protocol designs, and others, are also covered. Written by four leading experts in the field, Next Generation Multiple Access includes information on: * Foundation and application scenarios for non-orthogonal multiple access (NOMA) systems, including modulation, detection, power allocation, and resource management * NOMA's interaction with alternate applications such as satellite communication systems, terrestrial-satellite communication systems, and integrated sensing * Collision resolution, compressed sensing aided massive access, latency management, deep learning enabled massive access, and energy harvesting * Holographic-pattern division multiple access, over-the-air transmission, multi-dimensional multiple access, sparse signal detection, and federated meta-learning assisted resource management Next Generation Multiple Access is an essential reference for those who are interested in discovering practical solutions using NGMA technology, including researchers, engineers, and graduate students in the disciplines of information engineering, telecommunications engineering, and computer engineering.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 854
Veröffentlichungsjahr: 2024
Ähnliche
Table of Contents
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Acknowledgments
1 Next Generation Multiple Access Toward 6G
1.1 The Road to NGMA
1.2 Non‐Orthogonal Multiple Access
1.3 Massive Access
1.4 Book Outline
Part I: Evolution of NOMA Towards NGMA
2 Modulation Techniques for NGMA/NOMA
2.1 Introduction
2.2 Space‐Domain IM for NGMA
2.3 Frequency‐Domain IM for NGMA
2.4 Code‐Domain IM for NGMA
2.5 Power‐Domain IM for NGMA
2.6 Summary
References
Notes
3 NOMA Transmission Design with Practical Modulations
3.1 Introduction
3.2 Fundamentals
3.3 Effective Throughput Analysis
3.4 NOMA Transmission Design
3.5 Numerical Results
3.6 Conclusion
References
Notes
4 Optimal Resource Allocation for NGMA
4.1 Introduction
4.2 Single‐Cell Single‐Carrier NOMA
4.3 Single‐Cell Multicarrier NOMA
4.4 Multi‐cell NOMA with Single‐Cell Processing
4.5 Numerical Results
4.6 Conclusions
Acknowledgments
References
Notes
5 Cooperative NOMA
5.1 Introduction
5.2 System Model for D2MD‐CNOMA
5.3 Adaptive Aggregate Transmission
5.4 Performance Analysis
5.5 Numerical Results and Discussion
5.A Appendix
References
6 Multi‐scale‐NOMA: An Effective Support to Future Communication–Positioning Integration System
6.1 Introduction
6.2 Positioning in Cellular Networks
6.3 MS‐NOMA Architecture
6.4 Interference Analysis
6.5 Resource Allocation
6.6 Performance Evaluation
References
7 NOMA‐Aware Wireless Content Caching Networks
7.1 Introduction
7.2 System Model
7.3 Algorithm Design
7.4 Numerical Simulation
7.5 Conclusion
References
8 NOMA Empowered Multi‐Access Edge Computing and Edge Intelligence
8.1 Introduction
8.2 Literature Review
8.3 System Model and Formulation
8.4 Algorithms for Optimal Offloading
8.5 Numerical Results
8.6 Conclusion
Acknowledgments
References
9 Exploiting Non‐orthogonal Multiple Access in Integrated Sensing and Communications
9.1 Introduction
9.2 Developing Trends and Fundamental Models of ISAC
9.3 Novel NOMA Designs in Downlink and Uplink ISAC
9.4 Case Study: System Model and Problem Formulation
9.5 Case Study: Proposed Solutions
9.6 Case Study: Numerical Results
9.7 Conclusions
References
Part II: Massive Access for NGMA
10 Capacity of Many‐Access Channels
10.1 Introduction
10.2 The Many‐Access Channel Model
10.3 Capacity of the MnAC
10.4 Energy Efficiency of the MnAC
10.5 Discussion and Open Problems
Acknowledgments
References
11 Random Access Techniques for Machine‐Type Communication
11.1 Fundamentals of Random Access
11.2 A Game Theoretic View
11.3 Random Access Protocols for MTC
11.4 Variants of 2‐Step Random Access
11.5 Application of NOMA to Random Access
11.6 Low‐Latency Access for MTC
References
12 Grant‐Free Random Access via Compressed Sensing: Algorithm and Performance
12.1 Introduction
12.2 Joint Device Detection, Channel Estimation, and Data Decoding with Collision Resolution for MIMO Massive Unsourced Random Access
12.3 Exploiting Angular Domain Sparsity for Grant‐Free Random Access: A Hybrid AMP Approach
12.4 LEO Satellite‐Enabled Grant‐Free Random Access
12.5 Concluding Remarks
Acknowledgments
References
13 Algorithm Unrolling for Massive Connectivity in IoT Networks
13.1 Introduction
13.2 System Model
13.3 Learned Iterative Shrinkage Thresholding Algorithm for Massive Connectivity
13.4 Learned Proximal Operator Methods for Massive Connectivity
13.5 Training and Testing Strategies
13.6 Simulation Results
13.7 Conclusions
References
14 Grant‐Free Massive Random Access: Joint Activity Detection, Channel Estimation, and Data Decoding
14.1 Introduction
14.2 System Model
14.3 Joint Estimation via a Turbo Receiver
14.4 A Low‐Complexity Side Information‐Aided Receiver
14.5 Simulation Results
14.6 Summary
References
Note
15 Joint User Activity Detection, Channel Estimation, and Signal Detection for Grant‐Free Massive Connectivity
15.1 Introduction
15.2 Receiver Design for Synchronous Massive Connectivity
15.3 Receiver Design for Asynchronous Massive Connectivity
15.4 Conclusion
References
Notes
16 Grant‐Free Random Access via Covariance‐Based Approach
16.1 Introduction
16.2 Device Activity Detection in Single‐Cell Massive MIMO
16.3 Device Activity Detection in Multi‐Cell Massive MIMO
16.4 Practical Issues and Extensions
16.5 Conclusions
References
17 Deep Learning‐Enabled Massive Access
17.1 Introduction
17.2 System Model
17.3 Model‐Driven Channel Estimation
17.4 Model‐Driven Activity Detection
17.5 Auto‐Encoder‐Based Pilot Design
17.6 Numerical Results
17.7 Conclusion
References
Notes
18 Massive Unsourced Random Access
18.1 Introduction
18.2 URA with Single‐Antenna Base Station
18.3 URA with Multi‐Antenna Base Station
References
Note
Part III: Other Advanced Emerging MA Techniques for NGMA
19 Holographic‐Pattern Division Multiple Access
19.1 Overview of HDMA
19.2 System Model
19.3 Multiuser Holographic Beamforming
19.4 Holographic Pattern Design
19.5 Performance Analysis and Evaluation
19.6 Summary
References
Notes
20 Over‐the‐Air Computation
20.1 Introduction
20.2 AirComp Fundamentals
20.3 Power Control for AirComp
20.4 Beamforming for AirComp
20.5 Extension
20.6 Conclusion
References
Note
21 Multi‐Dimensional Multiple Access for 6G: Efficient Radio Resource Utilization and Value‐Oriented Service Provisioning
21.1 Introduction
21.2 Principle of MDMA
21.3 Value‐Oriented Operation of MDMA
21.4 Multi‐Dimensional Resource Utilization in Value‐Oriented MDMA
21.5 Numerical Results and Analysis
21.6 Conclusion
References
Notes
22 Efficient Federated Meta‐Learning Over Multi‐Access Wireless Networks
22.1 Introduction
22.2 Related Work
22.3 Preliminaries and Assumptions
22.4 Nonuniform Federated Meta‐Learning
22.5 Federated Meta‐Learning Over Wireless Networks
22.6 Extension to First‐Order Approximations
22.7 Simulation
22.8 Conclusion
References
Notes
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Advantages and disadvantages of spatial‐domain IM for NGMA.
Table 2.2 Advantages and disadvantages of frequency‐domain IM for NGMA.
Table 2.3 The computational complexity of the ML and GD detector for the pr...
Table 2.4 An example of mapping table for CIM‐SCMA with and .
Table 2.5 Advantages and disadvantages of code‐domain IM for NGMA.
Chapter 6
Table 6.1 Instantaneous channel gains of MS‐NOMA waveform in 4‐BSs scenario...
Table 6.2 Simulation parameters.
Table 6.3 The comparison between MS‐NOMA and PRS.
Chapter 12
Table 12.1 Simulation Parameters.
Chapter 14
Table 14.1 Simulation parameters.
Chapter 17
Table 17.1 Training setup.
Table 17.2 Parameters and computational complexities of channel estimation ...
Table 17.3 Parameters and computational complexities of activity detection ...
Chapter 19
Table 19.1 Simulation parameters.
Chapter 21
Table 21.1 MA mode selection for value‐oriented MDMA and MD‐IMA.
Chapter 22
Table 22.1 Key notations.
Table 22.2 Parameters in simulation.
Table 22.3 Test accuracy after 50 rounds of training.
List of Illustrations
Chapter 2
Figure 2.1 Transmitter structure of SM‐based downlink NOMA with multi‐RF cha...
Figure 2.2 Transmitter structure of single‐RF and SM‐based downlink NOMA.
Figure 2.3 Block diagram of SM‐NOMA with a single RF chain and without SIC....
Figure 2.4 Performance comparison among CRS‐SM‐NOMA, CRS‐NOMA, and SM‐OMA, w...
Figure 2.5 Block diagram of RSM‐based downlink NOMA.
Figure 2.6 Block diagram of uplink SM‐SCMA.
Figure 2.7 Block diagrams of the transmitter in the OFDM‐IM NOMA scheme.
Figure 2.8 Block diagrams of the receivers in the OFDM‐IM NOMA scheme.
Figure 2.9 Block diagram of the DM‐OFDM NOMA transmitter.
Figure 2.10 Schematic diagram of GCIM‐NOMA and CDM‐NOMA.
Figure 2.11 BER performance comparisons: “GCIM‐NOMA (4, 3, 8PSK, BPSK),” “CD...
Figure 2.12 Block diagram of uplink CIM‐SCMA.
Figure 2.13 Block diagram of downlink CIM‐MC‐CDMA.
Figure 2.14 Performance comparison between CIM‐MC‐CDMA and conventional MC‐C...
Figure 2.15 System model with two users, where is the near user, and the o...
Figure 2.16 Constellation () of superimposed signal transmitted with 2‐PA...
Figure 2.17 Constellations of the 2‐PAM signals and : (a) constellations ...
Figure 2.18 BER comparison between PS‐NOMA and NOMA.
Figure 2.19 Achievable rate comparison between PS‐NOMA and NOMA.
Chapter 3
Figure 3.1 System model of a downlink multichannel NOMA system.
Figure 3.2 (a) The constellation of employing ‐QAM; (b) The constellation...
Figure 3.3 (a) The constellation of employing ‐PAM; (b) The constellation...
Figure 3.4 Comparison of the proposed and optimal power allocation schemes w...
Figure 3.5 Comparison of the effective throughput versus the total power bud...
Figure 3.6 Comparison of the effective throughput obtained via different alg...
Chapter 4
Figure 4.1 The performance gap between the approximated and exact closed for...
Figure 4.2 Performance comparison between FDMA, FDMA–NOMA with different , ...
Figure 4.3 The outage probability and average sum‐rate of the JSPA, JRPA, an...
Chapter 5
Figure 5.1 System model. An illustration of the proposed adaptive aggregate ...
Figure 5.2 Outage probability for and to decode and in and (i.e....
Figure 5.3 Outage probability for to decode and in (i.e., and ), ...
Figure 5.4 Outage probability for and to decode and in and (i.e....
Figure 5.5 Comparison of Ergodic sum capacity among the adaptive aggregate t...
Chapter 6
Figure 6.1 The MS‐NOMA architecture.
Figure 6.2 The diagram of interference in the single‐cell network.
Figure 6.3 The diagram of interference in the multicell networks.
Figure 6.4 Average BER at (37.5, 30) viewpoint.
Figure 6.5 Average BER at (142.5, 30) viewpoint.
Figure 6.6 Example of BERs over C‐Subs (, ).
Figure 6.7 Range measurement accuracy ().
Figure 6.8 The relationship between and for PRS.
Figure 6.9 The resource element consumption. (a) MS‐NOMA ( MHz). (b) PRS (
Figure 6.10 The energy consumption. (a) MS‐NOMA ( MHz). (b) PRS ( MHz). (c...
Figure 6.11 The proposed CP4A algorithm.
Figure 6.12 The traditional method without power allocation.
Figure 6.13 The allocated power and CPRs with different channel gains ( MHz...
Figure 6.14 Detailed illustration of the positioning accuracy.
Figure 6.15 The positioning accuracy of PRS ( MHz).
Chapter 7
Figure 7.1 Convergence performance of the designed joint optimization algori...
Figure 7.2 System's average latency versus Backhaul link rate, wherein . (a...
Figure 7.3 System's average latency versus Cache size, wherein . (a) and ...
Figure 7.4 Cache hit ratio versus Cache size, wherein . (a) and (b) .
Chapter 8
Figure 8.1 A two‐tier radio access network with one MBS and two of SBSs. Eac...
Figure 8.2 Illustration of our CDD algorithm: the optimal values of Problem ...
Figure 8.3 Illustration of our TLHS algorithm: variations of and during ...
Figure 8.4 Performance comparisons among our proposed algorithm, LINGO, HED,...
Figure 8.5 Computing time comparisons among our proposed algorithm, LINGO, H...
Figure 8.6 The convergence example of the EUs' probabilities (i.e., ). (a) ...
Figure 8.7 Illustration of the effectiveness of CE‐based algorithm. (a) an...
Chapter 9
Figure 9.1 Illustration of (a) a downlink ISAC model and (b) an uplink ISAC ...
Figure 9.2 Illustration of the NOMA‐empowered downlink ISAC design.
Figure 9.3 An uplink ISAC system with OMA, NOMA, and semi‐NOMA schemes.
Figure 9.4 Simulation setup.
Figure 9.5 Convergence of Algorithm 9.1.
Figure 9.6 Trade‐off between throughput and effective sensing power. (a) Und...
Figure 9.7 Obtained transmit beampattern by different schemes when the commu...
Chapter 10
Figure 10.1 Plot of given by (10.14), where .
Figure 10.2 Plot of specified in Theorem 10.2, where , i.e., dB.
Figure 10.3 versus for , , i.e., each user transmits 100 bits.
Figure 10.4 versus for , , i.e., each user transmits 100 bits.
Chapter 11
Figure 11.1 Mixed strategy NE as a function of for different values of ....
Figure 11.2 4‐step random access protocol in MTC.
Figure 11.3 An illustration of fast retrial for multichannel ALOHA with 4 ch...
Figure 11.4 An example with and , where active user 2 transmits preamble ...
Figure 11.5 Throughput curves of pure ALOHA, S‐ALOHA, and NOMA‐ALOHA protoco...
Figure 11.6 Probability of error as a function of when and .
Figure 11.7 Contention resolution diversity S‐ALOHA.Casini et al. (2007)...
Chapter 12
Figure 12.1 Performance of the proposed URA schemes versus different paramet...
Figure 12.2 Factor graph for the MRF support structure.
Figure 12.3 Performance of various algorithms with , , and dB; (a) Detec...
Figure 12.4 Minimum SNR required to achieve with different values of . Th...
Figure 12.5 Performance comparison between the ConvSBL‐GAMP and TDSBL‐CF und...
Figure 12.6 Performance comparison between the ConvSBL‐GAMP and GMMV‐AMP und...
Figure 12.7 Performance comparison of DAD between the ConvSBL‐GAMP and GMMV‐...
Chapter 13
Figure 13.1 Illustration of an IoT network where only a small fraction of Io...
Figure 13.2 Illustration of the group–sparse matrix recovery problem.
Figure 13.3 Performance comparison between LISTA‐GS and ISTA‐GS.
Figure 13.4 Convergence performance comparison among ISTA‐GS, FISTA‐GS, LIST...
Figure 13.5 Convergence performance comparison among LISTA‐GS, LPOM‐GS, LPOM...
Chapter 14
Figure 14.1 Grant‐based and grant‐free RA. With grant‐based RA, each active ...
Figure 14.2 The proposed turbo receiver for massive RA.
Figure 14.3 The factor graph of the joint posterior distribution , where ....
Figure 14.4 The proposed SI‐aided receiver for massive RA.
Figure 14.5 Illustrations on the SI update rule in (14.22). We set and . ...
Figure 14.6 Activity detection error probability versus the number of active...
Figure 14.7 NMSE of channel estimation versus the number of active users....
Figure 14.8 BLER versus the number of active users.
Figure 14.9 BLER versus the normalized average execution time ().
Chapter 15
Figure 15.1 Factor graph representation of the considered synchronous system...
Figure 15.2 Performance versus SNR: , , , . (a) AER versus SNR. (b) SER ...
Figure 15.3 SER versus SNR: , , , .
Figure 15.4 The factor graph for the Bayesian receiver with toy‐problem para...
Figure 15.5 Illustration of message flow of check node and SDL module with...
Figure 15.6 Example of truncated filter and its sampling points at the rec...
Figure 15.7 Detection performance of the proposed design with , . (a) PER ...
Figure 15.8 Parameter estimation of the proposed design with , . (a) Chann...
Chapter 16
Figure 16.1 Phase transition of the covariance‐based approach for device act...
Figure 16.2 Comparison of the probability of error of existing algorithms ve...
Figure 16.3 Phase transition of the covariance‐based approach for device act...
Figure 16.4 Comparison of the probability of error of existing algorithms ve...
Chapter 17
Figure 17.1 Proposed model‐driven channel estimation approach. (a) GROUP LAS...
Figure 17.2 Proposed model‐driven activity detection approach. (a) Covarianc...
Figure 17.3 Proposed auto‐encoder‐based pilot design. (a) Auto‐encoder‐based...
Figure 17.4 Channel estimation in the correlated case with a single active g...
Figure 17.5 Channel estimation in the correlated case with a single active g...
Figure 17.6 Computation time for channel estimation in the correlated case w...
Figure 17.7 Device activity detection in the correlated case with i.i.d. gro...
Figure 17.8 Device activity detection in the correlated case with i.i.d. gro...
Figure 17.9 Computation time for device activity detection in the correlated...
Chapter 18
Figure 18.1 The architecture of a slotted transmission framework.
Figure 18.2 Minimum required versus the number of active users for the two...
Figure 18.3 Minimum required versus number of users for the three main alg...
Chapter 19
Figure 19.1 Physical structure of RHS.
Figure 19.2 Geometrical relation between RHS and object beam.
Figure 19.3 Illustration of HDMA.
Figure 19.4 HDMA wireless communication system aided by an extremely large‐s...
Figure 19.5 HDMA transmission block diagram.
Figure 19.6 Sum rate versus number of RHS elements.
Figure 19.7 Cost‐efficiency versus the physical dimension with different c...
Chapter 20
Figure 20.1 Two paradigms for WDA: sequential data communication and computa...
Figure 20.2 Illustration of the basic principle for AirComp (with an example...
Figure 20.3 The computation MSE of AirComp versus the average receive SNR in...
Figure 20.4 The average MSE of AirComp versus the average receive SNR in fad...
Figure 20.5 The computation MSE versus the transmit power when , , and
Figure 20.6 The computation MSE versus the transmit power when , , and
Chapter 21
Figure 21.1 Illustration of MDMA scheme via multi‐dimensional multiplexing f...
Figure 21.2 Illustration of the MDMA design incorporated with resource utili...
Figure 21.3 The performance of individual‐level value realization for each e...
Figure 21.4 The performance of system‐level value realization for network op...
Figure 21.5 CDF of UE's value‐realization utility function defined in formul...
Figure 21.6 Scatter plot of resource utilization cost in different dimension...
Figure 21.7 Average downlink throughput and resource utilization cost of UE ...
Chapter 22
Figure 22.1 The architecture of federated meta‐learning over a wireless netw...
Figure 22.2 Comparison of convergence rates with different numbers of partic...
Figure 22.3 Effect of local update steps on convergence rates. Fewer local s...
Figure 22.4 Comparison of convergence, energy cost, and wall‐clock training ...
Figure 22.5 Comparison of convergence, energy cost, and wall‐clock time unde...
Figure 22.6 Effect of channel gains on performance. Worse channel conditions...
Figure 22.7 Effect of weight parameters and . A large value of leads to...
Guide
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Acknowledgments
Begin Reading
Index
End User License Agreement
Pages
ii
iii
iv
xix
xx
xxi
xxiii
xxiv
xxv
xxvi
xxvii
xxviii
xxix
xxx
xxxi
xxxiii
xxxv
1
2
3
4
5
6
7
9
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
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
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
194
195
196
197
198
199
200
201
202
203
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
227
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
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
315
316
317
318
319
320
321
322
323
324
325
326
327
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
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
465
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
583
584
585
586
IEEE Press
445 Hoes Lane Piscataway, NJ 08854
IEEE Press Editorial Board
Sarah Spurgeon,
Editor in Chief
Jón Atli Benediktsson
Behzad Razavi
Jeffrey Reed
Anjan Bose
Jim Lyke
Diomidis Spinellis
James Duncan
Hai Li
Adam Drobot
Amin Moeness
Brian Johnson
Tom Robertazzi
Desineni Subbaram Naidu
Ahmet Murat Tekalp
Next Generation Multiple Access
Edited by
Yuanwei LiuQueen Mary University of LondonUK
Liang LiuHong Kong Polytechnic UniversityHong KongChina
Zhiguo DingUniversity of ManchesterUK
Xuemin ShenUniversity of WaterlooOntarioCanada
Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
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, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 750‐4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748‐6011, fax (201) 748‐6008, or online at http://www.wiley.com/go/permission.
Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional 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.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762‐2974, outside the United States at (317) 572‐3993 or fax (317) 572‐4002.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com.
Library of Congress Cataloging‐in‐Publication Data:
Names: Liu, Yuanwei, author. | Liu, Liang (Professor), author. | Ding, Zhiguo, author. | Shen, X. (Xuemin), 1958-author.Title: Next generation multiple access / Yuanwei Liu, Liang Liu, Zhiguo Ding, Xuemin (Sherman) Shen.Description: Hoboken, New Jersey : Wiley, [2024] | Includes index.Identifiers: LCCN 2023045953 (print) | LCCN 2023045954 (ebook) | ISBN 9781394180493 (hardback) | ISBN 9781394180509 (adobe pdf) | ISBN 9781394180516 (epub)Subjects: LCSH: Multiple access protocols (Computer network protocols)Classification: LCC TK5105.5 .L569 2024 (print) | LCC TK5105.5 (ebook) | DDC 004.6/2--dc23/eng/20231130LC record available at https://lccn.loc.gov/2023045953LC ebook record available at https://lccn.loc.gov/2023045954
Cover Design: WileyCover Image: © Ralf Hiemisch/Getty Images
About the Editors
Yuanwei Liu received the PhD degree in electrical engineering from the Queen Mary University of London, UK, in 2016. He was with the Department of Informatics, King's College London, from 2016 to 2017, where he was a Post‐Doctoral Research Fellow. He has been a Senior Lecturer (Associate Professor) with the School of Electronic Engineering and Computer Science, Queen Mary University of London, since August 2021, where he was a Lecturer (Assistant Professor) from 2017 to 2021. His research interests include non‐orthogonal multiple access, reconfigurable intelligent surfaces, integrated sensing and communications, and machine learning. Yuanwei Liu has been a Web of Science Highly Cited Researcher since 2021, an IEEE Communication Society Distinguished Lecturer, an IEEE Vehicular Technology Society Distinguished Lecturer, and the academic Chair for the Next‐Generation Multiple Access Emerging Technology Initiative. He was listed as one of 35 Innovators Under 35 China in 2022 by MIT Technology Review. He received the IEEE ComSoc Outstanding Young Researcher Award for EMEA in 2020. He received the 2020 IEEE Signal Processing and Computing for Communications (SPCC) Technical Committee Early Achievement Award and IEEE Communication Theory Technical Committee (CTTC) 2021 Early Achievement Award. He received the IEEE ComSoc Outstanding Nominee for Best Young Professionals Award in 2021. He is the co‐recipient of the Best Student Paper Award in IEEE VTC2022‐Fall, the Best Paper Award in ISWCS 2022, and the 2022 IEEE SPCC‐TC Best Paper Award. He serves as the Co‐Editor‐in‐Chief of IEEE ComSoc TC Newsletter, an Area Editor of IEEE Communications Letters, an Editor of IEEE Communications Surveys & Tutorials, IEEE Transactions on Wireless Communications, IEEE Transactions on Network Science and Engineering, and IEEE Transactions on Communications. He serves as the Guest Editor for IEEE JSAC on Next‐Generation Multiple Access, IEEE JSTSP on Intelligent Signal Processing and Learning for Next‐Generation Multiple Access, and IEEE Network on Next Generation Multiple Access for 6G. He serves as the Publicity Co‐Chair for IEEE VTC 2019‐Fall, Symposium Co‐Chair for Cognitive Radio & AI‐enabled networks for IEEE GLOBECOM 2022 and Communication Theory for IEEE GLOBECOM 2023. He serves as the chair of the Special Interest Group (SIG) in SPCC Technical Committee on Signal Processing Techniques for Next‐Generation Multiple Access, the vice‐chair of SIG in SPCC Technical Committee on Near Field Communications for Next Generation Mobile Networks, and the vice‐chair of SIG WTC on Reconfigurable Intelligent Surfaces for Smart Radio Environments.
Liang Liu received the BEng degree from the School of Electronic and Information Engineering at Tianjin University in 2010 and the PhD degree from the Department of Electrical and Computer Engineering at National University of Singapore (NUS) in 2014. He was a Post‐Doctoral Fellow at the University of Toronto from 2015 to 2017 and a research fellow at NUS from 2017 to 2019. Currently, he is an Assistant Professor in the Department of Electrical and Electronic Engineering at The Hong Kong Polytechnic University (PolyU). His research interests include wireless communications and networking, advanced signal processing and optimization techniques, and Internet‐of‐Things (IoT). Dr. Liang LIU is the recipient of the 2021 IEEE Signal Processing Society Best Paper Award, the 2017 IEEE Signal Processing Society Young Author Best Paper Award, the Best Student Paper Award for 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), and the Best Paper Award for 2011 International Conference on Wireless Communications and Signal Processing (WCSP). He was listed in Highly Cited Researchers, also known as “World's Most Influential Scientific Minds”, by Clarivate Analytics (Thomson Reuters) in 2018. He is an editor for IEEE Transactions on Wireless Communications and was a leading guest editor for IEEE Wireless Communications' special issue on “Massive Machine‐Type Communications for IoT.”
Zhiguo Ding received the BEng in electrical engineering from the Beijing University of Posts and Telecommunications in 2000 and his PhD degree in Electrical Engineering from Imperial College London in 2005. He is currently a Professor in Communications at the University of Manchester. Previously, he had been working at Queen's University Belfast, Imperial College, Newcastle University and Lancaster University. From October 2012 to September 2024, he has also been an academic visitor in Prof. Vincent Poor's group at Princeton University. Dr. Ding research interests are machine learning, B5G networks, cooperative and energy harvesting networks and statistical signal processing. His h‐index is 100, and his work receives 44,000+ Google citations. He is serving as an Area Editor for the IEEE TWC and OJ‐COMS, an Editor for IEEE TVT, COMST, and OJ‐SP, and was an Editor for IEEE TCOM, IEEE WCL, IEEE CL, and WCMC. He received the best paper award of IET ICWMC‐2009 and IEEE WCSP‐2014, the EU Marie Curie Fellowship 2012–2014, the Top IEEE TVT Editor 2017, IEEE Heinrich Hertz Award 2018, IEEE Jack Neubauer Memorial Award 2018, IEEE Best Signal Processing Letter Award 2018, Alexander von Humboldt Foundation Friedrich Wilhelm Bessel Research Award 2020, and IEEE SPCC Technical Recognition Award 2021. He is a member of the Global Research Advisory Board of Yonsei University, a Web of Science Highly Cited Researcher in two disciplines (2019–2023), an IEEE ComSoc Distinguished Lecturer, and a Fellow of the IEEE.
Xuemin Shen received the PhD degree in electrical engineering from Rutgers University, New Brunswick, NJ, USA, in 1990. He is a University Professor with the Department of Electrical and Computer Engineering, University of Waterloo, Canada. His research focuses on network resource management, wireless network security, the Internet of Things, 5G and beyond, and vehicular networks. Dr. Shen is a registered Professional Engineer of Ontario, Canada, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, a Royal Society of Canada Fellow, a Chinese Academy of Engineering Foreign Member, and a Distinguished Lecturer of the IEEE Vehicular Technology Society and Communications Society. Dr. Shen received “West Lake Friendship Award” from “Zhejiang Province in 2023,” President's Excellence in Research from the University of Waterloo in 2022, the Canadian Award for Telecommunications Research from the Canadian Society of Information Theory (CSIT) in 2021, the R.A. Fessenden Award in 2019 from IEEE, Canada, Award of Merit from the Federation of Chinese Canadian Professionals (Ontario) in 2019, James Evans Avant Garde Award in 2018 from the IEEE Vehicular Technology Society, Joseph LoCicero Award in 2015 and Education Award in 2017 from the IEEE Communications Society (ComSoc), and Technical Recognition Award from Wireless Communications Technical Committee (2019) and AHSN Technical Committee (2013). He has also received the Excellent Graduate Supervision Award in 2006 from the University of Waterloo and the Premier's Research Excellence Award (PREA) in 2003 from the Province of Ontario, Canada. He serves/served as the General Chair for the 6G Global Conference'23, and ACM “Mobihoc'15,” Technical Program Committee Chair/Co‐Chair for IEEE Globecom'24, 16 and 07, IEEE Infocom'14, IEEE VTC'10 Fall, and the Chair for the IEEE ComSoc Technical Committee on Wireless Communications. Dr. Shen is the President of the IEEE ComSoc. He was the Vice President for Technical and Educational Activities, Vice President for Publications, Member‐at‐Large on the Board of Governors, Chair of the Distinguished Lecturer Selection Committee, and Member of the IEEE Fellow Selection Committee of the ComSoc. Dr. Shen served as the Editor‐in‐Chief of the IEEE IoT Journal, IEEE Network, and IET Communications.
List of Contributors
Faouzi Bellili
Department of Electrical and Computer Engineering
University of Manitoba
Winnipeg
Manitoba
Canada
Xinyu Bian
Department of Electronic and Computer Engineering
The Hong Kong University of Science and Technology
Hong Kong
China
Xiaowen Cao
School of Science and Engineering (SSE) and Future Network of Intelligence Institute (FNii)
The Chinese University of Hong Kong (Shenzhen)
Shenzhen
China
Xuan Chen
Department of Electronics and Communication Engineering
Guangzhou University
Guangzhou
China
Yilong Chen
School of Science and Engineering (SSE) and Future Network of Intelligence Institute (FNii)
The Chinese University of Hong Kong (Shenzhen)
Shenzhen
China
Zhilin Chen
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering
University of Toronto
Toronto
Ontario
Canada
Jinho Choi
School of Information Technology
Deakin University
Burwood
Victoria
Australia
Shuguang Cui
School of Science and Engineering (SSE) and Future Network of Intelligence Institute (FNii)
The Chinese University of Hong Kong (Shenzhen)
Shenzhen
China
and
Data‐Driven Information System Lab
Shenzhen Research Institute of Big Data
Shenzhen
China
and
Peng Cheng Laboratory
Shenzhen
China
Ying Cui
IoT Thrust
Information Hub
Hong Kong University of Science and Technology (Guangzhou)
Guangzhou
China
Ruoqi Deng
School of Electronics
Peking University
Beijing
China
Boya Di
School of Electronics
Peking University
Beijing
China
Zhiguo Ding
School of Electrical and Electronic Engineering
University of Manchester
Manchester
UK
Yaru Fu
Department of Electronic Engineering and Computer Science
School of Science and Technology
Hong Kong Metropolitan University
Hong Kong
China
Dongning Guo
Department of Electrical and Computer Engineering
Robert R. McCormick School of Engineering and Applied Science
Northwestern University
Evanston
IL
USA
Wenfang Guo
School of Electronic Engineering
Beijing University of Posts and Telecommunications
Beijing
China
Wudan Han
Department of Electrical and Computer Engineering
Western University
London
Ontario
Canada
Ekram Hossain
Department of Electrical and Computer Engineering
University of Manitoba
Winnipeg
Manitoba
Canada
Kaibin Huang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Hong Kong SAR
China
Shuchao Jiang
Key Laboratory for Information Science of Electromagnetic Waves (MoE)
Department of Communication Science and Engineering
Fudan University
Shanghai
China
Wuyang Jiang
Department of Communications and Signals
School of Urban Railway Transportation
Shanghai University of Engineering Science
Shanghai
China
Eduard Jorswieck
Institute for Communications Technology
Technical University of Braunschweig
Braunschweig
Germany
Bo Li
Communication Research Center
Harbin Institute of Technology
Harbin
China
Qiang Li
Department of Electronic Engineering
Jinan University
Guangzhou
China
Tianya Li
Department of Electronic Engineering
School of Electronic Information and Electrical Engineering
Shanghai Jiao Tong University
Shanghai
China
Yang Li
State Key Laboratory of Internet of Things for Smart City
University of Macau
Macao
China
Liang Liu
Department of Electrical and Electronic Engineering
The Hong Kong Polytechnic University
Hong Kong
China
Lina Liu
Department of Electrical and Computer Engineering
Robert R. McCormick School of Engineering and Applied Science
Northwestern University
Evanston
IL
USA
Wang Liu
IoT Thrust
Information Hub
Hong Kong University of Science and Technology (Guangzhou)
Guangzhou
China
Ya‐Feng Liu
Institute of Computational Mathematics and Scientific/Engineering Computing
Academy of Mathematics and Systems Science
Chinese Academy of Sciences
Beijing
China
Yuanwei Liu
School of Electronic Engineering and Computer Science
Queen Mary University of London
London
UK
Yuyi Mao
Department of Electronic and Information Engineering
The Hong Kong Polytechnic University
Hong Kong
China
Jie Mei
Department of Electrical and Computer Engineering
Western University
London
Ontario
Canada
Amine Mezghani
Department of Electrical and Computer Engineering
University of Manitoba
Winnipeg
Manitoba
Canada
Xidong Mu
School of Electronic Engineering and Computer Science
Queen Mary University of London
London
UK
Dusit Niyato
School of Computer Science and Engineering
Nanyang Technological University
Singapore
Singapore
Liping Qian
College of Information Engineering
Zhejiang University of Technology
Hangzhou
China
Tony Q. S. Quek
Department of Information Systems Technology and Design
Information Systems Technology and Design
Singapore University of Technology and Design
Singapore
Singapore
Ju Ren
Department of Computer Science and Technology
Tsinghua University
Beijing
China
Sepehr Rezvani
Institute for Communications Technology
Technical University of Braunschweig
Braunschweig
Germany
and
Department of Telecommunication Systems
Technical University of Berlin
Berlin
Germany
and
Department of Wireless Communications and Networks
Fraunhofer Institute for Telecommunications Heinrich‐Hertz‐Institute
Berlin
Germany
Zhichao Shao
National Key Laboratory of Wireless Communications
University of Electronic Science and Technology of China
Chengdu
China
Boxiao Shen
Department of Electronic Engineering
School of Electronic Information and Electrical Engineering
Shanghai Jiao Tong University
Shanghai
China
Xuemin Shen
Department of Electrical and Computer Engineering
University of Waterloo
Waterloo
Ontario
Canada
Yuanming Shi
School of Information Science and Technology
ShanghaiTech University
Shanghai
China
Zheng Shi
School of Intelligent Systems Science and Engineering
Jinan University
Zhuhai
China
Volodymyr Shyianov
Department of Electrical and Computer Engineering
University of Manitoba
Winnipeg
Manitoba
Canada
Foad Sohrabi
Nokia Bell Labs
Murray Hill
NJ
USA
Lingyang Song
School of Electronics
Peking University
Beijing
China
Tianzhu Song
School of Electronic Engineering
Beijing University of Posts and Telecommunications
Beijing
China
Bowen Tan
IoT Thrust, Information Hub
Hong Kong University of Science and Technology (Guangzhou)
Guangzhou
China
Jie Tang
School of Electronic and Information Engineering
South China University of Technology
Guangzhou
China
Jiaheng Wang
National Mobile Communications Research Laboratory
Southeast University
Nanjing
China
and
Pervasive Communication Research Center
Purple Mountain Laboratories
Nanjing
China
Xianbin Wang
Department of Electrical and Computer Engineering
Western University
London
Ontario
Canada
Xin Wang
Key Laboratory for Information Science of Electromagnetic Waves (MoE)
Department of Communication Science and Engineering
Fudan University
Shanghai
China
Yuan Wang
National Mobile Communications Research Laboratory
Southeast University
Nanjing
China
and
Pervasive Communication Research Center
Purple Mountain Laboratories
Nanjing
China
Zhaolin Wang
School of Electronic Engineering and Computer Science
Queen Mary University of London
London
UK
Ziyue Wang
Institute of Computational Mathematics and Scientific/Engineering Computing
Academy of Mathematics and Systems Science
Chinese Academy of Sciences
Beijing
China
and
School of Mathematical Sciences
University of Chinese Academy of Sciences
Beijing
China
Miaowen Wen
Department of Electronic and Information Engineering
South China University of Technology
Guangzhou
China
Kai‐Kit Wong
Department of Electronic and Electrical Engineering
University College London
London
UK
Yongpeng Wu
Department of Electronic Engineering
School of Electronic Information and Electrical Engineering
Shanghai Jiao Tong University
Shanghai
China
Yuan Wu
State Key Laboratory of Internet of Things for Smart City
University of Macau
Macao
China
and
Zhuhai UM Science and Technology Research Institute
Zhuhai
China
Xinyu Xie
Department of Electronic Engineering
School of Electronic Information and Electrical Engineering
Shanghai Jiao Tong University
Shanghai
China
Chongbin Xu
Key Laboratory for Information Science of Electromagnetic Waves (MoE)
Department of Communication Science and Engineering
Fudan University
Shanghai
China
Jie Xu
School of Science and Engineering (SSE) and Future Network of Intelligence Institute (FNii)
The Chinese University of Hong Kong (Shenzhen)
Shenzhen
China
Yao Xu
School of Electronic and Information Engineering
Nanjing University of Information Science and Technology
Nanjing
China
Lu Yin
School of Electronic Engineering
Beijing University of Posts and Telecommunications
Beijing
China
Wei Yu
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering
University of Toronto
Toronto
Ontario
Canada
Xiaojun Yuan
National Key Laboratory of Wireless Communications
University of Electronic Science and Technology of China
Chengdu
China
Sheng Yue
Department of Computer Science and Technology
Tsinghua University
Beijing
China
Jun Zhang
Department of Electronic and Computer Engineering
The Hong Kong University of Science and Technology
Hong Kong
China
Nan Zhao
School of Information and Communication Engineering
Dalian University of Technology
Dalian
China
Tianying Zhong
National Mobile Communications Research Laboratory
Southeast University
Nanjing
China
Yong Zhou
School of Information Science and Technology
ShanghaiTech University
Shanghai
China
Guangxu Zhu
Data‐Driven Information System Lab
Shenzhen Research Institute of Big Data
Shenzhen
China
Yinan Zou
School of Information Science and Technology
ShanghaiTech University
Shanghai
China
Preface
Multiple access (MA) has long been the “pearls in the crown” for each generation of mobile communications networks. Compared to 1G–5G, the next generation of mobile communications network imposes much more stringent requirements, which calls for the development of advanced MA technologies, namely next‐generation multiple access (NGMA). The key concept of NGMA is to intelligently accommodate multiple terminals and multiple services in the allotted resource blocks in the most efficient manner possible, considering metrics such as resource efficiency, connectivity, coverage, and intelligence. In this book, we explore the road to developing NGMA with a focus on non‐orthogonal multiple access (NOMA), massive access, and other possible MA candidates. This book consists of three parts. In Part I, we discuss the evolution of NOMA toward NGMA with the aid of advanced modulation techniques, power allocation and resource management strategies, as well as NOMA‐empowered new wireless applications. In Part II, we discuss about massive IoT connectivity from the perspective of capacity limits, random access schemes, device activity detection in massive IoT connectivity, and deep learning for massive access. In Part III, we focus on advanced emerging MA techniques, which can be used in the next‐generation mobile networks. We believe that this book will provide readers with a clear picture of the development of NGMA toward next‐generation mobile networks to support ubiquitous and massive connectivity.
Yuanwei Liu
Liang Liu
Zhiguo Ding
Xuemin Shen
Acknowledgments
We would like to express our sincere gratitude to all the colleagues who contributed to the work and projects that led to this work.
We would also like to particularly thank all the editorial staff from Wiley for producing this book.
Yuanwei Liu
Liang Liu
Zhiguo Ding
Xuemin Shen
1Next Generation Multiple Access Toward 6G
Yuanwei Liu1, Liang Liu2, Zhiguo Ding3, and Xuemin Shen4
1School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
2Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China
3School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK
4Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
1.1 The Road to NGMA
Since the feasibility of wireless communications was first demonstrated at the end of the 19th century, wireless communication technologies have rapidly developed and significantly changed human life and society. Until today, there have been five generations of wireless networks, which enable diverse ways to communicate with each other (e.g., text, voice, and video). By the end of 2023, it is predicted that the number of mobile users and Internet‐enabled devices will reach 13.1 billion and 29.3 billion, respectively. Given the explosively increasing number of connected devices and the emergence of revolutionary killer applications (e.g., autonomous driving, telemedicine, metaverse, etc.), there are stringent requirements to be satisfied by future wireless networks, such as Tb/s‐order peak data rates, extremely low latency, ultra‐high reliability, and massive connectivity.
Considering the aforementioned targets, growing research efforts are being devoted to achieving them in next‐generation wireless networks (e.g., sixth generation (6G) and beyond). More importantly, in contrast to the current wireless networks, which have mainly focused on providing communication services within terrestrial coverage, next‐generation wireless networks' vision goes beyond this and can be summarized as follows:
Human–machine–things connections
: Next‐generation wireless networks have to shift from connecting humans only to connecting humans/machines/things, thus facilitating beneficial interactions between different types of devices and realizing promising applications, such as smart cities and smart factories.
Ubiquitous space‐air‐ground‐sea coverage
: Instead of merely providing terrestrial coverage, next‐generation wireless networks aim to achieve flawless information flow over space/air/ground/sea with the integration of heterogeneous infrastructure, such as satellite/drone/underwater vehicle‐based non‐terrestrial networks and cellular/WiFi‐based terrestrial networks.
Multi‐functionality integration
: Compared to current communication‐oriented wireless networks, next‐generation wireless networks are expected to integrate other diverse functionalities, including but not limited to radio frequency (RF) sensing, imaging, computing, and localization.
Native intelligent networks
: On the one hand, artificial intelligence (AI) will play an unprecedented important role in improving the performance of next‐generation wireless networks. On the other hand, next‐generation wireless networks also have to support seamless AI services. This necessitates the development of native AI for next‐generation wireless networks.
However, considering the fact that the available radio resources are limited and the emerging requirements are quite stringent, the realization of the above exciting vision of next‐generation wireless networks is nontrivial and requires advanced technologies to be developed. Among others, multiple access (MA) is one of the fundamental technologies in wireless networks, which enables a large number of user terminals to be simultaneously served given the available radio resources. Given the advantages of low complexity and interference avoidance, orthogonal multiple access (OMA) schemes have been extensively employed in practical wireless communication systems, such as frequency division multiple access (FDMA) in the first generation (1G), time division multiple access (TDMA) in the second generation (2G), code division multiple access (CDMA) with orthogonal codes in the third generation (3G), and orthogonal frequency division multiple access (OFDMA) in the fourth generation (4G) and fifth generation (5G), where users are allocated with orthogonal frequency/time/code resource blocks. As discussed above, next‐generation wireless networks not only have to satisfy stringent communication requirements but also have to connect heterogeneous types of devices, provide ubiquitous coverage, integrate diverse functionalities, and support native intelligence. In line with this, communication‐oriented MA schemes are expected to be replaced by advanced MA schemes, namely next generation multiple access (NGMA).
The key concept of NGMA is to intelligently accommodate multiple terminals and multiple services in the allotted resource blocks in the most efficient manner possible considering metrics such as resource efficiency, connectivity, coverage, and intelligence. In contrast to previous MA schemes, which are mainly employed in cellular systems, NGMA is expected to be applicable to a wide range of wireless systems, including but not limit to cellular systems, WiFi, satellite systems, unmanned systems, and radar/sensing/monitoring systems, thus realizing the attractive vision of next‐generation wireless networks. Given these promising features, in the past few years, the development of NGMA has been pursued from different viewpoints by multiple disciplines, including information theory, communication theory, wireless networking, signal processing, machine learning, big data, and hardware design, comprising both theoretical and experimental perspectives. The main research route toward NGMA can be summarized as follows. On the one hand, a paradigm shift in MA design can be observed from grant‐based OMA to non‐orthogonal multiple access (NOMA)/massive assess and other promising MA candidates, thus significantly improving the resource efficiency and supporting massive connectivity. On the other hand, new techniques (e.g., smart antenna, random access, and advanced modulation and channel coding schemes) and advanced machine learning (ML) tools have been exploited for NGMA to satisfy the stringent requirements and intelligently support new services. In the following, we will give a brief overview of the two main promising NGMA candidates, namely NOMA and massive access.
1.2 Non‐Orthogonal Multiple Access
Different from OMA, the key idea of NOMA is to allow different users to share the same resource blocks. To deal with the resulting interference caused by the non‐orthogonal resource allocation, superposition coding (SC), and successive interference cancelation (SIC) techniques have to be employed at the transmitters and receivers, respectively. Although NOMA increases the transmitter and receiver complexity, significant benefits can be achieved, such as supporting massive connectivity, achieving high spectral efficiency, and guaranteeing user fairness. Given the above advantages, growing research efforts have been devoted into NOMA. Generally speaking, NOMA can be loosely classified into power‐domain (PD)‐NOMA and code‐domain (CD)‐NOMA.
PD‐NOMA
: The key idea of PD‐NOMA is to serve multiple users in the same time/frequency/code resources and distinguish them in the power domain. SC and SIC are the two key technologies in PD‐NOMA, which have been proven to be capacity‐achieving in the single‐antenna broadcast (BC) and MAC. For broadband communications over frequency‐selective fading channels, where the channel coherence bandwidth is smaller than the system bandwidth, PD‐NOMA can be straightforwardly integrated with OFDMA by assigning multiple users to each OFDMA subcarrier and serving them with PD‐NOMA. This approach was adopted in multiuser superposition transmission (MUST), which was incorporated into LTE‐A for simultaneously supporting two users on the same OFDMA subcarrier. Another application of PD‐NOMA is layered division multiplexing (LDM), which was included in the digital TV standard (ATSC 3.0) to deliver multiple superpositioned data streams for TV broadcasting.
CD‐NOMA
: Inspired by CDMA, where multiple users are served via the same time/frequency resources and distinguished by the allocated dedicated user‐specific spreading sequences, CD‐NOMA was proposed, whose key idea is still to serve multiple users in the same time/frequency resources but employing user‐specific spreading sequences, which are either sparse sequences or non‐orthogonal cross‐correlation sequences having low cross‐correlation. At the receiver, multiuser detection (MUD) is usually carried out in an iterative manner using MP‐based algorithms. The family of CD‐NOMA schemes has many members, such as low‐density signature (LDS)‐CDMA, LDS‐OFDM, sparse code multiple access (SCMA), and pattern division multiple access (PDMA).
The motivation for treating NOMA as the one of the most promising candidates for NGMA can be explained as follows. On the one hand, we expect the overloaded regime to be an important use case for next‐generation wireless networks, for which NOMA is a promising technology for supporting massive connectivity. On the other hand, the existing research contributions have shown that NOMA provides a higher degree of compatibility and flexibility. This enables the synergistic integration of NOMA with other components of next generation networks, such as multi‐antenna techniques, multi‐functionality integration and other physical layer techniques. However, the evolution of NOMA toward NGMA also imposes many challenges. The recent advances in developing NOMA for NGMA will be provided in the Part I of this book.
1.3 Massive Access
The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new Internet‐of‐Things (IoT) applications. To achieve this goal, massive machine‐type communications (mMTC) has been defined as a key use case for 5G networks. A generic scenario for mMTC involves a massive number of IoT devices, among which a small number of them become active at each time slot. The sporadic traffic pattern in IoT systems is due to the fact that often IoT devices are designed to sleep most of the time in order to save energy and are activated only when triggered by external events. In the above massive connectivity setup, the core problem is how to detect the active users from a large number of users as quickly and accurately as possible such that we can schedule them to transmit their critical data with the minimum delay. In the conventional cellular network designed for human‐type communications, the contention‐based random access schemes, e.g., ALOHA, are widely used, where the users have to compete for the grant from the base stations for data transmission. However, in IoT systems with a large number of devices, the collision probability for competing for the transmission grant will be very high, which leads to huge access delay. To tackle this issue, the grant‐free random access scheme, where the users directly send their data to the base stations without waiting for their permissions, is now deemed as a low‐latency solution for massive IoT connectivity. In general, there are three ways for accommodating the active IoT devices to transmit under the grant‐free random access scheme.
Compressed sensing‐based random access
: Because of the sporadic user activity, the joint problem for device activity detection and channel estimation can be cast as a compressed sensing problem. Algorithms such as approximate message passing (AMP) can be applied to detect the active devices from a large number of devices and estimate their channels in massive connectivity.
Covariance‐based random access
: In some IoT application scenario, we merely aim to detect the active devices, without the need to know their channels. Recently, it has been shown that the covariance matrix of the received signals is sufficient for detecting the active devices. Because the task of channel estimation is not considered, the covariance‐based method can detect the active devices within a shorter time period as compared to the compressed sensing‐based method, which also aims to estimate the channels of the active devices.
Unsourced random access
: In some IoT application scenario, we are not interested in detecting which subset of users are active. Instead, we just would like to decode their messages. This belongs to the unsourced random access problem. In the literature, various codebooks have been designed for unsourced random access, such that user messages can be correctly decoded even if a large number of users use the same codebook.
1.4 Book Outline
This book provides a comprehensive overview of the novel technologies for developing NGMA, with a particular focus on the NOMA, massive access, and other new MA technologies. The rest of this book consists of three parts.
Part I – Evolution of NOMA toward NGMA
: This first part discusses the evolution of NOMA toward NGMA with the aid of advanced modulation techniques, power allocation, and resource management strategies, as well as NOMA‐empowered new wireless applications. In particular,
Chapter 2
provides a comprehensive overview of index modulation techniques for NOMA/NGMA and outlines the recent research progress.
Chapter 3
