Cover
Preface
List of Abbreviations
Part I: Mathematical Methods and Optimization Theories for Wireless Communications
1. 1 Historical Sketch of Cellular Communications and Networks
  1. 1.1 Evolution of Cellular Communications and Networks
  2. 1.2 Evolution to 5G Networks
  3. References
2. 2 5G Wireless Communication System Parameters and Requirements
  1. 2.1 5G Requirements
  2. 2.2 Trade‐off of 5G System Metrics
  3. References
3. 3 Mathematical Methods for Wireless Communications
  1. 3.1 Signal Spaces
  2. 3.2 Approximation and Estimation in Signal Spaces
  3. 3.3 Matrix Factorization
  4. References
4. 4 Mathematical Optimization Techniques for Wireless Communications
  1. 4.1 Introduction
  2. 4.2 Mathematical Modeling and Optimization Process
  3. 4.3 Linear Programming
  4. 4.4 Convex Optimization
  5. 4.5 Gradient Descent Method
  6. References
5. 5 Machine Learning
  1. 5.1 Artificial Intelligence, Machine Learning, and Deep Learning
  2. 5.2 Supervised and Unsupervised Learning
  3. 5.3 Reinforcement Learning
  4. References
Part II: Design and Optimization for 5G Wireless Communications and Networks
1. 6 Design Principles for 5G Communications and Networks
  1. 6.1 New Design Approaches and Key Challenges of 5G Communications and Networks
  2. 6.2 5G New Radio
  3. 6.3 5G Key Enabling Techniques
  4. References
2. 7 Enhanced Mobile Broadband Communication Systems*
  1. 7.1 Introduction
  2. 7.2 Design Approaches of eMBB Systems
  3. 7.3 MIMO
  4. 7.4 5G Multiple Access Techniques
  5. 7.5 5G Channel Coding and Modulation
  6. Problems
  7. References
3. 8 Ultra‐Reliable and Low Latency Communication Systems
  1. 8.1 Design Approaches of URLLC Systems
  2. 8.2 Short Packet Transmission
  3. 8.3 Latency Analysis
  4. 8.4 Multi‐Access Edge Computing
  5. References
4. 9 Massive Machine Type Communication Systems
  1. 9.1 Introduction
  2. 9.2 Design Approaches of mMTC Systems
  3. 9.3 Robust Optimization
  4. 9.4 Power Control and Management
  5. 9.5 Wireless Sensor Networks
  6. References
Index
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Technical specifications of 1G cellular systems.
2. Table 1.2 Technical specifications of 2G cellular systems.
3. Table 1.3 Technical specifications of 3G cellular systems.
4. Table 1.4 Technical specifications of 4G cellular systems.
Chapter 2
1. Table 2.1 Traffic characteristics for mMTC city scenario [2].
2. Table 2.2 5G eMBB requirements [1].
Chapter 3
1. Table p3.1 Measurement data of signal strength, distance from base station (B...
2. Table p3.2 Joint probability mass function ofX and Y.
Chapter 4
1. Table p4.1 Initial simplex tableau for Example 4.5.
2. Table p4.2 First selection of pivot column and row.
3. Table p4.3 Second simplex tableau.
4. Table p4.4 Third simplex tableau.
5. Table p4.5 Final simplex tableau.
6. Table p4.6 Initial simplex tableau for Example 4.6.
7. Table p4.7 First selection of pivot column and row.
8. Table p4.8 Simplex tableau 2.
9. Table p4.9 Second selection of pivot column and row.
10. Table p4.10 Simplex tableau 3.
11. Table p4.11 Third selection of pivot column and row.
12. Table p4.12 Final simplex tableau.
Chapter 5
1. Table 5.1 Comparison of supervised learning and unsupervised learning.
2. Table p5.1 The first iteration for Example 5.3.
3. Table p5.2 The second iteration for Example 5.3.
4. Table p5.3 The third iteration for Example 5.3.
5. Table p5.4 The fourth iteration for Example 5.3.
6. Table p5.5 The first iteration for 5.4.
7. Table p5.6 The second iteration for 5.4.
8. Table 5.2 Comparison of TD learning, Q learning, and SARSA.
Chapter 6
1. Table 6.1 5G spectrum usages.
2. Table 6.2 5G NR numerology.
3. Table 6.3 5G NR RB configuration.
4. Table 6.4 5G NR logical, transport, and physical channels.
5. Table 6.5 5G NR physical signals.
6. Table 6.6 5G NR physical layer.
Chapter 7
1. Table 7.1 Approaches for increasing network throughput.
2. Table p7.1 OFDM system parameters.
3. Table p7.2 Simulation configuration for turbo codes.
4. Table p7.3 Simulation parameters for (8176, 7156) LDPC code.
Chapter 8
1. Table 8.1 Approaches for reducing the latency.
2. Table 8.2 User plane latency calculations for 5G and 4G FDD.
3. Table 8.3 User plane downlink latency analysis with 0% HARQ BLER [13].
4. Table 8.4 User plane uplink latency analysis with 0% HARQ BLER [13].
5. Table 8.5 User plane downlink latency analysis with 10% HARQ BLER [13].
6. Table 8.6 User plane uplink latency analysis with 0% HARQ BLER [13].
7. Table 8.7 Control plane latency calculation (Steps 1–10) of 4G FDD, based on ...
8. Table 8.8 Control plane latency calculation (Steps 11–17) of 4G FDD [13].
9. Table 8.9 Handover latency calculation based on Figure 8.12b [13].
Chapter 9
1. Table 9.1 Features of 3GPP IoT standards.
2. Table 9.2 NB‐IoT signals and channels.
3. Table 9.3 mMTC design approaches.
4. Table 9.4 Tractable robust counterparts of uncertain linear optimization prob...
5. Table 9.5 Data fusion techniques.
6. Table p9.1 Likelihood matrices of two sensors.
7. Table p9.2 Posterior probabilities of two sensors.

List of Illustrations

Chapter 1
1. Figure 1.1 Timeline of 3GPP 5G developments.
2. Figure 1.2 Evolution of cellular systems.
Chapter 2
1. Figure 2.1 5G requirements [8].
Chapter 3
1. Figure 3.1 Shannon's communication architecture [1].
2. Figure p3.1 Geometric interpretation of x, y, x − y, x/2, y/2, and (x − y)/2...
3. Figure 3.2 Properties of linear transformation: (a) multiplicativity and (b)...
4. Figure 3.3 Example of projection.
5. Figure 3.4 Approximation problem in ℝ ³ .
6. Figure 3.5 Comparison of the variance of the error term .
7. Figure p3.2 Comparison of the linear model and paired data points.
8. Figure p3.3 Comparison of the linear model and paired data points.
9. Figure 3.6 System model based on a discrete channel [4].
10. Figure 3.7 Optimum receiver model using Bayesian formula: (a) transmission a...
11. Figure p3.4 System model for Example 3.19.
12. Figure 3.8 ML vs MAP: (a) MAP = ML; and (b) MAP ≠ ML.
13. Figure 3.9 Concept of Householder transformation.
Chapter 4
1. Figure 4.1 Quadratic optimization.
2. Figure 4.2 Examples of possible optimum values when the problem is bounded: ...
3. Figure 4.3 Examples of possible optimum values when the problem is unbounded...
4. Figure 4.4 Examples of global and local minima and maxima when the problem i...
5. Figure p4.1 A simple model of signal‐to‐interference in two base stations.
6. Figure 4.5 The shaded area shows the feasible region in the linear programmi...
7. Figure p4.2 The shaded area shows the feasible region in Example 4.3.
8. Figure p4.3 The shaded area shows the feasible region in Example 4.4.
9. Figure 4.6 Three different cases: (a) a single optimal solution; (b) an infi...
10. Figure 4.7 Examples of (a) a convex function f ₁( x ); and (b) a non‐convex fun...
11. Figure 4.8 Two approaches to solving optimization problems.
12. Figure 4.9 Approach of the interior point method.
13. Figure p4.4 For Example 4.7, B(x) when γ = 1, 5, and 20.
14. Figure p4.5 Iteration of the gradient descent method.
15. Figure 4.10 Approach of the projected gradient method.
16. Figure p4.6 Scatterplot of the dataset for Example 4.11.
17. Figure p4.7 Objective function for Example 4.11.
18. Figure p4.8 Linear regression by gradient descent method for Example 4.11.
Chapter 5
1. Figure 5.1 Relationship between AI, machine learning and deep learning.
2. Figure 5.2 (a) Classification for supervised learning, and (b) clustering fo...
3. Figure 5.3 Hyperplane to classify training samples.
4. Figure 5.4 Examples of (a) a linearly separable sample, and (b) and (c) not ...
5. Figure 5.5 Example of hyperplane decision in multidimensional space.
6. Figure 5.6 Maximum margin and support vectors.
7. Figure 5.7 Trade‐off between margin and misclassified points.
8. Figure 5.8 Example of a non‐linear classification in (a) one dimension and (...
9. Figure p5.1 Dataset for Example 5.1.
10. Figure p5.2p5.2 Classifier for the Example 5.1 dataset.
11. Figure p5.3 Dataset for Example 5.2.
12. Figure p5.4 Classifier for the Example 5.2 dataset.
13. Figure 5.9 Examples of clustering using (a) Euclidean distance and (b) nonli...
14. Figure 5.10 Examples of (a) partitioning clustering and (b) hierarchical clu...
15. Figure p5.5 The 120 two‐dimensional data points for Example 5.5.
16. Figure p5.6 The first iteration for Example 5.5.
17. Figure p5.7 The second iteration for Example 5.5.
18. Figure p5.8 The third iteration for Example 5.5.
19. Figure p5.9 The fourth and final iteration for Example 5.5.
20. Figure 5.11 The interaction process of reinforcement learning.
21. Figure 5.12 Example of a Markov decision process (MDP).
22. Figure 5.13 Comparison of Q learning and SARSA [15].
23. Figure p5.10 Gridworld for Example 5.6.
24. Figure p5.11 For Example 5.6, state values (a) at (2,4) and (b) after the fi...
25. Figure p5.12 Gridworld for Example 5.7.
26. Figure p5.13 Initial condition of Q ( s , a ) for Example 5.7.
27. Figure p5.14 Q value updates when moving right for Example 5.7.
28. Figure p5.15 Gridworld for 5.8.
29. Figure p5.16 Optimal path for 5.8.
30. Figure p5.17 Gridworld example for Problem 5.20.
Chapter 6
1. Figure 6.1 5G numerology and slot length.
2. Figure 6.2 5G network slicing architecture.
3. Figure 6.3 Functional split between NG‐RAN and 5GC.
4. Figure 6.4 5G NR network architecture.
5. Figure 6.5 (a) 5G NSA and (b) SA.
6. Figure 6.6 Examples of (a) 5G NR slots and (b) a flexible configuration.
7. Figure 6.7 5G NR resource grid and resource blocks.
8. Figure 6.8 Example of 5G BWPs.
9. Figure 6.9 5G channel types.
10. Figure 6.10 5G SS/PBCH block configuration.
11. Figure 6.11 5G channel mapping for (a) downlink and (b) uplink.
12. Figure 6.12 5G protocol stack for (a) user plane and (b) control plane.
13. Figure 6.13 UE state machine and transitions in NR and between NR and E‐UTRA...
14. Figure 6.14 Example of 5G packet segmentation and reassembly.
15. Figure 6.15 5G NR physical channel processing for PDSCH.
16. Figure 6.16 5G NR physical channel processing for PDCCH.
17. Figure 6.17 Example of PDCCH CORESET.
18. Figure 6.18 5G NR physical channel processing for PUSCH.
19. Figure 6.19 5G initial access procedure and beam management.
20. Figure 6.20 OFDM‐based 5G waveform.
21. Figure 6.21 Comparison of (a) OMA and (b) NOMA.
22. Figure 6.22 Polar encoding for 5G NR.
23. Figure 6.23 LDPC encoding for 5G NR.
24. Figure 6.24 Antenna array architecture for (a) digital beamforming, (b) anal...
25. Figure 6.25 3GPP network slicing architecture.
26. Figure 6.26 Comparison of (a) MEC and (b) fog computing.
27. Figure 6.27 MEC deployment in a 5G network.
Chapter 7
1. Figure 7.1 Euler diagram for P, NP, NP complete, and NP hard problems [1].
2. Figure 7.2 Bandwidth vs throughput.
3. Figure 7.3 Spectral efficiency comparison of different MIMO techniques.
4. Figure 7.4 Point‐to‐point MIMO channel.
5. Figure 7.5 Point‐to‐point MIMO channel conversion through SVD.
6. Figure p7.1 Capacity of 2 × 2 MIMO channel for Example 7.2.
7. Figure 7.6 N _t × N _t MIMO channel for space–time block coding.
8. Figure p7.2 Performance comparison of 2 × 2 and 2 × 1 Alamouti scheme and si...
9. Figure p7.3 Performance comparison of 4 × 1 OSTBC and single antenna system....
10. Figure 7.7 Space–time trellis encoder.
11. Figure p7.4 Example of STTC encoder.
12. Figure p7.5 Trellis diagram for STTC with four states, QPSK and two transmit...
13. Figure p7.6 QPSK signal constellation and mapping.
14. Figure p7.7 Paths diverging at time t ₁ and remerging at time t ₂.
15. Figure 7.8 D‐BLAST and V‐BLAST transmitter and data sequences.
16. Figure 7.9 MIMO detection algorithms.
17. Figure 7.10 MIMO system for spatial multiplexing.
18. Figure p7.8 2 × 2 MIMO system for spatial multiplexing.
19. Figure p7.9 Performance comparison of MIMO detection algorithms (ML, MMSE‐SI...
20. Figure p7.10 Performance comparison of massive MIMO (transmit antennas = 32,...
21. Figure 7.11 Spectrum comparison of (a) OFDM and (b) FBMC with eight subcarri...
22. Figure 7.12 5G multiple access techniques.
23. Figure 7.13 FDM with three carriers.
24. Figure 7.14 OFDM with three subcarriers.
25. Figure 7.15 OFDM transmitter with N parallel data sequences.
26. Figure 7.16 OFDM transmitter using IFFT/IDFT.
27. Figure 7.17 Up‐conversion from the baseband signal to the passband signal.
28. Figure 7.18 Down‐conversion from the passband signal to the baseband signal....
29. Figure 7.19 Conventional OFDM‐based communication system.
30. Figure p7.11 Subcarrier allocation in the OFDM symbol for Example 7.8.
31. Figure 7.20 FBMC transmitter and receiver.
32. Figure 7.21 GFDM transmitter and receiver.
33. Figure 7.22 UFMC transmitter and receiver.
34. Figure p7.12 Comparison of (a) OFDM with 200 subcarriers and (b) UFMC with 5...
35. Figure 7.23 Example of a Tanner graph.
36. Figure 7.24 Tanner graph example for LDPC decoding.
37. Figure 7.25 Two messages in the Tanner graph.
38. Figure 7.26 Examples of (a) bit node and (b) check node message passing in t...
39. Figure 7.27 (a) Original turbo encoder and (b) duo‐binary turbo encoder.
40. Figure p7.13 Turbo code BER performances with frame size (a) 1024, (b) 2048,...
41. Figure p7.14 FER performance of duo‐binary turbo codes, for Example 7.13.
42. Figure 7.28 Tanner graph and parity check matrix of (a) binary LDPC code and...
43. Figure p7.15 BER performance of (8176, 7156) LDPC code for Example 7.14.
44. Figure p7.16 Spectral efficiency comparison of non‐binary (2,4) LDPC codes o...
45. Figure p7.17 Throughputs of ACM for Example 7.16.
Chapter 8
1. Figure 8.1 Trade‐off of error probability and code rate.
2. Figure 8.2 Vertical and horizontal asymptotic view.
3. Figure 8.3 Communication model.
4. Figure 8.4 Normal approximation as a function of the block length.
5. Figure 8.5 Converse bound and achievability bound (≈ normal approximation)....
6. Figure p8.1 Converse bound and achievability bound (≈ normal approximation)....
7. Figure 8.6 Optimal error probability in terms of the information bits.
8. Figure 8.7 Memoryless block fading model.
9. Figure 8.8 Delay model for (a) uplink and (b) downlink.
10. Figure 8.9 HARQ latency model for FDD.
11. Figure 8.10 HARQ latency model for (a) TDD downlink and (b) uplink.
12. Figure 8.11 Delay model for the control plane from idle state to connected s...
13. Figure 8.12 4G handover procedure: (a) preparation, (b) execution, and (c) c...
14. Figure 8.13 The hourglass model of layered system architecture.
15. Figure 8.14 MEC system model for task scheduling.
Chapter 9
1. Figure 9.1 (a) Downlink and (b) uplink frame structure of NB‐IoT.
2. Figure 9.2 NB‐IoT connection establishment.
3. Figure 9.3 Robust feasible set vs nominal feasible set.
4. Figure 9.4 The concept of the uncertainty set.
5. Figure p9.1 The constraints of the problem in Example 9.1.
6. Figure p9.2 The ellipsoidal uncertainty set for Example 9.2.
7. Figure 9.5 The shaded area satisfying the first constraint.
8. Figure 9.6 The shaded area satisfying the second constraint.
9. Figure 9.7 The shaded area satisfying both constraints.
10. Figure 9.8 The shaded areas satisfying (a) the first constraint and (b) the ...
11. Figure 9.9 Transmit and receive beamforming.
12. Figure 9.10 JDL data fusion model.
13. Figure 9.11 Architectures of data fusion: (a) centralized architecture, (b) ...
14. Figure 9.12 Examples of (a) directed and (b) undirected graphs.
15. Figure p9.3 Graph model for the sensor network in Example 9.6.
16. Figure p9.4 Evolution of the states in Example 9.6.
17. Figure 9.13 Sensor network with parallel topology.
18. Figure 9.14 System model for distributed estimation.

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This edition first published 2020

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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 Kim
VTT Oulu, Finland

List of Abbreviations

1G: first generation
2G: second generation
3G: third generation
3GPP: Third Generation Partnership Project
4G: fourth generation
5G: fifth generation
5GC: 5G core
ACK: acknowledge
ACLR: adjacent channel leakage ratio
ACM: adaptive coding and modulation
ADSL: asymmetric digital subscriber line
AI: artificial intelligence
AMF: access and mobility management function
AMPS: Advanced Mobile Phone Service
APP: a posteriori probability
AR: augmented reality
ARFCN: Absolute Radio Frequency Channel Number
ARO: adjustable robust optimization
ARQ: automatic repeat request
AS: 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
BP: 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
CN: core network
CORESET: configurable control resource set
CP: convex optimization problems
CP: 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
DL: 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
FA: 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
FM: frequency modulation
FONC: first‐order necessary condition
GF: 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
LP: linear programming
LPWAN: lower power wide area network
LS: least squares
LTE: Long Term Evolution
LU: lower upper
M2M: machine‐to‐machine
MAC: medium access control
MAP: maximum a posteriori
MCG: master cell group
MD: missed detection
MDP: Markov decision problem/process
MEC: multi‐access edge computing
MF: matched filter
MIB: master information block
MIMO: multiple input multiple output
ML: 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
NP: 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
NR: 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
QP: quadratic programming
QPSK: quadrature phase shift keying
RACH: random access channel
RAN: radio access network
RB: resource block
REG: resource element group
RF: radio frequency
RL: reinforcement learning
RLC: radio link control
RO: robust optimization
RRC: radio resource control
RRU: remote radio unit
RS: Reed‐Solomon
RTT: round trip time
SA: 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
SE: 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
SN: 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
TD: temporal difference
TDD: time division duplexing
TDMA: time division multiple access
TM: transmission mode
TN: transport network
TRxP: transmission reception point
TTI: transmission time interval
UE: user equipment
UFMC: universal filtered multicarrier
UHD: ultra‐high definition
UL: 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
VR: virtual reality
WAP: wireless application protocol
WGN: white Gaussian noise
WSN: wireless sensor network
ZF: zero forcing
ZP: zero padding