Resource optimization in wireless communications : fundamentals, algorithms, and applications /

Resource Optimization in Wireless Communications: Fundamentals, Algorithms, and Applications provides an easy-to-understand overview of the fundamentals of resource optimization, along with the latest algorithms and applications for emerging 5G, and beyond, wireless systems offering a variety of ser...

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Bibliographic Details
Main Author:	Yang, Lie-Liang
Corporate Author:	ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	London : Elsevier Academic Press, 2025.
Subjects:	Wireless communication systems. Radio resource management (Wireless communications) Transmission sans fil. Ressources radio > Gestion. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

6.5.1 Beam allocation with greedy approach
6.5.2 Beam allocation with particle swarm optimization
6.6 Beam allocations in CoMP HBF MIMO networks
6.6.1 System model
6.6.2 Signal model
6.6.3 UE capacity evaluation
6.6.4 Problem formulation and greedy algorithm
6.6.5 Simulation results
6.7 Summary
7 Game-theory-assisted resource allocation
7.1 Introduction
7.1.1 Fundamental elements of a matching game
7.1.2 Fundamental elements of a coalitional game
7.2 Functional split networks
7.2.1 Problem description
7.2.2 Beam-allocation problem as a matching game
7.2.3 Beam-allocation problem as a coalitional game
7.3 In-band full-duplex networks
7.3.1 Problem description
7.3.2 Resource-block-allocation problem as a matching game
7.3.3 Resource-block-allocation problem as a coalitional game
7.4 Conclusion and open issues
8 Machine/deep-learning-based resource allocation
8.1 Introduction
8.2 Deep-learning-based resource allocation
8.3 Deep learning in millimeter wave communications
8.3.1 Introduction to millimeter wave
8.3.2 Beamforming-training problem
8.4 Deep-neural-network-based resource allocation
8.4.1 Forward propagation for a neural network
8.4.1.1 CNN layers: feature-map extraction
8.4.1.2 Example of CNN
8.4.1.3 DNN layers: training-beam prediction
8.4.1.4 Example of DNN
8.4.2 Backward propagation for a neural network
8.4.3 Activation function
8.4.4 Batch normalization
8.5 Reinforcement-learning-based resource allocation
8.5.1 Q-learning-based resource allocation
8.5.2 Example of Q-learning
8.6 Deep-reinforcement-learning-based resource allocation
8.7 Conclusion and open issues
9 Deep-reinforcement-learning-aided resource allocation in ultradense networks
9.1 Introduction
9.2 Modeling of a UDN.
9.3 Resource-allocation problem formulation and analysis
9.4 DNN-based optimization
9.5 CIAQ algorithm
9.5.1 Design objectives of the CIAQ algorithm
9.5.2 Principles of the CIAQ algorithm
9.6 Performance results
9.7 Summary
10 Resource allocation in multicell OFDMA systems
10.1 Introduction
10.2 Modeling of a multicell OFDMA system
10.3 Formulation of optimization problem for ICI-mitigation-oriented resource allocation
10.4 FIIDM and OOP algorithms
10.4.1 FIIDM algorithm
10.4.2 OOP algorithm
10.5 DDMC algorithm
10.6 CDMC algorithm
10.7 Extension
10.8 Performance results
10.9 Summary
11 Distributed resource allocation in multicell MC/DS-CDMA systems
11.1 Introduction
11.2 Modeling of a multicell MC/DS-CDMA system
11.3 Problem formulation and analysis
11.4 Benchmark resource allocation with ICI mitigation
11.4.1 Benchmark of distributed subcarrier allocation (benchmark-SA)
11.4.2 Benchmark of distributed code allocation (benchmark-CA)
11.4.3 Benchmark intercell interference mitigation (benchmark-IM)
11.5 RAIM scheme
11.5.1 RAIM: subcarrier allocation (RAIM-SA)
11.5.2 RAIM: code allocation (RAIM-CA)
11.5.3 RAIM: ICI mitigation (RAIM-IM)
11.6 Characteristics and complexity analysis of RAIM
11.6.1 Characteristic analysis
11.6.2 Complexity analysis
11.7 Performance results
11.8 Summary
12 Resource allocation for ultrareliable low-latency communications
12.1 Introduction
12.2 System model and problem formulation
12.2.1 Delay analysis
12.2.2 Problem formulation
12.3 The optimal FSM decision (OFD) scheme
12.3.1 Feasible set reduction (FSR)
12.3.2 Globally optimal solution search (GOSS)
12.4 Performance evaluation
12.5 Summary
13 Resource allocation in massive machine-type communications
13.1 Introduction.
13.2 Access control for mMTC
13.2.1 Access class barring
13.2.1.1 ACB factor with traffic loads
13.2.1.2 ACB factor with reinforcement learning
13.2.1.3 ACB factor with delay
13.2.2 Group-based access control
13.2.3 Clustering algorithms
13.2.4 HetNet access control
13.2.4.1 User association of both type of cells
13.2.4.2 User association of specific type of cells
13.2.4.3 User association of UDN scenario
13.3 Resource allocation in mMTC
13.3.1 Orthogonal and nonorthogonal resource allocation
13.3.2 Resource allocation for PRACH and PUSCH
13.3.3 Other advanced methods
13.4 Energy management in mMTC
13.4.1 General principles
13.4.2 Offloading mechanism
13.4.3 UAV-assisted networks
13.5 Advanced techniques in 5G NR
13.5.1 Power-saving techniques in 5G
13.5.2 Other techniques for power saving
13.6 Summary
14 Resource optimization in reflective intelligent surface (RIS)-aided mmWave systems
14.1 Introduction
14.2 A conceptual example
14.3 RIS-mmWave: single-RIS single user
14.4 RIS-mmWave: single-RIS multiple users
14.5 RIS-mmWave: multiple-RISs multiple users
14.6 Concluding remarks
15 Signaling and optimization in orthogonal time-frequency-space (OTFS) and related schemes
15.1 Introduction
15.2 Principles of OTFS
15.3 Relationship of OTFS with OSTF, OFDM, and SC-FDMA
15.4 Multiuser multiplexing and optimization in OTFS and OSTF systems
15.4.1 Uplink
15.4.2 Downlink
15.5 Concluding remarks
16 Optimization in MIMO integrated sensing and communications (ISAC)
16.1 Introduction
16.2 Fundamentals of MIMO communications and MIMO sensing
16.2.1 MIMO communications
16.2.2 MIMO sensing
16.3 Resource optimization in MIMO ISAC systems
16.4 ISAC mmWave systems
16.5 Concluding remarks.