Flexible and Cognitive Radio Access Technologies for 5G and Beyond (Telecommunications)

Cover
Contents
About the editors
Foreword
List of acronyms
Part I. Waveform design: an overview
	1 Introduction to waveform design
		1.1 Introduction
		1.2 The generalized definition of a waveform
		1.3 Relationships of channel and RF impairments with a waveform
		1.4 Application requirements of cellular use cases and wireless fidelity (Wi-Fi) standards
			1.4.1 Cellular communications use cases
			1.4.2 Wi-Fi communications standards
		1.5 Impact of the waveform design on RATs
			1.5.1 Limitations and challenges for RATs
			1.5.2 Performance indicators for the waveform design
			1.5.3 Waveform design guidelines for RATs
		1.6 An example of waveform frame: 5G NR standardization
			1.6.1 Reference documents for 3GPP
			1.6.2 Numerology structures
			1.6.3 Bandwidth part issues
			1.6.4 Slot structures
			1.6.5 Comparison for building blocks of 5G NR and LTE
		1.7 Conclusion
		References
	2 OFDM and alternative waveforms
		2.1 Introduction
		2.2 The baseline for waveform discussion: CP-OFDM
			2.2.1 Key features
			2.2.2 Performance in multipath channel
				2.2.2.1 Time-dispersive multipath channel
				2.2.2.2 Frequency-dispersive multipath channel
			2.2.3 Performance with impairments
				2.2.3.1 Frequency offset
				2.2.3.2 Symbol timing error
				2.2.3.3 Sampling clock offset
				2.2.3.4 Phase noise
				2.2.3.5 PA nonlinearities
				2.2.3.6 IQ impairments
		2.3 Alternative waveforms
			2.3.1 Multicarrier schemes
				2.3.1.1 Windowed-orthogonal frequency division multiplexing
				2.3.1.2 Filter bank multicarrier
				2.3.1.3 Generalized frequency division multiplexing
				2.3.1.4 Universal filtered multicarrier
				2.3.1.5 Filtered-orthogonal frequency division multiplexing
			2.3.2 Single-carrier schemes
				2.3.2.1 CP-DFT-s-OFDM
				2.3.2.2 ZT-DFT-s-OFDM
				2.3.2.3 UW-DFT-s-OFDM
		2.4 Discussion
		2.5 Conclusion
		References
	3 Mixed numerology OFDM and interference issues
		3.1 Introduction
		3.2 Mixed numerology multiplexing
			3.2.1 Frequency domain
			3.2.2 Time domain
		3.3 Inter-numerology interference modeling
		3.4 Factors affecting INI
			3.4.1 Subcarrier spacing ratio, Q
			3.4.2 Power offset
			3.4.3 Channel response
		3.5 INI management
			3.5.1 Restructuring INI through common CP
				3.5.1.1 INI analysis with common CP
			3.5.2 INI-aware scheduling
			3.5.3 INI-aware guard band allocation
		3.6 Asynchronicity in the mixed numerology frame
		3.7 Mixed numerology in single-carrier schemes
		3.8 Summary
		References
Part II. Flexible waveform and modulation options for beyond 5G
	4 Flexibility through hybrid waveforms
		4.1 Introduction
		4.2 Improved OFDM-based flexible structures for beyond 5G applications
			4.2.1 Spectrally localized OFDM
				4.2.1.1 Adaptive symbol transitioned OFDM
				4.2.1.2 Precoded OFDM
				4.2.1.3 Partial transmit sequenced OFDM
				4.2.1.4 OFDM with alignment signals
			4.2.2 Secure OFDM
			4.2.3 Beyond spectral localization: partially overlapping waveforms
		4.3 Waveform multiplexing approaches for beyond 5G RATs
			4.3.1 Time-domain OFDM numerology multiplexing
			4.3.2 FDM of OFDM numerologies against hybrid waveforms
		4.4 Numerology-based scheduling
		4.5 Conclusion
		Acknowledgment
		References
	5 Generalized and flexible modulation options
		5.1 Introduction
		5.2 The relation between modulation and waveform in communication systems
		5.3 Flexibility in modulation design
		5.4 Classifications of the modulation options for 5G and beyond waveforms
			5.4.1 Conventional and differential digital modulations for OFDM-based waveform
			5.4.2 Multi-dimensional modulation options for OFDM-based waveform
		5.5 Index-based modulation options
			5.5.1 SM-OFDM scheme
			5.5.2 OFDM-IM scheme
		5.6 Number-based modulation options
		5.7 Shape-based modulation options
		5.8 Performance evaluation and comparison of modulation options in practical conditions
			5.8.1 Spectral efficiency
			5.8.2 Reliability
			5.8.3 PAPR and power efficiency
			5.8.4 Out-of-band leakage
			5.8.5 Computational complexity
		5.9 Applications of the featured modulation options for 5G and beyond networks
		5.10 Other potential flexible modulation options for OFDM-based waveforms
		5.11 Futuristic modulation options for beyond 5G
		5.12 Conclusion
		References
	6 Index modulation-based flexible waveform design
		6.1 Introduction
		6.2 Index modulation in frequency domain: OFDM with index modulation
			6.2.1 Maximum likelihood detector
			6.2.2 Log-likelihood ratio detector
		6.3 State-of-the-art OFDM-IM solutions
			6.3.1 Interleaved OFDM-IM
			6.3.2 Generalized OFDM-IM
			6.3.3 Dual-mode OFDM
			6.3.4 Coordinate interleaved OFDM-IM
		6.4 Flexible OFDM with IM
			6.4.1 Subcarrier mapping scheme
				6.4.1.1 Equal bit protection
				6.4.1.2 Robustness against asynchronous transmission
				6.4.1.3 Avoiding deep fading
			6.4.2 Subcarrier activation ratio
				6.4.2.1 Avoiding Doppler spread
				6.4.2.2 Robustness against hardware imperfection
				6.4.2.3 Securing communication link
		6.5 Discussions and future directions
		6.6 Conclusion
		Acknowledgment
		References
Part III. Multiple antenna systems for 5G and beyond
	7 Massive MIMO for 5G and beyond
		7.1 Introduction of massive MIMO
		7.2 Information theory of massive MIMO
			7.2.1 Fundamental of massive MIMO
			7.2.2 Spectrum efficiency analysis of massive MIMO
				7.2.2.1 Perfect CSI
				7.2.2.2 Imperfect CSI
		7.3 Channel models for massive MIMO
			7.3.1 Correlation-based channel model
			7.3.2 Spatial channel model
		7.4 Signal detection for massive MIMO
			7.4.1 System model and MMSE detection
			7.4.2 Neumann sequence-based signal detection
			7.4.3 Iteration-based signal detection
		7.5 CSI acquisition for massive MIMO
			7.5.1 Channel estimation for massive MIMO
				7.5.1.1 Channel sparsity-based channel estimation
				7.5.1.2 Channel correlation-based channel estimation
			7.5.2 Channel feedback for massive MIMO
				7.5.2.1 Channel sparsity-based channel feedback
				7.5.2.2 Channel correlation-based channel feedback
				7.5.2.3 Channel partial reciprocity-based channel feedback
		7.6 Precoding for massive MIMO
			7.6.1 Digital precoding
				7.6.1.1 Single-user digital precoding
				7.6.1.2 Multiuser digital precoding
			7.6.2 Analog beamforming
			7.6.3 Hybrid precoding
				7.6.3.1 Single-user hybrid precoding
				7.6.3.2 Multiuser hybrid precoding
		7.7 Prototype and testbeds for massive MIMO
		7.8 Challenges and future research directions for massive MIMO
			7.8.1 Physical layer signal processing in wideband massive MIMO
			7.8.2 THz massive MIMO
			7.8.3 RIS-based massive MIMO
		7.9 Summary of the key points for massive MIMO
		References
	8 Beamforming and beam management in 5G and beyond
		8.1 Introduction
		8.2 Evolution of beamforming
		8.3 Beamforming in mmWave frequencies
			8.3.1 Analog/digital beamforming
				8.3.1.1 Analog beamforming
				8.3.1.2 Digital beamforming
			8.3.2 Hybrid beamforming
				8.3.2.1 Fully connected hybrid beamforming
				8.3.2.2 Sub-connected hybrid beamforming
			8.3.3 Beampattern adaptation
			8.3.4 Lens antenna for beamforming
		8.4 Beam management
			8.4.1 Beam management classes
				8.4.1.1 Non-standalone architecture
				8.4.1.2 Standalone architecture
			8.4.2 Beam switching
			8.4.3 Beam tracking
			8.4.4 Security-oriented beamforming techniques
		8.5 Challenges and future concepts
			8.5.1 Pilot contamination in mmWave frequencies
			8.5.2 Multi-lens antenna beamforming systems
			8.5.3 IRS-based beamforming
		8.6 Conclusion
		References
	9 Spatial modulation techniques for beyond 5G
		9.1 Basic principle and variants of SM
			9.1.1 Single-RF SM
			9.1.2 Generalized SM
			9.1.3 Differential SM
			9.1.4 Receive SM
		9.2 Performance enhancement for SM
			9.2.1 Link-adaptive SM
			9.2.2 Precoding/TCM-aided SM
			9.2.3 Transmit-diversity-enhanced SM
		9.3 Generalized SM integration with other promising technologies
			9.3.1 Compressed-sensing (CS) theory for SM
			9.3.2 Non-orthogonal multiple access (NOMA)-aided SM
			9.3.3 Security provisioning in SM
		9.4 Applications of SM to emerging communication systems
			9.4.1 SM in mmWave communications
			9.4.2 SM in optical wireless communications
			9.4.3 SM-based simultaneous wireless information and power transfer
			9.4.4 SM-based molecular communication
		9.5 Conclusions
		References
	10 Beyond massive MIMO: reconfigurable intelligent surface-assisted wireless communications
		10.1 Introduction
		10.2 Controllable wireless propagation: two illustrative examples
			10.2.1 Two-ray propagation with RISs
			10.2.2 Eliminating Doppler effects with RISs
		10.3 A brief literature survey
		10.4 Potential use-cases
		10.5 Conclusions and future perspectives
		Acknowledgment
		References
Part IV. Channel modeling and new frequency bands
	11 Channel modeling for 5G and beyond
		11.1 Introduction
			11.1.1 What defines a good channel model for 5G and B5G?
		11.2 Evolution of radio frequency channel models before 5G
			11.2.1 Analytical channel models
				11.2.1.1 Correlation-based models
				11.2.1.2 Propagation-motivated models
			11.2.2 Physical channel models
				11.2.2.1 Geometry-based stochastic models
				11.2.2.2 Non-geometry-based stochastic models
				11.2.2.3 Deterministic models
			11.2.3 Standardized channel models
				11.2.3.1 The COST channel models (259 and 273)
				11.2.3.2 The multidimensional parametric channel model
				11.2.3.3 The 3GPP spatial channel model
				11.2.3.4 The WINNER channel model
				11.2.3.5 IMT-advanced channel models from ITU
		11.3 Channel models for 5G and beyond
			11.3.1 Enhanced 3GPP channel models
			11.3.2 The MiWEBA channel model
			11.3.3 METIS channel models
				11.3.3.1 The METIS map-based model
				11.3.3.2 The METIS stochastic model
				11.3.3.3 The METIS hybrid model
			11.3.4 The QuaDRiGa/mmMAGIC channel model
			11.3.5 The IEEE 802.11ay channel model
			11.3.6 The IMT-2020 channel model
			11.3.7 The NYUSIM channel model
		11.4 Machine learning-based channel modeling for 5G and B5G
		11.5 Channel sparsity and compressed modeling in 5G and B5G
			11.5.1 Pilot reduction through compressive channel sampling
			11.5.2 Channel sparsity aspects in 5G and B5G
			11.5.3 Outstanding challenges and questions
		11.6 Conclusion
		Acknowledgment
		References
	12 On the advances of terahertz communication for 5G and beyond wireless networks
		12.1 Introduction
		12.2 Application scenarios
			12.2.1 Fronthaul and backhaul links
			12.2.2 Nano devices
			12.2.3 Entertainment technologies and augmented reality
			12.2.4 Heterogeneous networks
		12.3 Challenges and solutions
			12.3.1 Transceivers design in terahertz band
				12.3.1.1 Amplifiers
			12.3.2 Channel and noise modeling
				12.3.2.1 Channel
				12.3.2.2 Molecular absorption noise and loss
			12.3.3 Physical layer
				12.3.3.1 Modulation schemes
				12.3.3.2 Channel codes
				12.3.3.3 MIMO systems
				12.3.3.4 Medium access control
				12.3.3.5 Synchronization
		12.4 Achieved data rates
		12.5 Modeling the wireless propagation channel for terahertz band: a case study for 240–300 GHz
			12.5.1 Description of measurement setup
				12.5.1.1 Measurement methodology
			12.5.2 Measurement results
		12.6 Conclusion and future directions
		References
	13 Visible light communication for 5G and beyond
		13.1 Introduction
		13.2 Standardization activities
		13.3 System design
			13.3.1 Channel modeling
				13.3.1.1 Indoor light propagation
				13.3.1.2 LOS and NLOS channel models
				13.3.1.3 Channel parameters
			13.3.2 Optical modulation schemes
				13.3.2.1 Carrierless modulation schemes
				13.3.2.2 Single-carrier modulation schemes
				13.3.2.3 Multi-carrier modulation schemes
				13.3.2.4 Multicolor modulation schemes
			13.3.3 Medium access control
		13.4 Integrated visible light communication systems
			13.4.1 Integration of IR and VLC
			13.4.2 Integration of RF and VLC
			13.4.3 Integration of PLC and VLC
			13.4.4 Integration of VLC in 5G networks
		13.5 Applications ofVLC in 5G and beyond
			13.5.1 Indoor
				13.5.1.1 Hospitals
				13.5.1.2 Industries
				13.5.1.3 Data centers
				13.5.1.4 Secure communication
			13.5.2 Outdoor
			13.5.3 Underwater
			13.5.4 Underground
		13.6 Summary
		References
Part V. Coexistence, interference and radio resource management
	14 Coordinated networks: past, present and future
		14.1 Coordination in legacy networks
			14.1.1 Frequency reuse
			14.1.2 Intercell interference coordination
			14.1.3 Enhanced intercell interference coordination
			14.1.4 CoMP and its essentials
				14.1.4.1 CoMP architecture
				14.1.4.2 CoMP types
				14.1.4.3 CoMP scenarios
			14.1.5 CoMP implementation
				14.1.5.1 User selection and resource allocation
				14.1.5.2 Clustering
				14.1.5.3 Reference signals and interference measurement
		14.2 Coordination in 5G networks
			14.2.1 Throughput
			14.2.2 Reliability and latency
			14.2.3 Coverage
			14.2.4 Mobility
			14.2.5 Spectral efficiency
			14.2.6 Energy efficiency
		14.3 Coordination for future wireless networks
			14.3.1 Network architecture
				14.3.1.1 Cloud-based radio access network
				14.3.1.2 Fog-RAN
			14.3.2 Smart radio environment
			14.3.3 Communication technologies and standards
			14.3.4 Application and user requirements
		14.4 Challenges for future coordinated networks
			14.4.1 Synchronization/timing advance
			14.4.2 Functionality split
			14.4.3 Backhaul issues
			14.4.4 Performance analysis
		14.5 Conclusion
		Acknowledgments
		References
	15 Non-orthogonal radio access technologies
		15.1 Introduction
		15.2 Non-orthogonal multiple accessing in power domain
			15.2.1 Downlink PD-NOMA
			15.2.2 Uplink PD-NOMA
			15.2.3 Capacity in PD-NOMA
			15.2.4 Fairness in PD-NOMA
		15.3 State-of-the-art NOMA solutions
			15.3.1 Low-density spreading orthogonal frequency division multiple access
			15.3.2 Pattern division multiple access
			15.3.3 Index modulation in NOMA
		15.4 Grant-free random access techniques
			15.4.1 Transmission schemes
				15.4.1.1 Reactive transmission
				15.4.1.2 K-Repetitions
				15.4.1.3 Proactive transmission
			15.4.2 Adaptive resource utilization
		15.5 Waveform coexistence for multiple accessing
			15.5.1 Wideband and narrowband signals
			15.5.2 OFDM with OFDM-IM
			15.5.3 OFDM with multi-numerology
		15.6 Discussions and future directions
		15.7 Conclusion and remarks
		Acknowledgment
		References
	16 Cognitive radio spectrum sensing: from conventional approaches to machine-learning-based predictive techniques
		16.1 Introduction
		16.2 A brief description of cognitive radio concept
			16.2.1 Spectrum sensing
			16.2.2 Spectrum decision
			16.2.3 Spectrum sharing
			16.2.4 Spectrum mobility
		16.3 Traditional spectrum-sensing techniques
			16.3.1 Narrowband spectrum sensing
				16.3.1.1 Methodologies
				16.3.1.2 Limitations
			16.3.2 Wideband spectrum sensing
				16.3.2.1 Methodologies
				16.3.2.2 Limitations
		16.4 Predictive spectrum-sensing approach
			16.4.1 Employed machine-learning methodologies
				16.4.1.1 Hidden Markov models
				16.4.1.2 Artificial neural networks
				16.4.1.3 Deep learning
			16.4.2 State-of-the-art
		16.5 QoS-aware dynamic spectrum access techniques
			16.5.1 Performance evaluation
		16.6 Conclusion
		References
	17 Deep learning and federate learning toward 6G mobile communications
		17.1 Introduction to machine learning and deep learning
		17.2 Deep learning for wireless communication systems and networks
			17.2.1 Artificial neural network basics
			17.2.2 Data-driven prediction using deep learning
				17.2.2.1 Taxi trajectory data set
				17.2.2.2 Meteorology data set
				17.2.2.3 Points of interest data set
				17.2.2.4 Spatial prediction of trip demand
				17.2.2.5 Temporal prediction and multivariate long and short term memory model
				17.2.2.6 TheAE model
				17.2.2.7 The MLSTM model
				17.2.2.8 Method to prevent overfitting
			17.2.3 Deep learning for signal detection in digital communication systems
			17.2.4 Future network architect of machine learning
				17.2.4.1 Machine learning in mobile communication networks
				17.2.4.2 Networked multi-agent systems
		17.3 Federate learning over wireless communications
			17.3.1 Federated learning basics
			17.3.2 Federated learning through wireless communications
			17.3.3 Federated learning over wireless networks
			17.3.4 Federated learning over multiple access communications
		17.4 Spectrum map in cognitive radio networks by statistical inference and learning
		17.5 Conclusions
		References
Part VI. Securing wireless communication
	18 Physical layer security designs for 5G and beyond
		18.1 Introduction and motivation
		18.2 Fundamentals, preliminaries, and basic system model for PLS
		18.3 Secrecy notions and performance metrics
			18.3.1 Secrecy notions
				18.3.1.1 Perfect secrecy
				18.3.1.2 Strong secrecy
				18.3.1.3 Weak secrecy
				18.3.1.4 Semantic secrecy
				18.3.1.5 Ideal secrecy
			18.3.2 Secrecy performance metrics
		18.4 Popular security techniques
			18.4.1 PLS based on secure channel coding design
				18.4.1.1 Concepts, merits, and demerits
				18.4.1.2 Learned lessons
			18.4.2 Channel-based adaptation and optimization for PLS
				18.4.2.1 Concepts, merits, and demerits
				18.4.2.2 Examples in time, frequency, and space domains
				18.4.2.3 Learned lessons
			18.4.3 Addition of artificially interfering (noise/jamming) signals for PLS
				18.4.3.1 Concepts, merits, and demerits
				18.4.3.2 Examples in time, frequency, and space domains
				18.4.3.3 Learned lessons
			18.4.4 Extraction of secret sequences from wireless channels
				18.4.4.1 Concepts, merits, and demerits
				18.4.4.2 Examples in time, frequency, and space
				18.4.4.3 Learned lessons
		18.5 Applications of PLS in emerging technologies
			18.5.1 PLS in mmWave
			18.5.2 PLS in mMIMO
			18.5.3 PLS in URLLC
			18.5.4 PLS in IoT
			18.5.5 PLS in UAV
			18.5.6 PLS in CR systems
		18.6 PHY-authentication against spoofing attacks
			18.6.1 Channel-based PHY-authentication
			18.6.2 AFE-based PHY-authentication
			18.6.3 Reliability of PHY-authentication algorithms
			18.6.4 Efficient and fast authentication in complex heterogeneous networks
			18.6.5 Integration with the existing network infrastructure and authentication protocols
		18.7 Wireless jamming attacks and countermeasures
			18.7.1 Wireless jamming attacks: a brief summary
			18.7.2 Wireless jamming attacks, detection, and solutions
				18.7.2.1 Constant jammer
				18.7.2.2 Intermittent jammer
				18.7.2.3 Reactive jammer
				18.7.2.4 Adaptive jammer
				18.7.2.5 Intelligent jammer
		18.8 Challenges and future research directions
			18.8.1 Secrecy design based on service requirements
			18.8.2 Cross-layer security design
			18.8.3 PAPR of AN-based and precoding security techniques
			18.8.4 Security in LOS environment
			18.8.5 Robust channel estimation and channel reciprocity calibration
			18.8.6 Joint design of secrecy, throughput, delay, and reliability
			18.8.7 Hybrid security techniques
			18.8.8 Impersonation attacks
			18.8.9 Challenges related to solution against jamming attacks
			18.8.10 Mixed attacks in wireless networks and cognitive security
			18.8.11 A new direction for PLS
		18.9 Conclusion
		Acknowledgments
		References
	19 Physical layer security for NOMA systems
		19.1 Introduction
		19.2 Fundamentals of NOMA
			19.2.1 Downlink NOMA
			19.2.2 Uplink NOMA
		19.3 Fundamentals of PLS
			19.3.1 Information-theoretic secrecy
			19.3.2 Metrics
				19.3.2.1 Ergodic secrecy capacity
				19.3.2.2 Secrecy outage probability
		19.4 PLS-enhanced NOMA
			19.4.1 PLS in SISO–NOMA systems
			19.4.2 PLS in MIMO–NOMA systems
			19.4.3 PLS in massive MIMO–NOMA systems
		19.5 Conclusion
		References
Index
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