A Glimpse Beyond 5G in Wireless Networks

Preface
	Introduction
	Research Contributions
	Conclusions
	References
Acknowledgments
Contents
Editor and Contributors
	About the Editor
	Contributors
1 A Comprehensive Study on 5G: RAN Architecture, Enabling Technologies, Challenges, and Deployment
	1.1 Introduction
		1.1.1 Review of Related 5G Surveys
		1.1.2 Contributions and Chapter Organization
	1.2 5G Architecture for Public Networks
		1.2.1 Cloud-Based 5G Architecture
			1.2.1.1 Heterogeneous C-RAN (H-CRAN)
			1.2.1.2 Virtualized C-RAN (V-CRAN)
		1.2.2 Edge Computing with 5G Architecture
			1.2.2.1 Fog-Based 5G Architecture Within C-RAN
			1.2.2.2 Cloud-Edge 5G RAN Based on O-RAN and 3GPP
			1.2.2.3 Multi-Access Edge Computing within 5G RAN Based on 5GPPP and 3GPP
		1.2.3 Comparison Among Public 5G Architectures
	1.3 Private 5G Networks
		1.3.1 5G Architecture for Private Networks
		1.3.2 Spectrum Options for the Private 5G Networks
	1.4 5G Enabling Technologies
		1.4.1 Software-Defined Networking (SDN)
			1.4.1.1 SDN Architecture
			1.4.1.2 Research Works on SDN
		1.4.2 Network Functions Virtualization (NFV)
			1.4.2.1 NFV Architecture
			1.4.2.2 Research Works on NFV
		1.4.3 Network Slicing
			1.4.3.1 Network Slicing Architecture
		1.4.4 Artificial Intelligence/Machine Learning (AI/ML)
			1.4.4.1 Research Works on Network Slicing with AI/ML Strategies
		1.4.5 Multi-Access Edge Computing (MEC)
			1.4.5.1 MEC Architecture
	1.5 5G Development and Experimentations
		1.5.1 5G Development Platforms for R&D
			1.5.1.1 OpenAirInterface (OAI)
			1.5.1.2 Network Simulator-3 (NS-3)
			1.5.1.3 MATLAB 5G Toolbox
		1.5.2 5G R&D Projects
			1.5.2.1 MOSAIC 5G
			1.5.2.2 Open Networking Foundation (ONF)
	1.6 Challenging Issues and Directions
	References
2 Information Flows at the Deep Physical Layer Level
	2.1 Introduction
	2.2 Communication at the DPL Using the Space Resource
	2.3 Bandlimited Approximation in the Space Domain
	2.4 The Space-Time Polarization Number of Degrees of Freedom (STPNDF)
	2.5 Control of Information Flows at the DPL Level
	2.6 Conclusions
	References
3 FBMC: A Waveform Candidate for Beyond 5G
	3.1 Introduction
	3.2 Organization of Chapter
	3.3 FBMC System Model
		3.3.1 Data Detection
	3.4 MIMO-FBMC Semi-Blind CSI Estimation
		3.4.1 Review of Existing Works
		3.4.2 Semi-Blind MIMO-FBMC Channel Estimator
		3.4.3 MSE Gain of the Semi-Blind Estimate over the LS Estimate
	3.5 Performance of FBMC Waveform in Uplink of Massive MIMO
		3.5.1 Review of Existing Works
		3.5.2 Massive MIMO-FBMC System Model
		3.5.3 Uplink Sum Rate for Massive MIMO-FBMC with Imperfect CSI
			3.5.3.1 MRC Receiver
			3.5.3.2 ZF Receiver
		3.5.4 Uplink Sum Rate for Massive MIMO-FBMC with Perfect CSI
			3.5.4.1 MRC Receiver
			3.5.4.2 ZF Receiver
	3.6 Conclusions and Future Directions
	References
4 Full-Duplex Multi-Hop Communication for Beyond 5G
	4.1 Introduction
	4.2 Organization of the Chapter
	4.3 Massive MIMO
	4.4 Multi-hop Communication
		4.4.1 Full-Duplex Multi-Hop Communication
	4.5 Single-Pair/Multi-Pair One-Way/Two-Way Relay Models
		4.5.1 Single User Pair Relay Model
		4.5.2 Multi-Pair Two-Way Relay Model
	4.6 Single-Pair Half-Duplex Two-Way Massive MIMO Relay: Mathematical Model
		4.6.1 Relay Beamforming Design
			4.6.1.1 MRC/MRT Beamformer
			4.6.1.2 ZFR/ZFT Precoder
		4.6.2 Performance Analysis of Single-Pair Relay Model: MRT/MRC Beamforming
		4.6.3 Performance Analysis of Single-Pair Relay Model: ZFR/ZFT Beamforming
	4.7 Multi-Pair Half-Duplex Two-Way Relay: Mathematical Model
		4.7.1 MRC/MRT Beamforming
		4.7.2 ZFR/ZFT Beamforming
	4.8 Full-Duplex Multi-Pair AF Massive MIMO Relay
		4.8.1 SLI Suppression
			4.8.1.1 Passive Cancellation
			4.8.1.2 Large Antenna Array at Relay
			4.8.1.3 Lower Transmit Power at Relay
			4.8.1.4 Active Cancellation
	4.9 Multi-Pair Full-Duplex Relay: Mathematical Model
		4.9.1 Channel Model
		4.9.2 Data Transmission
	4.10 Performance Analysis
	4.11 Summary and Future Works
	References
5 NOMA for 5G and Beyond Wireless Networks
	5.1 Introduction
	5.2 Non-orthogonal Multiple Access
		5.2.1 Various Applications of NOMA
	5.3 Heterogeneous Networks
		5.3.1 Tier Selection Probability/Offloading Probability Based on Biased Received Power
		5.3.2 NOMA-Enabled Heterogeneous Networks
	5.4 Point Process
	5.5 Cooperation Using Device to Device Communication
	5.6 System Model
		5.6.1 Cooperation from D2D Tier
		5.6.2 Some Useful Probabilities
			5.6.2.1 Offloading Probability
			5.6.2.2 NOMA Compatibility Probability
			5.6.2.3 Corresponding PU Probability
		5.6.3 Total Outage Probability
			5.6.3.1 Total Outage Probability After Offloading
			5.6.3.2 Total Outage Probability After D2D Cooperation
	5.7 Results and Discussions
	5.8 Conclusion
	References
6 Energy Harvested Device-to-Device MIMO Systems for Beyond 5G Communication
	6.1 Introduction
		6.1.1 Wireless Energy Transmission Techniques
	6.2 Energy Harvesting Models
		6.2.1 Linear Energy Harvesting
		6.2.2 Nonlinear Energy Harvesting
	6.3 Impact of Imperfect CSI
		6.3.1 Mathematical Modeling
			6.3.1.1 Time Switching Protocol
			6.3.1.2 Phase 1
			6.3.1.3 Phase 2
		6.3.2 System Performance Metrics
			6.3.2.1 Outage Probability
			6.3.2.2 Asymptotic Analysis
			6.3.2.3 Throughput Analysis
		6.3.3 Results and Discussion
	6.4 Impact of Outdated CSI
		6.4.1 Mathematical Modeling
		6.4.2 System Performance Metrics
			6.4.2.1 Outage Probability
			6.4.2.2 Asymptotic Analysis
			6.4.2.3 Throughput Analysis
		6.4.3 Results and Discussions
			6.4.3.1 Impact of MIMO Antenna System
			6.4.3.2 Impact of Feedback Delays
			6.4.3.3 Throughput Analysis
	6.5 Conclusion
		6.5.1 Summary
	References
7 Vehicular Communications in the B5G Era
	7.1 Introduction
	7.2 V2X Communications
	7.3 Satellite Communications
	7.4 Vehicular Wireless Communication Link Case Study
	7.5 Challenges
	7.6 Conclusions
	References
8 Implementation of Context-Aware Environments with Massive IoT Systems
	8.1 Introduction
	8.2 Wireless Connectivity Analysis
		8.2.1 University Campus Scenario
		8.2.2 Urban City Center Scenario
		8.2.3 Indoor Warehouse Scenario
	8.3 Conclusions
	References
9 Feasibility of LDM to Serve User-IoT Pairs in the Future Wireless Network
	9.1 Introduction
	9.2 Literature Review
	9.3 Use Case Scenario of LDM in Future Wireless Network
		9.3.1 Urban Use Case Scenario for IoT Downlink Communication
		9.3.2 Rural Use Case Scenario for IoT Downlink Communication
	9.4 Transceiver Framework Adopting LDM
		9.4.1 Transmitter Framework with LDM Superposition
		9.4.2 Channel Model
		9.4.3 Receiver Framework for LDM Detection
	9.5 Theoretical Evaluation
		9.5.1 Bit Error Rate of LDM Upper Layer
		9.5.2 Channel Capacity Distribution
	9.6 Mobility Model
		9.6.1 IoT and User Mobility
		9.6.2 The Range of IoT Device\'s Initial Position
		9.6.3 Mobility Model
			9.6.3.1 Random Waypoint Mobility Model
			9.6.3.2 Manhattan Mobility Model
			9.6.3.3 Freeway Mobility Model
	9.7 Results and Analysis
		9.7.1 The Performance of LDM in an OFDM Framework
		9.7.2 Receiver Mobility
	9.8 Conclusion
	References
10 Wide Band THz Antenna Design Using Salp Swarm Algorithm for 6G Communications Systems
	10.1 Introduction
	10.2 Related Work
	10.3 Algorithm Description
	10.4 6G Patch Antenna Design
	10.5 Numerical Results
	10.6 Conclusion
	References
Index