Green Communications for Energy-Efficient Wireless Systems and Networks (Telecommunications)

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Contents
About the editors
1 Introduction
	1.1 Energy-efficient resource allocation
		1.1.1 Energy-efficient performance metrics
		1.1.2 Energy-efficient resource allocation methods
	1.2 Network design and deployment
		1.2.1 Dense networks
		1.2.2 Base station on/off switching
		1.2.3 Massive MIMO
		1.2.4 mmWave cellular systems
		1.2.5 Cloudification and virtualization
		1.2.6 Offloading techniques
	1.3 Energy harvesting communications
		1.3.1 Information-theoretic characterization of energy harvesting channels
		1.3.2 Offline energy management for throughput maximization
		1.3.3 Online energy management for performance optimization
		1.3.4 Routing and resource allocation in multi-hop energy harvesting networks
	1.4 Efficient hardware design
	1.5 Overview of the textbook
	References
Part I. Mathematical tools for energy efficiency
	2 Optimization techniques for energy efficiency
		2.1 Introduction and motivation
			2.1.1 Motivating single-link examples
			2.1.2 Interference networks with treating interference as noise
			2.1.3 Overview and outline
			2.1.4 Notation
		2.2 Fractional programming theory
			2.2.1 Pseudo-concavity
			2.2.2 Specific fractional programming problems
			2.2.3 Dinkelbach’s Algorithm
			2.2.4 Variants of Dinkelbach’s Algorithm
		2.3 Global optimization
			2.3.1 Branch and bound
			2.3.2 Bounding methods
			2.3.3 Feasibility test
				2.3.3.1 Box constraints
				2.3.3.2 Minimum rate constraints
				2.3.3.3 General inequality constraints
		2.4 Successive incumbent transcending scheme
			2.4.1 ε-Essential feasibility and the SIT scheme
			2.4.2 SIT for fractional DI problems with some convex variables
		2.5 Sequential convex approximation
		2.6 Conclusions
			2.6.1 Further reading
		References
	3 Deep learning for energy-efficient beyond 5G networks
		3.1 Introduction
			3.1.1 AI-based wireless networks
		3.2 Integration into wireless networks: smart radio environments
			3.2.1 The role of deep learning in smart radio environments
			3.2.2 ANNs deployment into wireless networks
		3.3 State-of-the-art review
		3.4 Energy efficiency optimization by deep learning
			3.4.1 Weighted sum energy efficiency maximization
			3.4.2 Energy efficiency in non-Poisson wireless networks: a deep transfer learning approach
		3.5 Conclusions
		References
	4 Scheduling resources in 5G networks for energy efficiency
		4.1 Introduction
		4.2 Preliminaries
			4.2.1 Energy efficiency metrics and objectives
			4.2.2 A primer on convex optimization
			4.2.3 Sensors and their measurements
		4.3 The proposed scheduling algorithm
			4.3.1 The mathematical model: the measurements
			4.3.2 The mathematical model: the network
			4.3.3 Scheduling for a single time instance
			4.3.4 Scheduling for multiple time instances
			4.3.5 Adaptive scheduling for multiple time instances
			4.3.6 The proposed algorithm
		4.4 Experimental results
			4.4.1 Scheduling without sensor failures
			4.4.2 Scheduling with sensor failures
		4.5 Conclusions
		References
Part II. Renewable energy and energy harvesting
	5 Renewable energy-enabled wireless networks
		5.1 Introduction
		5.2 Renewable energy to pursue mobile operator goals
			5.2.1 Renewable energy production variability
			5.2.2 The problem of uncoupled traffic demand and solar energy production
			5.2.3 Traffic load and BS energy consumption
		5.3 Scenarios
			5.3.1 On-grid BSs in an urban environment and reliable power grid
			5.3.2 Off-grid or on-grid BSs with unreliable power grid
			5.3.3 Green mobile networks in the smart grid
		5.4 Challenges, critical issues, and possible solutions
			5.4.1 PV system dimensioning
			5.4.2 System operation and management
			5.4.3 Interaction with the smart grid
		5.5 Some case studies
			5.5.1 Photovoltaic system dimensioning
			5.5.2 System operation and management
			5.5.3 Interaction with the smart grid
		5.6 Conclusion
		References
	6 Coverage and secrecy analysis of RF-powered Internet-of-Things
		6.1 Introduction
			6.1.1 Literature review
		6.2 RF-energy harvesting from a coexisting cellular network
			6.2.1 System setup
			6.2.2 Performance metrics
			6.2.3 Analysis and main results
			6.2.4 Numerical results and discussion
		6.3 RF-energy harvesting from a coexisting, secrecy-enhancing network
			6.3.1 System setup
			6.3.2 Performance metrics
			6.3.3 Analysis and main results
			6.3.4 Numerical results and discussion
		6.4 Summary
		Acknowledgment
		References
	7 Backscatter communications for ultra-low-power IoT: from theory to applications
		7.1 BackCom basic principle
			7.1.1 Architecture
			7.1.2 Modes and modulation
			7.1.3 Design parameters
				7.1.3.1 Operating frequency
				7.1.3.2 Impedance matching
				7.1.3.3 Antenna gain
				7.1.3.4 Polarization
			7.1.4 Standardization
		7.2 BackCom networks
			7.2.1 BackCom networks
				7.2.1.1 Monostatic BackCom networks
				7.2.1.2 Bistatic BackCom networks
			7.2.2 Multi-access BackCom network
			7.2.3 Interference BackCom network
		7.3 Emerging backscatter communication technologies
			7.3.1 Ambient BackCom
			7.3.2 Wirelessly powered BackCom
			7.3.3 Full-duplex BackCom
			7.3.4 Visible-light-BackCom
			7.3.5 BackCom system with technology conversion
		7.4 Performance enhancements of backscatter communication
			7.4.1 Waveform design
				7.4.1.1 Single-tag case
				7.4.1.2 Multi-tag case
			7.4.2 Multi-antenna transmissions
				7.4.2.1 Space-time coding
			7.4.3 Energy beamforming
		7.5 Applications empowered by backscatter communications
			7.5.1 BackCom-assisted positioning
			7.5.2 Smart home and cities
			7.5.3 Logistics
			7.5.4 Biomedical applications
		7.6 Open issues and future directions
			7.6.1 From wireless information and power transmission to BackCom
			7.6.2 Security and jamming issues
			7.6.3 mmWave-based BackCom
		Acknowledgment
		References
	8 Age minimization in energy harvesting communications
		8.1 Introduction: the age-of-information (AoI)
			8.1.1 Status updating under energy harvesting constraints
				8.1.1.1 Summary of related works
				8.1.1.2 Categorization
			8.1.2 Chapter outline and focus
		8.2 Status updating over perfect channels
			8.2.1 The case B=∞
			8.2.2 The case B=1
			8.2.3 The case B < ∞
				8.2.3.1 Renewal state analysis
				8.2.3.2 Multi threshold policy
		8.3 Status updating over erasure channels
			8.3.1 The case B=∞
				8.3.1.1 Updating without feedback
				8.3.1.2 Updating with perfect feedback
				8.3.2.2 Updating with perfect feedback
		8.4 Conclusion and outlook
		References
Part III. Energy-efficient techniques and concepts for future networks
	9 Fundamental limits of energy efficiency in 5G multiple antenna systems
		9.1 A primer on energy efficiency
			9.1.1 Organization
			9.1.2 Notation
		9.2 Massive MIMO
			9.2.1 What is massive MIMO?
			9.2.2 A simple network model
			9.2.3 Spectral efficiency
		9.3 Energy efficiency analysis
			9.3.1 Zero circuit power
			9.3.2 Constant but nonzero circuit power
			9.3.3 Impact of BS antennas
			9.3.4 Varying circuit power
			9.3.5 Impact of interference
			9.3.6 Summary of Section 9.3
		9.4 State of the art on energy efficiency analysis
			9.4.1 Impact of cooperation
			9.4.2 Impact of imperfect channel knowledge
			9.4.3 Impact of spatial correlation
			9.4.4 Impact of densification
		References
	10 Energy-efficient design for doubly massive MIMO millimeter wave wireless systems
		10.1 Introduction
			10.1.1 State of the art
			10.1.2 Chapter organization
			10.1.3 Notation
		10.2 Doubly massive MIMO systems
			10.2.1 Differences with massive MIMO at microwave frequencies
			10.2.2 Use cases
		10.3 System model
			10.3.1 The clustered channel model
			10.3.2 Transmitter and receiver processing
			10.3.3 Performance measures
		10.4 Beamforming structures
			10.4.1 Channel-matched, fully digital (CM-FD) beamforming
			10.4.2 Partial zero-forcing, fully digital (PZF-FD) beamforming
			10.4.3 Channel-matched, hybrid (CM-HY) beamforming
			10.4.4 Partial zero-forcing, hybrid (PZF-HY) beamforming
			10.4.5 Fully analog beam-steering beamforming (AB)
		10.5 Asymptotic SE analysis
			10.5.1 CM-FD beamforming
			10.5.2 PZF-FD beamforming
			10.5.3 Analog beamforming
		10.6 EE maximizing power allocation
			10.6.1 Interference-free case
			10.6.2 Interference-limited case
		10.7 Numerical results
		10.8 Conclusions
		Acknowledgments
		References
	11 Energy-efficient methods for cloud radio access networks
		11.1 Introduction
		11.2 Energy efficiency optimization: mathematical preliminaries
			11.2.1 Global optimization method: monotonic optimization
			11.2.2 Local optimization method: successive convex approximation
		11.3 Cloud radio access networks: system model and energy efficiency optimization formulation
			11.3.1 System model
			11.3.2 Power constraints
			11.3.3 Fronthaul constraint
			11.3.4 Power consumption
				11.3.4.1 Circuit power consumption
				11.3.4.2 Signal processing and fronthauling power
				11.3.4.3 Dissipated power on PA
				11.3.4.4 Total power consumption
			11.3.5 Problem formulation
		11.4 Energy-efficient methods for cloud radio access networks
			11.4.1 Globally optimal solution via BRnB algorithm
			11.4.2 Suboptimal solutions via successive convex approximation
				11.4.2.1 SCA-based mixed integer programming
				11.4.2.2 SCA-based regularization method
				11.4.2.3 SCA-based ℓ0-approximation method
			11.4.3 Complexity analysis of the presented optimization algorithms
		11.5 Numerical examples
			11.5.1 Convergence results
			11.5.2 Energy efficiency performance
		11.6 Conclusion
		References
	12 Energy-efficient full-duplex networks
		12.1 Introduction
		12.2 Literature review
			12.2.1 Resource allocation
			12.2.2 Protocol design
			12.2.3 Hardware design
			12.2.4 Energy harvesting
		12.3 Single-cell analysis
			12.3.1 System model
			12.3.2 Numerical results
		12.4 Multicell analysis
			12.4.1 System model
			12.4.2 Location-based classification criteria
			12.4.3 Hybrid-duplex heterogeneous networks
			12.4.4 Numerical results
		12.5 Conclusion
		References
	13 Energy-efficient resource allocation design for NOMA systems
		13.1 Introduction
			13.1.1 Background
			13.1.2 Organization
			13.1.3 Notations
		13.2 Fundamentals of NOMA
			13.2.1 From OMA to NOMA
			13.2.2 Code-domain NOMA
			13.2.3 Power-domain NOMA
			13.2.4 Downlink NOMA
			13.2.5 Uplink NOMA
		13.3 Energy efficiency of NOMA
			13.3.1 Energy efficiency of downlink NOMA
			13.3.2 The trade-off between energy efficiency and spectral efficiency
		13.4 Energy-efficient resource allocation design
			13.4.1 Design objectives
			13.4.2 QoS constraint
				13.4.2.1 Minimum data rate requirement
				13.4.2.2 Outage probability requirement
			13.4.3 Fractional programming
			13.4.4 Successive convex approximation
		13.5 An illustrative example: energy-efficient design for multicarrier NOMA
			13.5.1 System model
			13.5.2 Energy-efficient resource allocation design
		13.6 Simulation results and discussions
			13.6.1 Convergence of the proposed algorithms
			13.6.2 System energy efficiency versus the total transmit power
		13.7 Conclusions
		Appendices
			A.1 Proof of Theorem 1
			A.2 Proof of Theorem 2
		References
	14 Energy-efficient illumination toward green communications
		14.1 Introduction
		14.2 Novel modulation techniques
			14.2.1 Mixed-carrier communications
				14.2.1.1 Binary-level transmission
				14.2.1.2 Multilevel transmission
				14.2.1.3 Frame structure
				14.2.1.4 Spectrum management and interference analysis
				14.2.1.5 Performance and discussion
			14.2.2 Lightweight MCC
				14.2.2.1 FFT-less concept
				14.2.2.2 Performance evaluation
		14.3 State-of-the-artVLC topics
			14.3.1 Security of coexistence with RF technologies
				14.3.1.1 OFDM inVLC
				14.3.1.2 SA-OFDM transmission
				14.3.1.3 SA-OFDM reception
				14.3.1.4 SA-OFDM performance
			14.3.2 Augmented MIMO in VLC
				14.3.2.1 ASM system model
				14.3.2.2 ASM performance evaluation
			14.3.3 Deep learning in VLC
				14.3.3.1 Background
				14.3.3.2 Autoencoder OFDM-basedVLC system
				14.3.3.3 Autoencoder-based optical camera communications
		14.4 Conclusion
		References
	15 Conclusions and future developments
		15.1 Flattening the energy curve to support 5G evolution
		15.2 Potential solutions for a greener future
		References
Index
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