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

Cover
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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