This edition first published 2017
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CONTENTS

Preface

Notes

List of Abbreviations

Part I: Green Mobile Networking Technologies

Chapter 1: Fundamental Green Networking Technologies
1. 1.1 Energy Efficient Multi-cell Cooperation
2. 1.2 Heterogeneous Networking
3. 1.3 Mobile Traffic Offloading
4. 1.4 Device-to-Device Communications and Proximity Services
5. 1.5 Powering Mobile Networks With Renewable Energy
6. 1.6 Green Communications via Cognitive Radio Communications
7. 1.7 Green Communications via Optimizing Mobile Content Delivery
8. Note
Chapter 2: Multi-cell Cooperation Communications
1. 2.1 Traffic Intensity Aware Multi-cell Cooperation
2. 2.2 Energy Aware Multi-cell Cooperation
3. 2.3 Energy Efficient CoMP Transmission
4. 2.4 Summary and Future Research
5. 2.5 Questions
6. Note
Chapter 3: Powering Mobile Networks with Green Energy
1. 3.1 Green Energy Models: Generation and Consumption
2. 3.2 Green Energy Powered Mobile Base Stations
3. 3.3 Green Energy Powered Mobile Networks
4. 3.4 Summary
5. 3.5 Questions
6. Notes
Chapter 4: Spectrum and Energy Harvesting Wireless Networks
1. 4.1 Spectrum Harvesting Techniques
2. 4.2 Energy Harvesting Techniques
3. 4.3 FreeNet: Spectrum and Energy Harvesting Wireless Networks
4. 4.4 Summary
5. 4.5 Questions
6. Note

Part II: Green Mobile Networking Solutions

Chapter 5: Energy and Spectrum Efficient Mobile Traffic Offloading
1. 5.1 Centralized Energy Spectrum Trading Algorithm
2. 5.2 Auction-Based Decentralized Algorithm
3. 5.3 Performance Evaluation
4. 5.4 Summary
5. 5.5 Questions
Chapter 6: Optimizing Green Energy Utilization for Mobile Networks with Hybrid Energy Supplies
1. 6.1 Green Energy Optimization Scheme for Mobile Networks With Hybrid Energy Supplies
2. 6.2 Optimal Renewable Energy Provisioning for BSs
3. 6.3 Summary
4. 6.4 Questions
5. Notes
Chapter 7: Energy Aware Traffic Load Balancing in Mobile Networks
1. 7.1 Traffic Load Balancing in Mobile Networks
2. 7.2 ICE: Intelligent Cell brEathing to Optimize the Utilization of Green Energy
3. 7.3 Energy- and QoS-Aware Traffic Load Balancing
4. 7.4 Energy Efficient Traffic Load Balancing in Backhaul Constrained Small Cell Networks
5. 7.5 Traffic Load Balancing in Smart Grid Enabled Mobile Networks
6. 7.6 Summary
7. 7.7 Questions
8. Notes
Chapter 8: Enhancing Energy Efficiency via Device-to-Device Proximity Services
1. 8.1 Energy Efficient Cooperative Wireless Multicasting
2. 8.2 Green Relay Assisted D2D Communications
3. 8.3 Green Content Brokerage
4. 8.4 Summary
5. 8.5 Questions
6. Note
Chapter 9: Greening Mobile Networks via Optimizing the Efficiency of Content Delivery
1. 9.1 Mobile Network Measurements
2. 9.2 Mobile System Evolution
3. 9.3 Content and Network Optimization
4. 9.4 Mobile Data Offloading
5. 9.5 Web Content Delivery Acceleration System
6. 9.6 Multimedia Content Delivery Acceleration
7. 9.7 Summary
8. 9.8 Questions
9. Notes

References

Index

EULA

List of Illustrations

Chapter 1

Figure 1.1 Scenario 1: One SCBS per macro site.
Figure 1.2 Scenario 2: Two SCBSs per macro site.
Figure 1.3 Scenario 3: Three SCBSs per macro site.
Figure 1.4 Scenario 4: Five SCBSs per macro site.
Figure 1.5 Green energy enabled mobile networks. Source: Han 2014 [17]. Reproduced with permission of IEEE.
Figure 1.6 Classification hierarchy of content delivery acceleration solutions in mobile networks. Source: Han 2013 [14]. Reproduced with permission of IEEE.

Chapter 2

Figure 2.1 Original Network Layout. Source: Han 2013 [5]. Reproduced with permission of IEEE.
Figure 2.2 BS partially switched off. Source: Han 2013 [5]. Reproduced with permission of IEEE.
Figure 2.3 BS entirely switched off. Source: Han 2013 [5]. Reproduced with permission of IEEE.
Figure 2.4 Energy utilization of the cellular network. Source: Han 2013 [5]. Reproduced with permission of IEEE.
Figure 2.5 Cooperation to increase coverage area. Source: Han 2013 [5]. Reproduced with permission of IEEE.
Figure 2.6 Future cellular networks with holistic cooperations. Source: Han 2013 [5]. Reproduced with permission of IEEE.

Chapter 3

Figure 3.1 A green energy powered BS. Source: Han 2014 [17]. Reproduced with permission of IEEE.
Figure 3.2 A BS’s power supplies. Source: Han 2014 [17]. Reproduced with permission of IEEE.

Chapter 4

Figure 4.1 Relaying and cooperation in CR systems. Source: Huang 2015 [100]. Reproduced with permission of IEEE.
Figure 4.2 Heterogeneous CR system. Source: Huang 2015 [100]. Reproduced with permission of IEEE.
Figure 4.3 The harvest-store-use mechanism of a cognitive device. Source: Huang 2015 [100]. Reproduced with permission of IEEE.
Figure 4.4 The RF energy harvesting mechanism. Source: Huang 2015 [100]. Reproduced with permission of IEEE.
Figure 4.5 Cognitive functionalities in energy harvesting. Source: Huang 2015 [100]. Reproduced with permission of IEEE.
Figure 4.6 FreeNet: spectrum and energy harvesting wireless network. Source: Ansari 2016 [130]. Reproduced with permission of IEEE.
Figure 4.7 Grid power consumption vs. input traffic. Source: Ansari 2016 [130]. Reproduced with permission of IEEE.

Chapter 5

Figure 5.1 Illustration of energy spectrum trading scheme. Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.2 Simulation topology. Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.3 Performance of the EST vs. the percentage of cell edge users (topology 1). Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.4 Performance of the EST vs. the distance between the PBS the SBSs (topology 1). Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.5 The average distance between mobile users and the SBSs (topology 1). Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.6 Performance of the EST vs. the SBSs’ power-spectral density (topology 1). Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.7 Performance of the EST vs. the SBSs’ compensating bandwidth (topology 1). Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.8 Performance of the EST vs. the SBSs’ compensating bandwidth (topology 2). Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.9 The SBSs’ power cost and obtained bandwidth vs. the SBSs’ compensating bandwidth (topology 2). Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.10 Power consumption comparison and secondary data rate. Source: Han 2014 [139]. Reproduced with permission of IEEE.
Figure 5.11 Power consumption comparison and secondary data rate. Source: Han 2014 [139]. Reproduced with permission of IEEE.

Chapter 6

Figure 6.1 Hourly solar energy generation. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.2 The rationale of decomposition. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.3 Illustration of the GEO algorithm. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.4 Illustration of the problem in cell size adaptation. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.5 The energy drainage rate of BS 1. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.6 The energy drainage rate of BS 2. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.7 The performance of the MEB algorithm. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.8 The on-grid energy consumption with different generation rates. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.9 Green energy consumption with different generation rates. Source: Han 2013 [18]. Reproduced with permission of IEEE.
Figure 6.10 An illustration of solar panel size and battery capacity. Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.11 Average traffic rate. Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.12 Solar power rate. Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.13 The convergence of provisioning cost aware load balancing (α = 1). Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.14 The maximum latency ratio. Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.15 The weighted power cost. Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.16 Maximum latency ratio of BSs (α = 1). Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.17 The weighted power cost of the network (α = 1). Source: Han 2016 [155]. Reproduced with permission of IEEE.
Figure 6.18 The total provisioning cost. Source: Han 2016 [155]. Reproduced with permission of IEEE.

Chapter 7

Figure 7.1 Illustration of the failure of the greedy algorithm. Source: Han 2012 [11]. Reproduced with permission of IEEE.
Figure 7.2 The maximal EDR comparison (N = 25). Source: Han 2012 [11]. Reproduced with permission of IEEE.
Figure 7.3 The EDR comparison (N = 25, M = 600). Source: Han 2012 [11]. Reproduced with permission of IEEE.
Figure 7.4 LBSs’ statuses (N = 25, M = 600, G = 25). Source: Han 2012 [11]. Reproduced with permission of IEEE.
Figure 7.5 The user outage percentage. Source: Han 2012 [11]. Reproduced with permission of IEEE.
Figure 7.6 The user outage percentage comparison (M = 600, τ = 360s). Source: Han 2012 [11]. Reproduced with permission of IEEE.
Figure 7.7 A HetNet powered by hybrid energy sources: on-grid power and green energy. Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.8 A hybrid energy powered BS. Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.9 The practical implementation of vGALA. Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.10 Green energy aware (GA). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.11 Latency aware (LA). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.12 vGALA (θ = 0.8, κ = 4). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.13 The comparison of different user association schemes (θ = 0.8, κ = 4). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.14 The performance of user association schemes with different average traffic arrival rates (θ = 0.8, κ = 4). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.15 The performance of vGALA with various κ (θ = 1). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.16 The performance of vGALA with various θ (κ = 4). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.17 The performance of vGALA versus solar cell power efficiency (θ = 0.8, κ = 4). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.18 The performance of vGALA versus CRE (θ = 0.8). Source: Han 2016 [157]. Reproduced with permission of IEEE.
Figure 7.19 The small cell network. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.20 The traffic delivery process as a queuing system. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.21 The value of ψ(η) with different κ. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.22 The traffic delivery latency with different κ. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.23 The brown power consumption with different κ. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.24 The value of ψ(η) versus the solar power efficiency. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.25 Traffic delivery latency versus solar power efficiency. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.26 Brown power consumption versus solar power efficiency. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.27 The value of ψ(η) versus data rate bias. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.28 The value of ψ(η) versus backhaul data rates. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.29 The traffic delivery latency versus backhaul data rates. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.30 Brown power consumption versus backhaul data rates. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.31 The coverage area of vGALA. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.32 The coverage area of NUA-NC. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.33 The coverage area of NUA. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.34 The value of ψ(η) with R₅ reduced from 5 Mbps to 1 Mbps. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.35 The traffic delivery latency with R₅ reduced from 5 Mbps to 1 Mbps. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.36 Brown power consumption with R₅ reduced from 5 Mbps to 1 Mbps. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.37 vGALA. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.38 NUA-NC. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.39 NUA. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.40 The impact of cache awareness on traffic delivery latency. Source: Han 2017 [158]. Reproduced with permission of IEEE.
Figure 7.41 The smart grid enabled mobile network. Source: Han 2014 [182]. Reproduced with permission of IEEE.
Figure 7.42 ELLA convergence. Source: Han 2014 [183]. Reproduced with permission of IEEE.
Figure 7.43 Coverage area (τ = 8). Source: Han 2014 [182]. Reproduced with permission of IEEE.
Figure 7.44 On-grid power consumption. Source: Han 2014 [182]. Reproduced with permission of IEEE.
Figure 7.45 Traffic delivery latency. Source: Han 2014 [182]. Reproduced with permission of IEEE.

Chapter 8

Figure 8.1 Green relay assisted D2D communications. Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.2 Algorithm flowchart. Source: Han 2011 [196]. Reproduced with permission of IEEE.
Figure 8.3 Transmit power versus number of users. Source: Han 2011 [196]. Reproduced with permission of IEEE.
Figure 8.4 User SNR. 120 users are uniformly distributed in the sector. Source: Han 2011 [196]. Reproduced with permission of IEEE.
Figure 8.5 Power consumed during the relay phase. Source: Han 2011 [196]. Reproduced with permission of IEEE.
Figure 8.6 The number of iterations versus the number of users (μ = 0.01). Source: Han 2011 [196]. Reproduced with permission of IEEE.
Figure 8.7 Power consumption versus number of users. Source: Han 2011 [196]. Reproduced with permission of IEEE.
Figure 8.8 Illustration of cooperative communications. Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.9 Illustrative example of the recursive GRA algorithm. Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.10 The relay locations for the simulations. Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.11 Minimum data rate comparison (M = 4). Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.12 Minimum data rate comparison (M = 5). Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.13 Total data rate comparison (M = 5). Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.14 Minimum data rate vs. green load capacity. Source: Han 2013 [189]. Reproduced with permission of IEEE.
Figure 8.15 The content slot structure. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.16 The communications procedure of the content brokerage scheme. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.17 An illustration of the COS problem. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.18 The formation of the bipartite graph. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.19 An illustration of the optimization. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.20 MBS energy consumption over time. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.21 MBS energy consumption versus content availability ratios. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.22 GCB energy consumption versus content availability ratios. Source: Han 2014 [199]. Reproduced with permission of IEEE.
Figure 8.23 MBS energy consumption versus content popularity ratios. Source: Han 2014 [199]. Reproduced with permission of IEEE.

Chapter 9

Figure 9.1 The RRC states in UMTS. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.2 Packet loss in the handover process. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.3 Overall system architecture. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.4 The RRC states in EPS. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.5 UMTS and EPS downlink user plane handling comparison. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.6 New network coding layer in the protocol stack. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.7 The dynamic of the inter-packet interval. Source: Han 2012 [248]. Reproduced with permission of IEEE.
Figure 9.8 TCP sender’s response to ACK. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.9 TCP sender’s response to DUPACK. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.10 The network topology for the simulation. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.11 Web page response time. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.12 Response time for inlined object. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.13 Average traffic drop at the gateway. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.14 Number of retransmitted packets on web server. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.15 Test of ACK marking mechanism at BS. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.16 The sending rate adaptation of TCP-ME. Source: Han 2012 [249]. Reproduced with permission of IEEE.
Figure 9.17 pTCP structure. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.18 Web connection procedure. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.19 GPRSWeb. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.20 WebAccel. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.21 Nett-Gain platform. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.22 The standard HTTP protocol. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.23 The Silo protocol. Source: Han 2013 [14]. Reproduced with permission of IEEE.
Figure 9.24 Application layer bandwidth estimation. Source: Han 2013 [14]. Reproduced with permission of IEEE.

Preface

Greening is not merely a trendy concept, but is becoming a necessity to bolster social, environmental, and economic sustainability. Naturally, green communications have received much attention recently. As mobile network infrastructures and mobile devices proliferate, an increasing number of users rely on cellular networks for their daily lives. As a result, mobile networks are among the major energy hogs of communication networks and their contribution to global energy consumption is increasing fast. Therefore, greening of cellular networks is crucial to reducing the carbon footprint of Information and Communications Technology (ICT). As a result, the field is attracting tremendous research efforts from both academia and industry.

This book is intended to provide a technical description of state-of-the-art developments in greening of mobile networks from a networking perspective. It discusses fundamental networking technologies that lead to energy-efficient mobile networks. These technologies include heterogeneous networking, multi-cell cooperation, mobile traffic offloading, traffic load balancing, renewable energy integrated mobile networking, device-to-device networking, and mobile content delivery optimization. The text is suitable for graduate courses in electrical and computer engineering and computer science. The authors have adopted some materials presented in this book for their graduate courses at New Jersey Institute of Technology¹ and University of North Carolina–Charlotte². This book also includes many results and patented algorithms from our research, which makes this book a valuable reference for graduate students, practicing engineers, and research scientists in the field of green communications and networking.

The material is structured in a modular fashion with chapters being reasonably independent of each other. Individual chapters can be perused in an arbitrary order to the liking and interest of the reader, and they can also be incorporated as part of a larger, more comprehensive course. The first chapter provides an overview of existing networking technologies and solutions for greening mobile networks. The second to fourth chapters cover three major networking technologies in detail: multi-cell cooperation, green energy enabled mobile networking, and spectrum and energy harvesting. The fifth to ninth chapters present green mobile networking solutions including mobile traffic offloading, optimizing green energy enabled mobile networks, traffic load balancing, device-to-device networking, and content delivery optimization.

Notes

List of Abbreviations

3GPP: 3rd generation partnership project
ACE: Acknowledgment based on CWND estimation
AF; DF: Amplify-and-forward; decode-and-forward
AMC: Adaptive modulation and coding
ARQ; HARQ: Automatic repeat-request; hybrid ARQ
BA: Bandwidth allocation
BDP: Bandwidth delay product
BES: Base station energy sharing
BESS: Binary energy system sizing
BM: Battery minimization
BPO: Base station operation and power distribution optimization
BS: Base station
CAPEX; OPEX: Capital expenditure; operational expenditure
CBR: Constant bit rate
CELL_DCH: Cell dedicated channel
CELL_FACH: Cell forward access channel
CELL_PCH: Cell page channel
CF: Cluster formation
CH: Cluster head
CoMP: Coordinated multi-point
COS: Content owner selection
CR: Cognitive radio
CRE: Cell range expansion
CSS: Cooperative sensing scheduling
CW: Congestion warning
CWND: Congestion window
D2D: Device-to-device
DAC; ADC: Digital-to-analog converter; analog-to-digital converter
DC; AC: Direct current; alternating current
DCH: Dedicated channel
DRA: Dynamic resource allocation
DRB: Data rate bias
DSA: Dynamic spectrum access
EC-constraint: Energy causality constraint
EDR: Energy depleting ratio
EDS: Energy dependent set
EE: Energy efficiency
EH: Energy harvesting
ELLA: Energy loss and latency aware
EA: Energy Allocation
EPS: Evolved packet system
ESG: Energy saving greedy
ESM: Energy savings maximization
EST: Energy spectrum trading
EUTRAN (UTRAN): Evolved UTMS terrestrial radio access network
FC: Fusion center
GALA, vGALA: Green energy aware and latency aware; virtual GALA
GAP: Green energy aware problem
GCB: Green content broker
GEO: Green energy optimization
GEP: Green energy provisioning
GESS: Green energy system sizing
GPRS: General packet radio service
GRA: Green relay assignment
GUA: General user association
HetNets: Heterogeneous networks
HTO: Heuristic traffic offloading
ICE: Intelligent cell breathing
ICT: Information and communications technology
IoT: Internet of things
ISP: Internet service provider
IT: Interference temperature
KKT: Karush–Kuhn–Tucker
LAP: Latency aware problem
LEM: Largest EDR minimization
LM: Latency minimization
LOEP: Loss of energy probability
LOLP: Loss of load probability
LTE: Long term evolution
MAC: Media access control
MBS: Macro base station
MDP: Markov decision process
MEA: Multi-stage energy allocation
MEB: Multi-BS energy balancing
MIMO: Multi-input-multi-output
MINLP: Mixed integer non-linear programming
MNO: Mobile network operator
MRC: Maximal ratio combining
MWBM: Maximum weight bipartite matching
NC: No cache
NLOS: Non-line-of-sight
NPS: Non-interfering prefetching system
NUA: Network utility aware
O-DSTC: Opportunistic distributed space-time coding
OFDM; OFDMA: Orthogonal frequency-division multiplexing; orthogonal frequency-division multiple access
P2P; P2MP: Point to point; point to multi-point
PBR: Power-bandwidth ratio
PBS: Primary base station
PCA: Provisioning cost aware
PCM; SPCM; HPCM; WPCM: Power consumption minimization; simplified PCM; heuristic PCM; weighted PCM
PDCP: Packet data convergence protocol
POMDP: Partially observable Markov decision process
PS: Processor sharing
QB: QoS bound
QoE: Quality of experience
QoS: Quality of service
RA: Resource allocation
RAN; RANC: Radio access network; RAN controller
RAT: Radio access technology
RED: Random early detection
RF: Radio frequency
RLC: Radio link control
RN: Relay node
RR: Round robin
RRC: Radio resource control
RSSI: Received-signal-strength-indication
RTO: Retransmission timeouts
RTT: Round trip time
SAM: System advisor model
SBS: Secondary base stations
SCBS: Small cell base station
SCN: Small cell network
SCS: Serving content selection
SD: Source-destination
SDR: Secondary data rate
SDU: Service data unit
SE: Spectrum, efficiency
SGSN; GGSN: Serving GPRS support node; gateway GPRS support node
SHA: Secure hash algorithm
SI: Side information
SINR: Signal interference noise ratio
SN: Source node
SNR: Signal to noise ratio
SoftRAN: Software-defined radio access network
SSF: Strongest signal first
TDMA; FDMA: Time division multiple access; frequency division multiple access
TOM: Traffic offloading maximization
UA: User association
UE: User equipment
UMTS: Universal mobile telecommunications system
URA_PCH: UTRAN registration area paging channel
URICA: Usage-aware interactive content adaptation
WBST: Wireless boosted session transport
WEM: Weighted energy minimization
WLA: Wireless loss alarm
WUA; AWUA: Weighted user–BS association; approximate WUA
X2: Inter-eNode B interfaces

Green Mobile Networks

A Networking Perspective

Preface

Notes

List of Abbreviations

Part I
Green Mobile Networking Technologies