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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
This book focuses on the emerging research topic "green (energy efficient) wireless networks" which has drawn huge attention recently from both academia and industry. This topic is highly motivated due to important environmental, financial, and quality-of-experience (QoE) considerations. Specifically, the high energy consumption of the wireless networks manifests in approximately 2% of all CO2 emissions worldwide. This book presents the authors’ visions and solutions for deployment of energy efficient (green) heterogeneous wireless communication networks. The book consists of three major parts. The first part provides an introduction to the "green networks" concept, the second part targets the green multi-homing resource allocation problem, and the third chapter presents a novel deployment of device-to-device (D2D) communications and its successful integration in Heterogeneous Networks (HetNets).
The book is novel in that it specifically targets green networking in a heterogeneous wireless medium, which represents the current and future wireless communication medium faced by the existing and next generation communication networks. The book focuses on multi-homing resource allocation, exploiting network cooperation, and integrating different and new network technologies (radio frequency and VLC), expanding the network coverage and integrating new device centric communication paradigms such as D2D Communications. Whilst the book discusses a significant research topic supported with advanced mathematical analysis, the resulting algorithms and solutions are explained and summarized in a way that is easy to follow and grasp. This book is suitable for networking and telecommunications engineers, researchers in industry and academia, as well as students and instructors.
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Table of Contents
Cover
Title Page
Copyright
Preface
Acknowledgements
Dedication
Part One: Introduction to Green Networks
Chapter 1: Green Network Fundamentals
1.1 Introduction: Need for Green Networks
1.2 Traffic Models
1.3 Energy Efficiency and Consumption Models in Wireless Networks
1.4 Performance Trade-Offs
1.5 Summary
Chapter 2: Green Network Solutions
2.1 Green Solutions and Analytical Models at Low and/or Bursty Call Traffic Loads
2.2 Green Solutions and Analytical Models at High and/or Continuous Call Traffic Loads
2.3 Green Projects and Standards
2.4 Road Ahead
2.5 Summary
Part Two: Multi-homing Resource Allocation
Chapter 3: Green Multi-homing Approach
3.1 Heterogeneous Wireless Medium
3.2 Green Multi-homing Resource Allocation
3.3 Challenging Issues
3.4 Summary
Chapter 4: Multi-homing for a Green Downlink
4.1 Introduction
4.2 Win–Win Cooperative Green Resource Allocation
4.3 IDC Interference-Aware Green Resource Allocation
4.4 Summary
Chapter 5: Multi-homing for a Green Uplink
5.1 Introduction
5.2 Green Multi-homing Uplink Resource Allocation for Data Calls
5.3 Green Multi-homing Uplink Resource Allocation for Video Calls
5.4 Summary
Chapter 6: Radio Frequency and Visible Light Communication Internetworking
6.1 Introduction
6.2 VLC Fundamentals
6.3 Green RF–VLC Internetworking
6.4 Summary
Part Three: Network Management Solutions
Chapter 7: Dynamic Planning in Green Networks
7.1 Introduction
7.2 Dynamic Planning with Dense Small-Cell Deployment
7.3 Dynamic Planning with Cooperative Networking
7.4 Balanced Dynamic Planning Approach
7.5 Summary
Chapter 8: Greening the Cell Edges
8.1 Introduction
8.2 Two-Tier Small-Cell-on-Edge Deployment
8.3 Energy-Aware Transmission Design
8.4 Area Spectral Efficiency of HetNets
8.5 Analytical Bounds on ASE of HetNets
8.6 Analytical Bounds on ASE over Generalized- Fading Channel
8.7 Energy Analysis of HetNets
8.8 Ecology and Economics of HetNets
8.9 Summary
APPENDIX A - Simulation Parameters
APPENDIX B - Proof of (8.38)
Chapter 9: D2D Communications in Hierarchical HetNets
9.1 Introduction
9.2 Modelling Hierarchical Heterogeneous Networks
9.3 Spectral Efficiency Analysis
9.4 Average User Transmission Power Analysis
9.5 Backhaul Energy Analysis
9.6 Summary
Appendix A
Appendix B - Simulation Parameters
Chapter 10: Emerging Device-Centric Communications
10.1 Introduction
10.2 Emerging Device-Centric Paradigms
10.3 Devices-to-Device Communications
10.4 Optimal Selection of Source Devices and Radio Interfaces
10.5 Optimal Packet Split among Devices
10.6 Green Analysis of Mobile Devices
10.7 Some Challenges and Future Directions
10.8 Summary
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Begin Reading
List of Illustrations
Chapter 1: Green Network Fundamentals
Figure 1.1 Breakdown of power consumption of a wireless cellular network [7]
Figure 1.2 Carbon footprint contribution by the telecommunications industry: (a) 2002 and (b) 2020 [13]
Figure 1.3 Spatial and temporal traffic fluctuations [38]
Figure 1.4 Average daily data traffic profile in a European country [39]
Figure 1.5 Percentage of power consumption at different components of a large-cell BS [27]
Figure 1.6 Different backhaul topologies [55]: (a) ring topology, (b) star topology, and (c) tree topology
Figure 1.7 MT circuit and transmit energy consumption [56]
Figure 1.8 Comparison of (a) energy efficiency and (b) energy consumption indices [60]
Figure 1.9 Performance trade-offs [9]
Figure 1.10 Energy efficiency versus SNR (a) with and (b) without MT circuit power consumption
Chapter 2: Green Network Solutions
Figure 2.1 BS wake-up schemes (a) -based scheme; (b) -based scheme: single vacation; (c) -based scheme: multiple vacations [27]
Figure 2.2 BS switching off mode entrance and exit [27]. (a) BS wilting; (b) BS blossoming
Figure 2.3 Modelling of MT on–off switching as a server with repeated vacations [27]. The model is similar to the BS -based scheme with multiple vacations
Figure 2.4 Configurations for small-cell deployment [27]. (a) Cell-on-edge deployment; (b) uniformly distributed deployment
Figure 2.5 Illustration of the difference between the relay station and femto-cell
Figure 2.6 Green hybrid solution [27]
Chapter 3: Green Multi-homing Approach
Figure 3.1 Illustration of a heterogeneous wireless network [126]
Figure 3.2 Illustration of multi-homing uplink and downlink radio communications in a heterogeneous wireless medium [126]
Figure 3.3 Centralized and decentralized implementations [126]. (a) Centralized; (b) decentralized
Figure 3.4 Illustration of IDC interference [127]
Figure 3.5 Illustration of the impact of frequency separation between the LTE and WLAN channels on the IDC interference [127]. (a) Maximum interference occur for adjacent LTE and WLAN channels; (b) interference decreases as the frequency separation increases between the LTE and WLAN channels; (c) zero interference for sufficiently faraway LTE and WLAN channels
Figure 3.6 IDC interference on different WLAN channels due to the uplink transmission of LTE at 2,397.5, 2,387.5 and 2,377.5 MHz [127]
Figure 3.7 IDC interference on LTE channels due to the uplink transmission of WLAN at 2,412, 2,422 and 2,432 MHz [127]
Figure 3.8 The presence of multiple BSs/APs for a limited number of radio interfaces per MT
Chapter 4: Multi-homing for a Green Downlink
Figure 4.1 Network coverage areas [137]
Figure 4.2 Illustration of non-cooperative single-network and cooperative multi-homing radio resource allocation. (a) Non-cooperative single-network solution; (b) Cooperative multi-homing solution
Figure 4.3 Total power consumption in the geographical region with different [137]. The BSs are separated by 250 m. The total bandwidth available at each BS is 10 MHz
Figure 4.4 Power consumption for each BS with different [137]. The BSs are separated by 250 m. The total bandwidth available at each BS is 10 MHz
Figure 4.5 Total power consumption in the geographical region with different [137]. The distance between both BSs is 250 m. The total bandwidth available at BS 1 is 10 MHz and BS 2 is in the range MHz
Figure 4.6 Power consumption for each BS with different [137]. The distance between both BSs is 250 m. The total bandwidth available at BS 1 is 10 MHz and BS 2 is in the range MHz
Figure 4.7 Total power consumption in the geographical region with different separation distances between the two BSs [137]. The total bandwidth available at each BS is 10 MHz
Figure 4.8 Power consumption for each BS with different separation distances between the two BSs [137]. The total bandwidth available at each BS is 10 MHz
Figure 4.9 LTE network performance [127]. (a) Achieved data rate; (b) power consumption
Figure 4.10 WLAN performance [127]. (a) Achieved data rate; (b) power consumption
Chapter 5: Multi-homing for a Green Uplink
Figure 5.1 Network coverage areas [54]
Figure 5.2 Illustration of the framework described in Algorithms 5.2.4–5.2.7
Figure 5.3 Achieved energy efficiency versus total power available at each MT [54]. (a) Minimum achieved energy efficiency; (b) average achieved energy efficiency
Figure 5.4 Average achieved satisfaction index versus total power available at any MT [54].
Figure 5.5 GoP structure with frame dependencies [161]. For instance, the circled I frame is an ancestor for the first B and P frames in the base layer and the I frame in the enhancement layer.
Figure 5.6 Flow chart of the proposed energy management sub-system procedure.
Figure 5.7 Performance comparison for the achieved video quality versus time using TEF, EEF and SGF [161]. kJ, and ε
c
= 0.3.
Figure 5.8 MT residual energy versus time. kJ, and ε
c
= 0.3.
Chapter 6: Radio Frequency and Visible Light Communication Internetworking
Figure 6.1 Illustration of VLC transceiver [183]
Figure 6.2 VLC interference in different cell formations [180, 188]
Figure 6.3 Illustration of VLC and RF APs coverage [183, 188]
Figure 6.4 Energy efficiency versus the number of MTs [183]
Figure 6.5 Energy efficiency versus the fixed power of the VLC system [183]
Figure 6.6 Energy efficiency versus the number of LEDs used by the VLC system [183]
Figure 6.7 Energy efficiency versus the LoS availability probability in VLC and RF systems [183]
Figure 6.8 Energy efficiency versus the LoS availability probability in RF systems [183]
Chapter 7: Dynamic Planning in Green Networks
Figure 7.1 Dense macro–pico network [196]
Figure 7.2 Number of active macro BSs for a dense macro–pico network [196]
Figure 7.3 Trade-off between outage probability and energy consumption for a dense macro–pico network [196]
Figure 7.4 Area energy efficiency for a dense macro–pico network [196]
Figure 7.5 User association for a dense macro–pico network [196]
Figure 7.6 Network coverage areas [12]
Figure 7.7 Time sequence of optimization events for the network cooperation energy-saving framework [12]
Figure 7.8 Aggregate traffic mean arrival rate in each cell [12]
Figure 7.9 Call-blocking probability in each cell with the optimal number of active channels from the active BSs [12]
Figure 7.10 Dynamic planning with unbalanced energy saving [197]. MTs with uplink traffic are associated with faraway BSs
Figure 7.11 Example of dynamic planning cluster consisting of two BSs [197]. For simplicity, two tilting angles are assumed per BS leading to two coverage areas per BS
Figure 7.12 Illustration of the fast and slow timescales under consideration, the system states, actions, transition probabilities and the decision-making process [197]
Figure 7.13 Expected downlink energy consumption versus the arrival rate of uplink users and the weighting factor [197]: (a) balanced approach and (b) unbalanced approach. The spatial distribution is for downlink users
Figure 7.14 Expected uplink energy consumption versus the arrival rate of uplink users and the weighting factor [197]: (a) balanced approach and (b) unbalanced approach. The spatial distribution is for uplink users
Figure 7.15 Expected energy consumption of uplink users versus the spatial distribution of the uplink users near the proximity of the first BS [197]. The uplink users' arrival rate is 0.4
Chapter 8: Greening the Cell Edges
Figure 8.1 Graphical illustration of the two-tier HetNets, where a macro-cell is surrounded by small-cells around the edge of the reference macro-cell
Figure 8.2 Summary of uplink transmission power adaptation for several competitive networks configurations
Figure 8.3 Geometrical illustration of the macro-cell-level interference problem, where the interfering mobile user is located at in one of the co-channel macro-cells at a reuse distance
Figure 8.4 Geometrical illustration of the small-cell-level interference problem where the interfering mobile users are located at , that is, mobile users are located in two adjacent small-cells of the reference small-cell
Figure 8.5 Comparison of the ASE of MoNet with two different HetNet configurations: (i) COE configuration and (ii) UDC configuration as a function of the reference macro-cell
Figure 8.6 Geometrical illustration of uplink interference showing the worst- and best-case distance of the interferers in both macro and small cellular networks
Figure 8.7 Analytical bounds on the ASE of (i) COE configuration considering that the interferers are located at the worst and best distances in each of the two adjacent small-cells and co-channel macro cells and (ii) MoNet configuration as a function of the radius of the macro-cell
Figure 8.8 Summary of energy analysis per user as a function of small-cell radius. (a) Energy consumption; (b) spectral and energy gains
Figure 8.9 Summary of carbon footprint of HetNets. (a) Uplink emissions for several networks; (b) Daily emissions profile corresponding to various traffic loads
Figure 8.10 Low carbon economy index (LCEI) for several competitive network configurations
Chapter 9: D2D Communications in Hierarchical HetNets
Figure 9.1 Hierarchical heterogeneous network showing MBS, SBS and D2D communication in the higher tiers
Figure 9.2 D2D user density based on the CDF approximation of
Figure 9.3 Three-tier hierarchical HetNet showing only two-rings for illustrative purpose
Figure 9.4 Sum Rate of MBS, SBSs with/without D2D users
Figure 9.5 Total Sum Rate of HetNet and hierarchical HetNet
Figure 9.6 Interference Geometry for two user densities
Figure 9.7 Average user transmission power comparison of our proposed deployment against full small-cell deployment
Figure 9.8 Transmission power saving of our proposed deployment against full small-cell deployment
Figure 9.9 Average user transmission power comparison of our proposed deployment against full small-cell deployment versus user density
Figure 9.10 Backhaul power consumption comparison of the network with D2D communication against full small-cell deployment
Figure 9.11 Backhaul energy-efficiency comparison of D2D communication against full small-cell deployment for a fixed macro-cell radius m
Figure 9.12 Tier 2 uplink sum transmission power comparison of D2D communication against full small-cell deployment
Figure 9.13 Downlink power consumption comparison of D2D communication against full small-cell deployment
Chapter 10: Emerging Device-Centric Communications
Figure 10.1 Illustration of conventional D2D, multi-homing D2D and Ds2D communication approaches
Figure 10.1 Achieved average energy efficiency versus the number of candidate source devices
Figure 10.2 Energy consumption per source device to transfer a 1-Mbit file versus the number of candidate source devices
Figure 10.3 Optimal packet split over two interfaces of two source mobile devices vs. range of data rate levels
Figure 10.4 Latency of transferring the requested file to the sink mobile device over two radio interfaces of two source mobile devices by exploiting the optimal packet split
Figure 10.5 Relative gain in file transfer latency (FTL) over Ds2D communication with optimal packet split and random packet split in comparison with direct D2D communication
Figure 10.6 Power consumption (Wh) of source devices versus range of achieved data rate
Figure 10.7 Monthly electricity cost for an average download of a file with a size of 80 MB over Ds2D communications with optimal and random packet split schemes and traditional D2D communication for the range of achieved data rate levels
Figure 10.8 Average improvement in battery life of source devices over Ds2D communications with optimal and random packet split and traditional D2D communications for a range of data rate levels
List of Tables
Chapter 1: Green Network Fundamentals
Table 1.1 Summary of different traffic models [27]
Table 1.2 Summary of different power models proposed in the literature [27]
Table 1.3 Power consumption profile for a femto-cell BS [27]
Table 1.4 MT power consumption for different technologies [27]
Table 1.5 MT power consumption for different data rates of audio streaming and downloading a 200-MB file using WiFi [27]
Table 1.6 Summary of different energy efficiency and consumption definitions proposed in the literature [27]
Chapter 2: Green Network Solutions
Table 2.1 Summary of green solutions and analytical models at low and/or bursty call traffic loads [27]
Table 2.2 Summary of green solutions and analytical models at high and/or continuous call traffic loads [27]
Chapter 3: Green Multi-homing Approach
Table 3.1 Interference parameters [127]
Chapter 4: Multi-homing for a Green Downlink
Table 4.1 Simulation parameters [127]
Chapter 5: Multi-homing for a Green Uplink
Table 5.1 Simulation parameters [54]
Chapter 7: Dynamic Planning in Green Networks
Table 7.1 System parameters [196]
Table 7.2 System parameters [12]
Table 7.3 BS working mode [12]
GREEN HETEROGENEOUS WIRELESS NETWORKS
Muhammad Ismail
Texas A&M University at Qatar, Doha, Qatarxs
Muhammad Zeeshan Shakir
University of the West of Scotland, Glasgow, UK
Khalid A. Qaraqe
Texas A&M University at Qatar, Doha, Qatar
Erchin Serpedin
Texas A&M University, College Station, Texas, USA
This edition first published 2016
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Library of Congress Cataloging-in-Publication Data
Names: Ismail, Muhammad, 1985 November 20- author. | Shakir, Muhammad\hb
Zeeshan, author. | Qaraqe, Khalid A., author. | Serpedin, Erchin, 1967- author.
Title: Green heterogeneous wireless networks / Muhammad Ismail, Muhammad Zeeshan Shakir, Khalid A. Qaraqe, Erchin Serpedin.
Description: Chichester, UK ; Hoboken, NJ : John Wiley & Sons, 2016. | Includes bibliographical references and index.
Identifiers: LCCN 2016010885 (print) | LCCN 2016015082 (ebook) | ISBN 9781119088059 (cloth) | ISBN 9781119088028 (pdf) | ISBN 9781119088035 (epub)
Subjects: Green communications | Energy efficiency | Heterogeneous wireless medium | multi-homing.
Classification: LCC TK5105.78 .I86 2016 (print) | LCC TK5105.78 (ebook) | DDC 004.6/8--dc23
LC record available at http://lccn.loc.gov/2016010885
A catalogue record for this book is available from the British Library.
ISBN: 9781119088059
Preface
This book focuses on the emerging research topic ‘green (energy- efficient) wireless networks’ that has drawn huge attention recently from both academia and industry. This topic is highly motivated due to important environmental, financial and quality-of-experience (QoE) considerations. Due to such concerns, various solutions have been proposed to enable efficient energy usage in wireless networks, and these approaches are referred to as green wireless communications and networking. The term ‘green’ emphasizes the environmental dimension of the proposed solutions. Hence, it is not sufficient to present a cost-effective solution unless it is eco-friendly.
In this book, we mainly focus on energy-efficient techniques in base stations (BSs) and mobile terminals (MTs) as they constitute the major sources of energy consumption in wireless access networks, from the operator and user perspectives. Furthermore, this book targets the heterogeneous nature of the wireless communication medium, and therefore, the book is entitled ‘Green Heterogeneous Wireless Networks’. The wireless communication medium has become a heterogeneous environment with overlapped coverage due to the co-existence of different cells (macro, micro, pico and femto), networks (cellular networks, wireless local areas networks and wireless metropolitan area networks) and technologies (radio frequency, device-to-device (D2D) and visible light communications (VLC)). In such a networking environment, MTs are equipped with multiple radio interfaces. Through multi-homing capability, an MT can maintain multiple simultaneous associations with different networks. Besides enhancing the achieved data rate through bandwidth aggregation, the heterogeneous wireless medium together with the multi-homing service can enhance the energy efficiency of network operators and mobile users.
This book consists of three parts. The first part provides an introduction to the ‘green networks’ concept and identifies the key problems associated with the existing green solutions. The first part consists of two chapters. The first chapter discusses the need for green (energy-efficient) communications, the modelling techniques used for energy efficiency and call traffic in wireless networks and different performance metrics. The second chapter reviews the existing solutions for green networking at different call traffic load conditions. It covers the green solutions adopted by different standards (e.g. 3GPP). Limitations and key problems of the existing solutions are also discussed.
The second part of the book targets the green multi-homing resource allocation problem, and it consists of four chapters. The first chapter introduces the green multi-homing resource allocation problem and discusses its potential benefits andchallenges. The limitations of the existing multi-homing green solutions are discussed and practical aspects that should be accounted for are presented to assist engineers and network operators in building green multi-homing solutions. These limitations and practical considerations are then discussed in detail in the following chapters. The second chapter addresses a major limitation of practical value in the existing green downlink multi-homing resource allocation strategies. Specifically, the existing solutions implicitly assume that all networks are willing to cooperate unconditionally for energy saving, which is not practical, and therefore, we present a novel win–win resource allocation mechanism that enables energy saving for network operators. Furthermore, a radio resource allocation framework that accounts for the in-device coexistence (IDC) interference between the LTE and WiFi networks is also presented. The third chapter addresses a major limitation of existing research on green uplink multi-homing resource allocation for data calls. Specifically, existing solutions adopt a single-user system model, which is not practical for uplink resource allocation, and hence we present a novel joint bandwidth and power allocation framework in a multi-user system that maximizes the minimum energy efficiency among all MTs in service. In addition, uplink resource allocation for sustainable multi-homing video transmission is also discussed. An energy management subsystem that adapts the MT energy consumption during the call to achieve at least the target video quality lower bound is presented. The last chapter of the second part of the book presents a novel framework that integrates femto cells with VLC for a green downlink multi-homing resource allocation strategy to exploit jointly their benefits in energy saving while overcoming their practical limitations in terms of VLC reliability and femto-cell high energy consumption as compared to VLC.
The third part of the book addresses green network management solutions and consists of four chapters. The first chapter addresses BS on–off switching methods for energy saving. Two mechanisms are presented to serve the mobile users while switching off the BSs. One mechanism relies on a dense deployment of small-cells while the other mechanism relies on a cooperative networking technique. Furthermore, existing solutions mainly shift the energy consumption burden from the network operators to mobile users, which is not practical as it will drain the mobile user terminals at faster rates, and consequently, we present a novel dynamic planning approach with a balanced energy saving strategy for network operators and mobile users. The second chapter presents a novel deployment model for small-cells and cell-on-edge deployment to enhance energy efficiency of the networks. The third chapter presents a novel deployment of D2D communications and their successful integration into heterogeneous networks. This chapter presents also an end-to-end analysis of power consumption in the whole network and stresses out the significance of device-centric communications for ‘greener networks’. The last chapter in this part of the book presents an emerging device centric green approach for content exchange/download between the devices by exploiting the multihoming and packet split over multiple interfaces in D2D links.
Muhammad Ismail, Muhammad Zeeshan Shakir,
Khalid A. Qaraqe, and Erchin Serpedin
Doha, Qatar
June 2016.
Acknowledgements
The authors would like to acknowledge their research collaborators for the joint research effort on many topics of mutual interest that helped the realization of this book. Special thanks go to Drs Weihua Zhuang, Mohamed Kashef, Mohamed Abdallah, Mohamed Marzban, Mohamed Khairy, Amila Gamage, Sherman Shen, Hafiz Yasar Lateef, Amr Mohamed, Mohamed-Slim Alouini, Hina Tabassum and Muhammad Ali Imran.
The authors would like to acknowledge the support from Qatar National Research Fund offered through NPRP.
Dedication
Muhammad Ismail dedicates this book to his beloved wife Noha, lovely sister Dina and dear parents Ismail and Wafaa.
Muhammad Zeeshan Shakir dedicates this book to his family members and friends for their support.
Khalid A. Qaraqe dedicates this book to the memory of his amazing father Ali, beloved wife May and lovely family.
Erchin Serpedin thanks his family and collaborators for their support.
Part OneIntroduction to Green Networks
Chapter 1Green Network Fundamentals
Efficient energy usage in wireless networks has drawn significant attention from both academia and industry, mainly because of critical environmental, financial, and quality-of-experience (QoE) concerns. Research efforts have led to various solutions that allow efficient use of energy in wireless networks. Such approaches are referred to as green wireless communication and networking. Throughout this book, our main focus is on developing energy-efficient communication techniques in base stations (BSs) and mobile terminals (MTs), as they represent the major sources of energy consumption in wireless access networks, from the operator and user perspectives, respectively, while accounting for the heterogeneous nature of the wireless communication medium. Towards this end, the first two chapters of the first part of this book are dedicated to introducing the background concepts of green networking. The first chapter discusses the need for green (energy-efficient) communications, the modelling techniques used for energy efficiency and call traffic in wireless networks, and different conflicting performance metrics. Building on such a background, the second chapter reviews the state-of-the-art green communication solutions and analytical models proposed for network operators and mobile users at different traffic load conditions, and points out their major shortcomings.
1.1 Introduction: Need for Green Networks
In response to the increasing demand for wireless communication services during the past decade, there has been wide deployment of wireless access networks [1]. By definition, a wireless access network is a wireless system that uses BSs and access points (APs) to interface MTs with the core network or the Internet [2]. Hence, the main components of a wireless access network are BSs/APs and MTs [3]. BSs/APs are mainly in charge of radio resource control and user mobility management, and provide access to the Internet. MTs are equipped with processing and display capabilities, and provide voice services, video streaming, and data applications to mobile users. Currently, MTs are provided with multiple radio interfaces, and mobile users can connect to different networks, such as cellular networks, wireless local area networks (WLANs), and wireless metropolitan area networks (WMANs), and enjoy single-network and/or multi-homing services [4–6].
From the network operator side, BS is the main source of energy consumption in the wireless access network [2]. The breakdown of a cellular network's typical power consumption is shown in Figure 1.1, which shows that almost of the operator's total power consumption is in the BS [2, 8, 9]. Worldwide, there are about 3 million BSs, which consume in total 4.5 GW of power [10]. From the user side, it has been estimated that there exist roughly 3 billion MTs in the world with a total power consumption of 0.2–0.4 GW [11]. Such high energy consumption of wireless access networks has triggered environmental, financial, and QoE concerns for both network operators and mobile users.
Figure 1.1 Breakdown of power consumption of a wireless cellular network [7]
From an environmental standpoint, the telecommunications industry is responsible for of the total emissions worldwide, and this percentage is expected to double by 2020 [12]. As shown in Figure 1.2, the mobile communications sector has contributed of the telecommunication carbon footprint in 2002, and this contribution is expected to grow to by 2020 [14]. Furthermore, the MT rechargeable batteries' expected lifetime is about 2–3 years and manifests in 25,000 t of disposed batteries annually, a factor that raises environmental concerns (and financial considerations for the mobile users as well) [15]. In addition, the high energy consumption of BSs and MTs is a source of high heat dissipation and electronic pollution [16]. From a financial standpoint, a significant portion of a service provider's annual operating expenses is attributed to energy costs [17, 18]. Technical reports have indicated that the cost of energy bills of service providers ranges from (in mature markets in Europe) to (in India) of the operational expenditure (OPEX) [19, 20]. The energy expenses reach up to of the OPEX for cellular networks outside the power grid [21, 22]. Finally, from a user QoE standpoint, it has been reported that more than of mobile users complain about their limited battery capacity [23]. In addition, the gap between the MT's offered battery capacity and the mobile users' demand for energy is growing exponentially with time [24]. Consequently, the MT's operational time between battery chargings has become a crucial factor in the mobile user's perceived quality-of-service (QoS) [25].
Figure 1.2 Carbon footprint contribution by the telecommunications industry: (a) 2002 and (b) 2020 [13]
The aforementioned concerns have triggered increasing demand for energy-efficient solutions in wireless access networks. Research efforts carried out in this direction are referred to as green network solutions. The term ‘green’ confirms the environmental dimension of the proposed approaches. Therefore, a cost-effective solution that is not eco-friendly is not attractive. For instance, having a cost-effective electricity demand schedule for a network operator that relies on different electricity retailers, in a liberated electricity market, is not considered a green solution if it does not ensure that the proposed solution is also eco-friendly in terms of the associated carbon footprint [26]. The objectives of the green wireless communications and networking paradigm are, therefore, (i) reducing energy consumption of communication devices and (ii) taking into account the environmental impacts of the proposed solutions.
In order to develop/analyse a green networking solution, an appropriate definition of energy efficiency/consumption for network operators and mobile users should be formulated. This definition should account for the power consumption, throughput, traffic load models, and conflicting performance metrics for network operators and mobile users. The first chapter of this book is dedicated to building this necessary background.
1.2 Traffic Models
Some energy-efficiency and consumption models are defined on the basis of the temporal fluctuations in the traffic load. In addition, different green approaches can be adopted at different traffic load conditions. Furthermore, some green approaches rely on the temporal and spatial fluctuations in the traffic load to save energy. For instance, in order to determine the sleep duration of a BS or MT, traffic models are used to probabilistically predict the idle period duration, as will be presented in Chapter 2. Moreover, the performance evaluation of the green approaches should be carried out using an appropriate traffic model. Consequently, it is necessary to gain a better understanding of the different traffic load models proposed in the literature before introducing energy efficiency and consumption models as well as green solutions.
Overall, the traffic modelling can be categorized into two classes, as shown in Table 1.1. The first class is referred to as the static model and assumes a fixed set of MTs, , that communicate with a fixed set of BSs, [23] [28–34] [45]. The static model suffers from several limitations. First, it does not consider the mobility of MTs in terms of their arrivals and departures. Second, it does not capture the call-level or packet-level dynamics in terms of call duration, packet arrival, and so on. On the other side, the second class, which is referred to as the dynamic model, captures the spatial and temporal fluctuations of the traffic load, and is discussed next in detail.
Table 1.1 Summary of different traffic models [27]
1.2.1 Traffic Spatial Fluctuation Modelling
Studies have indicated that traffic is quite diverse even among closely located BSs, as shown in Figure 1.3 [37, 38]. As a result, different models have been proposed in the literature to reflect the spatial fluctuations in call traffic load [18, 35, 36].
Figure 1.3 Spatial and temporal traffic fluctuations [38]
Location-based traffic load density is one approach to capture traffic spatial fluctuations [35]. In this context, a geographical region is covered by a set of BSs and the region is partitioned into a set of locations. In a given location , the file transfer request arrivals follow an inhomogeneous Poisson point process (PPP) with an arrival rate per unit area. The file sizes are independently distributed with mean at the location. Consequently, the traffic load density is given by , which is used as a measure of the spatial traffic variability.
The aforementioned approach adopts a pre-defined set of BSs, , with specific locations. An alternative approach, which is more suitable for a design stage, defines the locations of BSs based on the stochastic geometry theory [18]. Hence, the network's BS locations follow a homogeneous PPP, , with intensity in the Euclidean plane. Similarly, MTs are located according to a different independent stationary point process with intensity . According to the stationary PPP , the distance between an MT and its serving BS, , follows the same distribution regardless of the MT's exact location. The probability density function (PDF) of is expressed as [18]
The aforementioned models reflect the spatial variability of the traffic among different cells. To capture the spatial distribution variability of MTs within a givencell , a finite-state Markov chain (FSMC) model is adopted [36]. This model classifies the MTs into groups according to cell 's radius. Assuming there are MTs in cell , a spatial location distribution is considered within the cell. Thus, the FSMC model presents states. The state transition probability is the probability of the spatial distribution of the MTs within the cell at time slot to assume , given that it was at time slot , where and . Following this model, the dynamic fluctuations in the number of MTs in different regions within the cell can be captured.
1.2.2 Traffic Temporal Fluctuation Modelling
Two different time scales can capture the temporal fluctuations in the traffic load [12] [39]. The first time scale is a long-term one that reflects the traffic variations over the days of the week. Such a model can help in evaluating different energy-efficient approaches for network operators, as it captures both high and low call traffic load conditions. The second time scale is a short-term one that reflects the call (packet) arrivals and departures of the MTs. Such a model plays a vital role in evaluating energy-efficient resource allocation schemes for MTs and BSs. In the following subsections, we describe the two scales.
1.2.2.1 Long-Term Traffic Fluctuations
Real call traffic traces demonstrate a sinusoidal traffic profile in each cell, as shown in Figure 1.3 [17, 38]. During daytime (11 am–9 pm), traffic is much higher than that during nighttime (10 pm–9 am) [17] [37]. Furthermore, during weekends and holidays, the traffic profile, even during the peak hours, is much lower than that of a normal week day [17]. The traffic profile during a weekday is less than its peak value of the time, and this increases to of the time during weekends [17]. This behaviour can be captured using an activity parameter , which specifies the percentage of active subscribers over time , as shown in Figure 1.4 [39]. Denote as the population density of users per , as the number of operators (each being able to carry of the total traffic volume), and as the fraction of subscribers with an average data rate for terminal type (e.g. smart phone and tablet). Hence, the traffic demand, in bits per second per , is given by
Figure 1.4 Average daily data traffic profile in a European country [39]
Studies have indicated that the traffic load difference between two consecutive days for of the BSs is less than [37]. As a result, the long-term fluctuations in call traffic load can be estimated from the historical mobile traffic records; that is, the activity parameter and the average data rate can be inferred in practice from historical data.
1.2.2.2 Short-Term Traffic Fluctuations
Two categories can be distinguished for short-term traffic fluctuation models, namely call (flow)-level and packet-level models. Call (flow)-level models are useful in designing and evaluating green resource scheduling mechanisms at both BSs and MTs under high call traffic load. For myopic resource allocation solutions, the call arrivals are modelled using a Poisson process with rate , and the call durations are represented by an exponential distribution [12] [40–42]. Dynamic resource allocation solutions rely on FSMC to model traffic dynamics in terms of call arrivals and departures [36]. In this model, the number of calls in a given cell is captured by an -state Markov chain, with the state set . The state transition probability is the probability of having MTs within cell at time slot , given that there were MTs at time slot , where , .
In a low call traffic load condition, packet-level traffic models are useful in designing and evaluating green resource solutions (on–off switching) at the BSs and MTs, through modelling the BS/MT buffer dynamics in terms of packet arrival and transmission [43] [44]. For an infinite buffer size, the MT buffer dynamics can be expressed as
where , , and are the numbers of backlogged packets in the buffer, arriving packets, and transmitted packets, for MT in time slot , respectively. For a buffer with a finite size , the MT buffer dynamics can be represented by
The models (1.3) and (1.4) are used to investigate the optimal on–off switching mechanisms for the radio interfaces of MTs to achieve energy-efficient (green) communications at a low call traffic load condition, a topic that will be addressed in Chapter 2.
1.3 Energy Efficiency and Consumption Models in Wireless Networks
Following the temporal and spatial fluctuations in traffic load, this section summarizes different definitions that have been proposed in the literature to assess energy consumption/efficiency of wireless networks. Towards this end, we first present different throughput and power consumption models for BSs and MTs.
1.3.1 Throughput Models
The utility obtained from the wireless network in exchange for its consumed power is expressed most of the time in terms of the achieved throughput. In this context, we first introduce the concepts of aggregate BS capacity , area spectral efficiency , and user-achieved data rate , which will be used in the energy efficiency definitions to be presented later.
1.3.1.1 Network Side
The BS aggregate capacity for BS is measured using Shannon's formula as follows [26]
where denotes the total bandwidth of BS , represents the unit matrix, is the transmission power vector of BS to every MT in service, and stands for the channel gain matrix between BS and each MT , which accounts for the channel's fast fading, noise, and interference affecting the radio transmission. The BS capacity in (1.5) is measured in bits per second (bps).
At a low call traffic load condition, the area spectral efficiency provides a better representation of the BS's attained utility than the BS's aggregate capacity since it accounts for the coverage probability, which matters the most at such acondition [18]. Specifically, measures the BS throughput while considering the coverage probability. Denote as the success probability of the signal-to-noise ratio (SNR) received by an MT at location from a given BS at some location satisfying a certain QoS threshold . Averaging the success probability over the propagation range to location yields the coverage probability . For BS , the area spectral efficiency measured over a unit area is expressed as
1.3.1.2 Mobile Terminal Side
While the definitions in (1.5) and (1.6) are mainly from the operator side, two definitions can be used to quantify the mobile user's attained utility (in terms of the achieved data rate in the uplink by MT ) in exchange for the MT power consumption. Given the instantaneous channel state information (CSI), the achieved data rate in bps can be expressed as [16, 28, 45, 46]
where stands for the uplink allocated bandwidth to MT , represents the SNR of MT received at the destination, and denotes the SNR gap between the channel capacity and a practical coding and modulation scheme. For the Shannon formula, . Reporting instantaneous CSI from each MT to the serving BS, in order to determine (1.7), leads to a large signalling overhead. In order to reduce the associated signalling overhead, a statistical CSI is used. Consequently, in bps is expressed as
where represents the expectation over the channel state .
1.3.2 Power Consumption Models
In order to attain the aforementioned utilities in (1.5)–(1.8), power is consumed at both the network side and user side. In the literature, different models are proposed to capture such a power consumption, as summarized in Table 1.2. These models are next discussed.
Table 1.2 Summary of different power models proposed in the literature [27]
1.3.2.1 Network Side
The total power consumption of a wireless access network , from the network operator perspective, can be captured using the aggregate power consumption of the network BSs. Recently, in addition to the BS power consumption, more emphasis is put on the backhaul power consumption, due to the information exchange among BSs for cooperative transmission/networking. Next, we will outline the different power consumption models proposed for BSs and backhauls.
For a large-cell BS (macro- and micro-BS), Figure 1.5 illustrates the power consumption percentage of different components of the BS. Furthermore, the power consumption profile of a femto-cell BS is shown in Table 1.3. According to Figure 1.5 and Table 1.3, the following facts turn out:
The signal processing part is responsible for most of the power consumption in a femto-cell BS as opposed to a large-cell BS (namely,
and
for femto and large-cell BSs, respectively).
The radio frequency (RF) transmission/reception power consumption in a femto-cell BS is almost half of that of a large-cell BS, with only
of the power consumed in the femto-cell BS power amplifier as opposed to
in a large-cell BS.
Figure 1.5 Percentage of power consumption at different components of a large-cell BS [27]
Table 1.3 Power consumption profile for a femto-cell BS [27]
In the literature, different models are adopted to represent the BS power consumption . For a large-cell BS, the simplest model is an ideal load-dependent representation, which assumes that the BS consumes no power in its idle state, that is, the BS consists of energy-proportional devices [35]. Hence, the BS power consumption can be expressed as
where stands for the system traffic load density, and denotes the BS's transmitted power. The major limitation with such a model is that it is unrealistic, as the power consumption of some BS components in reality is not load-dependent, as shown in Figure 1.5 (e.g. power supply and air conditioning). To capture the power consumption of both load-dependent and load-independent components in the BS, a more sophisticated model assumes the following expression [39]
where represents the RF power consumption, denotes the baseband unit power consumption, is the power amplifier efficiency, and , , , and stand for the losses incurred by the antenna feeder, DC–DC power supply, main supply, and active cooling, respectively. The model (1.10) is further approximated using a linear (affine) function for simplicity [12, 18, 21, 26, 35]. The affine function consists of two components to represent . The first term is denoted by and represents a fixed (load-independent) power component that captures the power consumption at the power supply, cooling, and other circuits. The second term is a load-dependent component. The affine model is expressed as
where is the slope of the load-dependent power consumption.
For a femto-cell BS, the power consumption model is described by Deruyck et al. [47]
where , , , and denote the power consumption of the microprocessor, field-programmable gate array (FPGA), transmitter, and power amplifier, respectively. While the power consumption model in (1.12) captures most of the components in Table 1.3, it does not exhibit any dependence on the call traffic load. Experimental results in [48] have pointed out the dependence of the femto-cell BS power consumption on the offered load and the data packet size. Consequently, the power consumption model for a femto-cell BS is expressed by Riggio and Leith [48]
where represents the BS power consumption, which depends on the traffic load [Mbps] and packet size [bytes], and stands for the idle power consumption component.
In order to capture the temporal fluctuations in the call traffic load, as discussed in Section 1.2.1, a weighted sum of power consumptions at different traffic load conditions (full load, half load, and idle conditions) is considered [7]
where , , and denote the full rate, half rate, and sleep mode power consumption, respectively. The weights in (1.14) are determined statistically based on the historical traffic records.
Recently, cooperative networking among different BSs and APs in the heterogeneous wireless medium is regarded as an effective approach to enhance the network's overall capacity and reduce the associated energy consumption [1] [4–6] [54]. However, this approach relies on information exchange among different BSs and APs, such as CSI, call traffic load, and resource availability, which are carried mainly over the backhaul connecting these BSs and APs together. Hence, more emphasis is given to the backhaul design and its power consumption. Three types of backhaul solutions can be distinguished, namely copper, microwave, and optical fibre. The most common choice for backhaul is the copper lines [49]. Microwave backhauls are deployed in locations where it is difficult to deploy wired (copper) lines. Also, optical fibre backhauls are mainly used in locations with high traffic due to their high deployment cost. Current research is focusing mainly on the power consumption of microwave and optical fibre links, as they can support the current high data rates. In its simplest form, the microwave (wireless) backhaul power consumption is expressed as [49]
where and represent the BS's required backhaul capacity and the microwave backhaul total capacity (100 Mbps), respectively, and denotes the associated power consumption (50 W). However, the model in (1.15) does not account for many features of the backhaul. To gain a better understanding of the powerconsumption of backhauls, we first provide a brief description of the backhaul structure and associated topologies.
As shown in Figure 1.6, each BS is connected to one or more BSs via a backhaul link. All traffic from BSs is backhauled through a hub node (traffic aggregation point) [55]. Any BS in the network can serve as such a hub node. In general, more than one aggregation level (hub node) can be present. Each hub node is connected to a sink node, which, in turn, is connected to the core network. A BS is equipped with a switch if more than one backhaul link originates or terminates at this BS. Following this description, the microwave backhaul power consumption is expressed as [49]
where is the power consumption at the sink node, denotes the power consumption associated with the backhaul operations at BS , and stands for the total number of BSs. The following relationships hold
where and represent the required backhaul capacity for BS and the sink node, respectively. The variable denotes the number of microwave antennas, and represent the power consumed for transmitting and receiving backhaul traffic for BS and the sink node, respectively, and models the BS/sink switch power consumption. On the other hand, for an optical fibre backhaul, the power consumption is expressed as [49]
where stands for the maximum number of downlink interfaces available at one aggregation switch, denotes the power consumption due to one interface of a switch, and represent the total number of uplink interfaces and power consumption of one uplink interface, and denotes the power consumption of a pluggable optical interface, which is used to connect a BS to the switch at the hub node.
Figure 1.6 Different backhaul topologies [55]: (a) ring topology, (b) star topology, and (c) tree topology
A limitation with the models (1.9)–(1.16) is that they focus mainly on the BS's operation power. In a more general model, the BS's total consumption is described in terms of the BS's operating energy and embodied energy, and , respectively. The BS's embodied energy represents 30–40% of the BS's total energy consumption [19] and accounts for the energy consumed by all the processes associated with the manufacturing and maintenance of the BS. Over the BS's lifetime, the embodied energy is calculated as 75 GJ [19]. It consists of two components. The first component refers to the initial embodied energy , while the second one stands for the maintenance embodied energy . The initial embodied energy comprises the energy used to acquire and process raw materials, manufacture components, and assemble and install all BS components. The initial embodied energy is accounted for only once in the initial BS manufacturing process. The
