Chapter 1
Green 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...