Introduction

To date, there have been a number of research proposals to explore the newly emerging wireless charging technologies based on radio-frequency (RF) signals, ambient or dedicated. In particular, research efforts towards achieving the goal of transmitting information and energy at the same time have been rapidly expanding, but the feasibility of this goal has not been fully addressed. Moreover, the respective coverage areas of transmitting information and energy are wildly different, the latter being considerably smaller than the former. This is because the receiver sensitivities are very different, namely -60 dBm for an information receiver and -10 dBm for an energy receiver [1, 2].

Owing to this limitation, recently a commercial implementation of RF energy transfer has been restricted to lower-power sensor nodes with dedicated RF energy transmitters, such as the Powercast wireless rechargeable sensor system [3] and the Cota system [4].

In this chapter, we discuss the implementation of long- and short-range RF energy harvesting systems, where the former is to provide far-field-based RF energy transfer over long distances with a 4 × 4 phased antenna array and the latter to provide biosensors with RF energy over short distances. An overall circuit design for these RF energy harvesting systems is described in detail, along with the measurement results to validate the feasibility of far-field-based RF energy transfer. We illustrate the designed test-beds which will be applied to develop sophisticated energy beamforming algorithms to increase the transmission range. Finally, a new research framework is developed through the cross-layer design of the RF energy harvesting system, which is intended to power a low-power sensor node, like the Internet-of-Things (IoT) sensor node. To this end, we present a circuit-layer stored energy evolution model based on the measurements which will be used in designing the upper-layer energy management algorithm for efficient control of the stored energy at the sensor node. The new framework will be useful because the existing works on RF energy harvesting do not explicitly take into account a realistic temporal evolution model of the stored energy in the energy storage device, like such as a supercapacitor.

Introduction

Energy harvesting in wireless cellular networks is a cornerstone of emerging 5G and beyond 5G (B5G) cellular networks as it aims to “cut the last wires” of the existing wireless devices [1]. In particular, energy harvesting has a significant potential to attract subscribers since it promotes mobility and connectivity anywhere and anytime, which is one of the key visions of next-generation wireless networks. In general, wireless energy harvesting can be classified according to the following two categories.

1. • Ambient energy harvesting (EH). This refers to energy harvested from renewable energy sources (such as thermal, solar, wind, etc.) as well as energy harvested from radio signals of different frequencies in the environment that can be sensed by EH receivers (e.g., co-channel interference, TV or radio broadcasting, etc.).

2. • Dedicated EH. This enables the intentional transmission of energy from dedicated energy sources to energy harvesting devices.

To satisfy the power demands of delay-constrained wireless applications, it is of utmost importance to ensure the availability of sufficient energy at the user terminals whenever required. This fact has motivated researchers toward the development of dedicated wireless-powered cellular networks (WPCNs) where dedicated energy sources or hybrid access points (HAPs) take care of both energy transfer and information transmission to and from the subscribers.

In this chapter, we focus on dedicated EH techniques. We first highlight the associated challenges. Next, we theoretically characterize and comparatively analyze a number of different network architectures for centralized and distributed dedicated wireless EH. Numerical results are provided to validate the analytical results.

Major Challenges in Dedicated Wireless Energy Harvesting

In this section, we will discuss a number of major challenges related to dedicated wireless energy harvesting (WEH) from the perspective of network architecture and modeling and resource allocation.

Network Architectures for Wireless Energy Harvesting

Different network architectures have been studied for WEH. However, most of the studies have been limited to a two- or three-node network model, a central base station (BS) that takes care of both the wireless information transmission and energy transfer, and follows a specific configuration of energy harvesting; i.e., a user harvests energy from a centralized half-duplex BS or full-duplex BS or through randomly deployed power beacons (PBs), etc.

Introduction

The concept of modulating backscatter for communication was first introduced by Stockman in 1948 [1] and promptly received a lot of attention from researchers and developers owing to its potential advantages. Basically, backscatter communication is a technique that allows wireless nodes to communicate without requiring any active radiofrequency (RF) components on the tag [2]. In a conventional backscatter communication system (CBCS), there are two main components, called the wireless tag reader device (WTRD) and the wireless tag device (WTD), as illustrated in Figure 6.1. The WTD in the CBCS is able not only to harvest energy from the received signals, but also to modulate and reflect the signals back to the WTRD. The signal reflection is caused by the intentional mismatch between the antenna and the load impedance at the WTD. Theoretically, when the load impedance is varied, it will generate the complex scatter coefficient which can be used to modulate the reflected signal with information bits. The WTRD then uses the receive antenna to receive reflected signals from the WTD and demodulate these signals to obtain the useful information.

In conventional backscattering communication systems, there are two special features that differ from traditional communication systems. First, in conventional backscattering communication systems, the receivers (i.e., WTRDs) have to be equipped with a power source to transmit RF signals to the transmitter (i.e., WTDs). Second, the transmitters do not need to be equipped with a power source to transmit data because they will reflect signals received by the receivers instead of generating their own signals. The second feature is the most important characteristic and also the main objective for the development of conventional backscattering communication systems. This special communication feature of CBCSs has received a great deal of attention, mainly because of the successful implementation of RFID systems and the potential use in sensor devices that are small in size and have a low power supply.

Typically, backscattering communication systems operate using RF signals and require the WTRD to be able to transmit RF signals to the WTD. However, a new solution, called ambient backscatter communication, which utilizes RF signals from ambient sources, e.g., TV signals [3] and Wi-Fi signals [4], to help the WTRD to obtain data from the WTD without generating RF signals has recently been introduced.

Introduction

Energy harvesting has emerged as one of the enabling technologies for Green Communication. As the name suggests, energy harvesting is a technique by which energy of ambient sources, for example, solar, wind, and thermal, is converted into electrical energy and used to power network equipment or mobile devices [1, 2]. This technology makes use of perpetual renewable energy sources, which reduces the consumption of high-cost constant energy sources. Lately, wireless power transfer has gained much attention in the field of energy harvesting since wireless signal is also an ambient source of energy in itself [3, 4]. Owing to the lower range of harvested energy, RF energy harvesting has potential for powering smaller network nodes.

Wireless energy harvesting technology has been studied in two different paradigms of wireless communication networks. The first is simultaneous wireless information and power transfer (SWIPT) in downlink [5–7]. The SWIPT technique is used to transmit wireless energy to user equipments (UEs) whilst carrying out downlink information transmission to them as shown in Figure 4.1. The technological requirements necessary to enable this simultaneous information and energy transmission at the receiver circuit will be discussed later in this chapter. The SWIPT technique increases the energy efficiency of the network since the energy cost decreases for UEs capable of harvesting energy from the received information signal [8]. In other words, energy spent at the information transmitter is partially reused at the receiver. The reuse of energy is partial mainly because of the path-loss and non-ideal energy harvesting efficiency.

On the other hand, when the UEs are carrying out uplink transmission, it is not possible to transmit energy to them using the SWIPT technique. Hence, another important paradigm of wireless energy harvesting networks is wireless-powered communication (WPC) in the uplink [9, 10]. In this technique, some fraction of the uplink transmission duration is dedicated to downlink energy transmission (DET) from the access point (AP) to the UEs. The energy harvested during this time is utilized by the UEs for uplink information transmission (UIT) to the AP in the remaining fraction of time as shown in Figure 4.2.

Recently, there has been an upsurge of research interest in wireless-powered communication networks. These networks are based on energy harvesting and/or energy transfer technology, for mobile devices using wireless propagation media. This technology offers the capability of using different types of wireless medium, such as radio frequency and magnetic induction, to carry energy from dedicated sources to wireless nodes or to harvest energy from ambient sources. Therefore, this has become a promising solution to power energy-constrained wireless networks. Conventionally, energy-constrained wireless networks such as wireless sensor networks have a limited lifetime, which leads to significant deterioration in network performance and usability. By contrast, a network with wireless energy harvesting and transfer capability can be powered without using a fixed power supply. For example, it can harvest energy from environmental sources such as solar and wind energy or from other dedicated or non-dedicated sources which are tetherless. Hence, there is no need to charge or replace the batteries physically, which can improve the flexibility and availability of the network substantially. Wireless energy has many advantages over other energy sources, including indoor support and stable and more predictable supply.

There are three major types of wireless energy harvesting and transfer technique, namely, radio frequency (RF), inductive coupling, and magnetic resonance coupling techniques. In RF energy harvesting, radio signals with frequencies in the range from 3 kHz to 300 GHz are used as a medium to carry energy in the form of electromagnetic radiation. Inductive coupling is based on magnetic coupling that delivers electrical energy between two coils tuned to resonate at the same frequency. The electric power is carried through the magnetic field between two coils. Magnetic resonance coupling utilizes evanescent-wave coupling to generate and transfer electrical energy between two resonators. The resonator is formed by adding a capacitance on an induction coil. Inductive coupling and magnetic resonance coupling are near-field wireless transmission techniques featuring high power density and conversion efficiency. By contrast, RF energy transfer can be regarded as a far-field energy transfer technique. It is suitable for powering a larger number of devices distributed over a wide area. Wireless energy harvesting and transfer have found many applications and have recently been implemented in many devices, including mobile phones, healthcare devices, sensors, and RFID tags.

Introduction

A cognitive radio network (CRN) is an intelligent radio network in which unlicensed users (i.e., secondary users) can opportunistically access idle channels when such channels are not occupied by licensed channels (i.e., primary users). The main purpose of CRNs is to utilize available spectrum efficiently, since spectrum is becoming more and more scarce due to the boom in wireless communication systems. Basically, we can define a cognitive radio as a radio that can change its transmitter parameters as a result of interaction with the environment in which it operates [1]. From this definition, there are two main characteristics of cognitive radio that are different from traditional communication systems, namely cognitive capability and reconifigurability [2].

1. • Cognitive capability. This characteristic enables cognitive users to obtain necessary information from their environment.

2. • Reconfigurability. After gathering information from the environment, the cognitive users can dynamically adjust their operating mode to environment variations in order to achieve optimal performance.

To support these characteristics of cognitive radio, there are four main functions that need to be implemented for cognitive users.

1. • Spectrum sensing. The goal of spectrum sensing is to determine the channel status and the activity of the licensed users by periodically sensing the target frequency band.

2. • Spectrum analysis. The information obtained from spectrum sensing is used to schedule and plan for spectrum access.

3. • Spectrum access. After a decision has been made on the basis of spectrum analysis, unlicensed users will be allocated access to the spectrum holes.

4. • Spectrum mobility. Spectrum mobility is a function related to the change of operating frequency band of cognitive users. The spectrum change must ensure that data transmission by cognitive users can continue in the new spectrum band.

Under these functions, cognitive users can utilize the limited spectrum resource in a more efficient and flexible way.

Recently, the development of wireless power transfer technologies has brought a new research direction, called wireless-powered cognitive radio networks (CRNs), which would seem to provide a promising solution for energy conservation for CRNs. In wireless-powered CRNs, secondary users are able to harvest energy wirelessly and then use the harvested energy to transmit data to the primary channels opportunistically.

The ever-increasing use of smart phone devices, multimedia applications, and social networking, along with the demand for higher data rates, ubiquitous coverage, and better quality of service, pose new challenges to the traditional mobile wireless network paradigm that depends on macro cells for service delivery. Small cell networks (SCNs) have emerged as an attractive paradigm and hold great promise for future wireless communication systems (5G systems). SCNs encompass a broad variety of cell types, such as micro, pico, and femto cells, as well as advanced wireless relays, and distributed antenna systems. SCNs co-exist with the macro cellular network and bring the network closer to the user equipment. SCNs require low power, incur low cost, and provide increased spatial reuse. Data traffic offloading eases the load on the expensive macro cells with significant savings expected to the network operators using small cells.

As the demand for increased bandwidth rages on, SCNs emerged in dense urban areas mainly to provide coverage and capacity. They have now gained momentum and are expected to dominate in the coming years, with the rollout in large scale – either planned or in ad-hoc manner – and the development of 5G systems with many small cell components. Already, the number of “small cells” in the world exceeds the total number of traditional mobile base stations. SCNs are also envisioned to pave the way for new services. However, there are many challenges in the design and deployment of small cell networks, which have to be addressed in order to be technically and commercially successful. This book provides various concepts in the design, analysis, optimization, and deployment of small cell networks, using a treatment approach suitable for pedagogical and practical purposes.

This book is an excellent source for understanding small cell network concepts, associated problems, and potential solutions in next-generation wireless networks. It covers from fundamentals to advanced topics, deployment issues, environmental concerns, optimized solutions, and standards activities in emerging small cell networks. New trends, challenges, and research results are also provided. Written by leading experts in the field from academia and industry around the world, it is a valuable resource dealing with both the important, core, and specialized issues in these areas. It offers a wide coverage of topics, while balancing the treatment to suit the needs of first-time learners of the concepts and specialists in the field.

We disseminate a set of small cells' field trial experiments conducted at 2.6 GHz and focused on coverage/capacity within multi-floor office buildings. LTE pico cells deployed indoors as well as LTE small cells deployed outdoors are considered. The latter rely on small emission power levels coupled with intelligent ways of generating transmission beams with various directivity levels by means of adaptive antenna arrays. Furthermore, we introduce an analytical three-dimensional (3D) performance prediction framework, which we calibrate and validate against field measurements. The framework provides detailed performance levels at any point of interest within a building; it allows us to determine the minimum number of small cells required to deliver desirable coverage and capacity levels, their most desirable location subject to deployment constraints, transmission power levels, antenna characteristics (beam shapes), and antenna orientation (azimuth, tilt) to serve a targeted geographical area. In addition, we disseminate specialized solutions for LTE small cells’ deployment within hotspot traffic venues, such as stadiums, through design and deployment feasibility analysis.

Introduction

Small cells are low-cost, low-power base stations designed to improve coverage and capacity of wireless networks. By deploying small cells on top and in complement to the traditional macro cellular networks, operators are in a much better position to provide the end users with a more uniform and improved quality of experience (QoE). Small cells’ deployment is subject to service delivery requirements, as well as to the actual constraints specific to the targeted areas. For a good uniformity of service, in populated areas where the presence of buildings is the main reason for significant radio signal attenuation, small cells may need to be closely spaced, e.g., within a couple of hundred meters from each other. Naturally, the performance of small cells is highly dependent on environment-specific characteristics, such as the materials used for building construction, their specific propagation properties, and the surroundings. It is particularly important to have a proper characterization of an environment where small cells are deployed.

This chapter focuses on in-building performance and feasibility of LTE small cells through measurements, taking as reference both outdoor small cell and indoor pico cell deployments. We created scenarios where wireless connectivity within a target building is offered either by small cells located on the exterior of other buildings (small cells with outdoor characteristics) or simply by small cells located within the target building (pico cells with indoor characteristics).

Small cell networks

The tremendous increase of bandwidth-craving mobile applications (e.g., video streaming, video chatting, and online gaming) has posed enormous challenges to the design of future wireless networks. Deploying small cells (e.g., pico, micro, and femto) has been shown to be an efficient and cost-effective solution to support this constantly rising demand since the smaller cell size can provide higher link quality and more efficient spatial reuse [1]. Small cells could also deliver some other benefits such as offloading the macro network traffic, providing service to coverage holes and regions with poor signal reception (e.g., macro cell edges). Following this trend, the evolving 5G networks [2] are expected to be composed of hundreds of interconnected heterogeneous small cells.

Figure 11.1 gives an illustration of a heterogeneous network (HetNet) where a macro cell is underlaid with different types of small cells. Different from the cautiously planned traditional network, the architecture of a HetNet is more random and unpredictable due to the increased density of small cells and their impromptu way of deployment. In this case, the manual intervention and centralized control used in traditional network management will be highly inefficient, time consuming, and expensive, and therefore will be not applicable for dense heterogeneous small cell networks. Instead, self-organization has been proposed as an essential feature for future small cell networks [3, 4].

The motivations for enabling self-organization in small cell networks are explained below.

1. • Numerous network devices with different characteristics are expected to be interconnected in future wireless networks. Also, these devices are expected to have “plug and play” capability. Therefore the initial pre-operational configuration has to be done with minimum expertise involvement.

2. • With the emergence of small cells, the spatio-temporal dynamics of the networks has become more unpredictable than legacy systems due to the unplanned nature of small cell deployment. Therefore intelligent adaptation of the network nodes is necessary. That is, the self-organizing small cells need to learn from the environment and adapt with the network dynamics to achieve the desired performance.

Introduction

This chapter introduces novel approaches in heterogeneous networks (HetNets) where both large and small cells are deployed in a mixed manner to satisfy the increasing traffic demand and, at the same time, to improve the energy efficiency (EE) of future cellular networks.

In recent years, there has been a tremendous increase in the number of mobile handsets, in particular smart phones, supporting a wide range of applications, such as image and video transfer, cloud services, and cloud storage. The average smart phone usage rate has nearly been tripled and the overall amount of mobile data traffic demand grew 2.3 times in 2011 [1]. Furthermore, the amount of mobile data traffic is expected to increase dramatically in the coming years; recent forecasts are expecting the data traffic to increase more than 500 times in the next ten years [2, 3]. The current cellular systems would not be able to cope with the expected traffic demand increase. This huge amount of traffic demand leads to the need for further densification of the networks, for example in hotspot areas where traffic demand is concentrated as seen in Figure 18.1.

However, traffic load varies from time to time because of the typical night–day behavior due to the users’ daily activities in offices and being back to residential areas during the night [4]. In the current cellular networks, the power consumption of the radio access network (RAN) does not effectively scale with the traffic variations as shown in Figure 18.2. The traffic variations create the opportunities for the design of an adaptive network paradigm that can dynamically scale its power consumption according to the traffic variations.

Generally speaking, the power consumption of the RAN scales with the number of deployed base stations (BSs), each with offset power consumption. In cellular networks, only 10% of the overall power consumption stems from the user equipments (UEs) whereas nearly 90% of power consumption is incurred by the operator networks [5]. Figure 18.3 gives an idea on how the power consumption is distributed across the different parts of a typical cellular network. It is obvious that the RAN and the operation of data centers that provide computations, storage, applications, and data transfer are the most energy intensive parts of the entire network.

Multi-tier, heterogeneous networks (HetNets) using small cells (e.g., pico and femto cells) are an important part of operators’ strategy to add low-cost network capacity through aggressive reuse of the cellular spectrum. In the near term, a number of operators have also relied on un-licensed WiFi networks as a readily available means to offload traffic demand. However, the use of WiFi is expected to remain an integral part of operators’ long-term strategy to address future capacity needs, as licensed spectrum continues to be scarce and expensive. Efficient integration of cellular HetNets with alternate radio access technologies (RATs), such as WiFi, is therefore essential for next-generation networks.

This chapter describes several WiFi-based multi-RAT HetNet deployments and architectures, and evaluates the associated performance benefits. In particular, we consider deployments featuring integrated multi-RAT small cells with co-located WiFi and LTE interfaces, where tighter coordination across the two radio links becomes feasible. Integrated multi-RAT small cells are an emerging industry trend toward leveraging common infrastructure and lowering deployment costs when the footprints of WiFi and cellular networks overlap. Several techniques for cross-RAT coordination and radio resource management are reviewed and system performance results showing significant capacity and quality service gains are presented.

Introduction

Multi-tier HetNets based on small cells (e.g. pico cells, femto cells, relay cells, WiFi APs, etc.) are considered to be a fundamental technology for cellular operators to address capacity and coverage demands of future 5G networks. Typical HetNet deployment architectures comprise an overlay of a macro cell network with additional tiers of densely deployed cells with smaller footprints, such as picos, femtos, relay nodes, WiFi access points, etc. Figure 2.1 illustrates the various deployment options in a multi-radio HetNet.

HetNets allow for greater flexibility in adapting the network infrastructure according to the capacity, coverage, and cost needs of a given deployment. As shown, the macro base station tier may be used for providing wide area coverage and seamless mobility, across large geographic areas, while smaller inexpensive low-powered small cells may be deployed, as needed, to improve coverage by moving infrastructure closer to the clients (such as for indoor deployments), as well as to add capacity in areas with higher traffic demand. Conceptually, mobile clients with direct client-to-client communication may also be considered as one of the tiers within this hierarchical deployment, wherein the clients can cooperate with other clients to locally improve access in an inexpensive manner.

How many small cell (SC) access points (APs) are required to guarantee a chosen quality of service in a heterogeneous network? In this chapter, we answer this question considering two different network models. The first is the downlink of a finite-area SC network where the locations of APs within the chosen area are uniformly distributed. A key step in obtaining the closed-form expressions is to generalize the well-accepted moment matching approximation for the linear combination of lognormal random variables. For the second model, we focus on a two-layer downlink heterogeneous network with frequency reuse-1 hexagonal macro cells (MCs), and SC APs that are placed at locations that do not meet a chosen quality of service from macro base stations (BSs). An important property of this model is that the SC AP locations are coupled with the MC coverage. Here, simple bounds for the average total interference within an MC makes the formulation possible for the percentage of MC area in outage, as well as the required average number of SCs (per MC) to overcome outage, assuming isolated SCs.

Introduction

Heterogeneous cellular networks (HCNs) are being considered as an efficient way to improve system capacity as well as effectively enhance network coverage [1, 2]. Comprising multiple layers of access points (APs), HCNs encompass a conventional macro cellular network (first layer) overlaid with a diverse set of small cells (SCs) (higher layers). Cell deployment is an important problem in heterogeneous networks, both in terms of the number and positioning of the SCs.

Traditional network models are either impractically simple (such as the Wyner model [3]) or excessively complex (e.g., the general case of random user locations in a hexagonal lattice network [4]) to accurately model SC networks. A useful mathematical model that accounts for the randomness in SC locations and irregularity in the cells uses spatial point processes, such as Poisson point process (PPP), to model the location of SCs in the network [5–10]. The independent placement of SCs from the MC layer, has the advantage of analytical tractability and leads to many useful SINR and/or rate expressions. However, even assuming that wireless providers would deploy SCs to support mobile broadband services, the dominant assumption remains that SCs are deployed randomly and independent of the MC layer [11].

In recent years the use of data services in mobile networks has notably increased, which requires a higher quality of services and data throughput capacity from operators. These requirements become much more demanding in indoor environments, where, due to the wall-penetration losses, communications suffer a higher detriment. As a solution, short-range base stations (BSs), known as femto cells [1], are proposed. Femto cells are installed by the end consumer and communicate with the macro cell system through the internet by means of a digital subscriber line (DSL), fiber, or cable connection. Due to this deployment model, the number and location of femto cells are unknown for the operators and therefore there is no possibility for centralized network planning. In the case of co-channel operation, which is the more rewarding option for operators in terms of spectral efficiency, an aggregated interference problem may arise due to multiple, simultaneous, and uncoordinated femto cell transmissions. On the other hand, the macro cell network can also cause significant interference to the femto cell system due to a lack of control of position of the femto nodes and their users. In this chapter we focus then on the challenging problem in the area of small cell networks, the inter-cell interference coordination among different layers of the network.

We propose two different self-organizing solutions, based on two smart techniques that operate on the most appropriate configuration parameters of the network for each situation. In particular, we address femto–macro and macro–femto problems. On the one hand, for the femto–macro case, we propose in Section 16.1 a machine learning (ML) approach to optimize the transmission power levels by modeling the multiple femto BSs as a multi-agent system [2], where each femto cell is able to learn transmission power policy in such a way that the interference it is generating, added to the whole femto cell network interference, does not jeopardize the macro cell system performance. To do this, we propose a reinforcement learning (RL) category of solutions [3], known as time-difference learning. On the other hand, for the macro–femto problem, we propose in Section 16.2 a solution based on interference minimization through self-organization of antenna parameters. In homogeneous macro cell networks, BS antenna parameters are configured in the planning and deployment phase and left unchanged for a long time.

Introduction

The general definition of a small cell is the low-powered radio access node operating in licensed and unlicensed spectrum with the smaller coverage of ten meters to one or two kilometers, compared to a mobile macro cell with a range of a few tens of kilometers. With the introduction of this new concept, the heterogeneous network (HetNet) constructed with different layers of small cells and large cells can deliver the increased bandwidths, reduced latencies, and higher uplink (UL) and downlink (DL) throughput to end users. Since 2009, the standard evolution of the small cell related topics has been studied in 3GPP (The 3rd Generation Partnership Project) LTE (long-term evolution) and LTE-Advanced. The following sections in this chapter will introduce the standardization progress of LTE and LTE-Advanced in small cells.

Definition of small cells in 3GPP LTE-Advanced

In 3GPP LTE and LTE-Advanced, small cells can generally be characterized as either relay nodes, or pico cells (also referred to as hotzone cells), controlled by a pico eNodeB, or femto cells, controlled by a Home evolved NodeB (HeNB). The common features among the relays, pico cells, and femto cells are low transmission power node and independent eNB functionality, while the typical different features can be summarized as follows:

1. Relay node [1, 2]. A relay node (RN) is a network node connected wirelessly to a source eNodeB, called the donor eNodeB. According to the different implementation types of the relay node into wireless network, the roles of the relay node played are also different.

2. Pico cell. A pico cell usually controls multiple small cells, which are planned by:

1. a. The 3rd Generation Partnership Project (3GPP), which unites six telecommuni-cations standard development organizations (ARIB, ATIS, CCSA, ETSI, TTA, and TTC), known as organizational partners, and provides their members with a stable environment to produce the highly successful reports and specifications that define 3GPP technologies.

2. b. The evolved Node B could be abbreviated as eNodeB or eNB by the network operator in a similar way as the macro cells [3]. The pico cell is usually open to all users (open subscriber group (OSG))[4].

Introduction

Combined with a technology upgrade to LTE/LTE-A, small cells are presented by many industry players (from operators to equipment vendors to analysts) as the most cost-effective solution to the known increase in mobile data demand [1–3]. As small cells (e.g., femto and pico) become the broadly adopted solution for adding capacity to modern, smart phone-dominated cellular networks, their numbers, and the areas where they will be deployed, will increase dramatically (Figure 17.1).

This reduction in cell size and the growth in cell numbers has required many new approaches to be developed to ensure that the next generation of networks are built to exploit costly, limited spectrum resources while maximizing capacity.

Such new methods consider the network design process in a holistic manner and ensure sufficient computational power is available to remove any accuracy compromises inherent with the traditional design processes. The set of techniques used to accomplish this we call large-scale network design, or L-SND for short.

The US cellular market has many good examples of planned small cell deployments that are to occur at a national level [1]. Results and data from such small cell designs are included in this chapter to illustrate the L-SND accuracy and scalability difficulties that have been overcome when compared to the limitations found with traditional methods.

Large-scale network design

Large-scale network design groups advanced radio-planning algorithms and technical solutions to evaluate vast numbers of sites without overloading engineering resources. Big data, cloud computing, and optimized metaheuristic algorithms are basic parts of the pool of multiple components that have to be combined to solve the small cell challenge.

The methodology described in this chapter has been implemented in Overture and the results shown have been shared by Keima Technologies.

Following model-based control theory [4, 5], L-SND aims to reduce the gap between business-case analysis and deployment. By utilizing the advantages of big data and cloud computing it is possible to seamlessly link marketing, planning, and deployment information. As the analysis evolves, robustness is built into the study by integration of feedback and new or updated big data components to review the objectives. In a real-life scenario, such systems will utilize practical and realistic information to evaluate outcomes and to maximize the advantages of economies of scale through a quicker (but more detailed) design phase.

The traditional mobile cellular networks are often designed so that a base station (BS) is always under uninterrupted working condition without considering the dynamic nature of user traffic, which results in an inefficient usage of energy. How to improve the system energy efficiency in order to achieve green networking is our major concern in this chapter. Beginning with a comprehensive review of the related works in literature, we introduce a self-organized BS virtual small networking (VSN) protocol so as to adaptively manage BSs' working states based on heterogeneity of user traffic changing in space and time. Motivated by the fact that low-traffic areas can apply a more aggressive BS-off strategy than hotspots, the proposed method is targeted at dividing BSs into groups with some similarity measurements so that the BS-off strategy can be performed more efficiently. Numerical results show that our proposals can save energy consumption on the entire cellular network to a great extent.

Introduction

As demand increases for more energy-efficient technologies in wireless networks, to tackle critical issues such as boosting cost on power consumption and excessive greenhouse gas emissions, the concept of green networking has drawn great attention in recent years. In fact, during the last decades, people have witnessed that the carbon footprint of the telecommunications industry has been exponentially growing due to the explosive rise of service requirements and subscribers’ demands. The concern on reducing power consumption comes from both environmental and economical reasons. With respect to the environment, the information and communications technology (ICT) industry is responsible for approximately 2% of current global electricity demands, with 6% yearly growth in ICT-related carbon dioxide emission (CO2-e) forecast till 2020 [1]. With respect to economics, the power consumption for operating a typical base station (BS), which needs to be connected to the electrical grid, may cost approximately $3,000/year, while off-grid BSs, generally running on diesel power generators in remote areas, may cost ten times more [2]. As more than 120,000 new BSs are deployed annually [3], there is still no end in sight for the development of mobile communications with a large amount of new subscribers and a constant desire for upgrading user equipment from 2G to 3G, and then to 4G.