Traditionally, the spectrum allocation policy grants fixed spectrum bands to licensed users for exclusive access, which worked well over the past decades. However, with the proliferation of wireless services and data volume in recent years, spectrum scarcity, as a major drawback of this static spectrum allocation policy, has been unveiled. Meanwhile, it is observed that a significant amount of the licensed spectrum is rather underutilized.

As an opposite of the conventional static spectrum management policy, the concept of dynamic spectrum access has been proposed to increase the flexibility in spectrum usage, so as to alleviate the spectrum scarcity problem and improve spectrum utilization. In the existing literature, Dynamic Spectrum Access (DSA) models can be categorized as follows: exclusive-use, shared-use, and commons models. In the exclusive-use model, a licensed user can grant an unlicensed user the spectrum access rights to have exclusive access to the spectrum. In a shared-use model, an unlicensed user accesses the spectrum opportunistically without interrupting a licensed user. In a commons model, an unlicensed user can access the spectrum freely. DSA can be implemented in a centralized or a distributed network architecture. DSA can be optimized globally if a central controller is available in the network. On the other hand, when a central controller is not available, distributed algorithms would be required for dynamic spectrum access. Issues related to spectrum trading, such as pricing, will also need to be considered for dynamic spectrum access, especially with the exclusive-use model. For DSA-based radio networks, MAC protocols designed for traditional wireless networks have to be modified to include spectrum sensing, spectrum access, as well as spectrum trading between a licensed user and an unlicensed user.

Most current dynamic spectrum access paradigms are designed for HD devices with the basic assumption that data transmission and reception of any devices must be separated in the time or frequency domain. Recently, with the rapid development of self-interference suppression techniques, FD communication rapidly extends its application to different scenarios. In FD communications where co-channel simultaneous data transmission and reception becomes possible, many more possible communication modes among communication devices arise. For example, two FD devices can perform bidirectional transmission to each other; one FD devices can receive data from a source, and transmits data to another destination concurrently on the same channel.

Overview

With more and more new multimedia-rich services being introduced and offered to a rapidly growing population of global subscribers, there is an ever-increasing demand for higher data rate wireless access, making more efficient use of this precious resource a crucial need. As a consequence, new wireless technologies such as Long Term Evolution (LTE) and LTE-Advanced have been introduced. These technologies are capable of providing high-speed, large-capacity, and guaranteed quality-of-service (QoS) mobile services.With the technological evolution of cellular networks, new techniques, such as small cells, have also been developed to further improve the network capacity by effectively reusing the limited radio spectrum. However, all existing wireless communication systems deploy half-duplex (HD) radios which transmit and receive the signals in two separate/orthogonal channels. They dissipate the precious resources by employing either time-division or frequency-division duplexing.

Full-duplex (FD) systems, where a node can send and receive signals at the same time and frequency resources, can offer the potential to double spectral efficiency; however, for many years it has been considered impractical. This is because the signal leakage from the local output to input, referred to as self-interference, may overwhelm the receiver, thus making it impossible to extract the desired signal. How to effectively eliminate self-interference has remained a long-standing challenge. Recently, there has been significant progress in self-interference cancellation in FD systems, which presents great potential for realizing FD communications for the next generation of cellular networks.

This book provides state-of-the-art research on FD communications and cellular networks covering the physical, MAC, network, and application layer perspectives. The book also includes fundamental theories based on which FD communications will be built. In addition to the self-interference cancellation signal processing algorithms, the book discusses physical layer algorithms, radio resource allocation and network protocols in the practical design and implementation of centralized and distributed FD wireless networks. Main applications such as FD cognitive radio networks, FD cooperative networks, and FD heterogeneous networks are explored.

The key features of this book are as follows:

1. • A unified view of FD communications and networking;

2. • A comprehensive review of the state-of-the-art research and key technologies of FD communications networks;

3. • Coverage of a wide range of techniques for design, analysis, optimization, and application of FD communications networks;

4. • An outline of the key research issues related to FD communications and networking.

Introduction

Wireless communications, together with its underlying applications, is among today's most active areas of technology development, with the demand for data rates expected to grow to unprecedented levels by 2020. Cisco's latest report predicts a monthly mobile traffic of 24.3 exabytes (260 bytes) in 2019, which represents a 57% compound annual growth rate compared with 2014 [1]. The catalyst for this seminal development is 5G, the fifth generation of wireless systems, which denotes the next major phase of mobile telecommunications standards beyond the current fourth generation (4G) International Mobile Telecommunications-Advanced (IMT-Advanced) standards and promises speeds far beyond what the current 4G can offer. This represents a radically new paradigm in the field of wireless communications and promises to substantially improve the area spectral efficiency (measured in bit/s/Hz/km2) and energy efficiency (EE) (measured in bit/J). According to [2], there are three symbiotic technologies that can support the required “data-rate boom”:

1. 1. extreme densification and offloading to serve more active nodes per unit area and Hz, also known as massive multiple-input multiple-output (MIMO);

2. 2. increased bandwidth, primarily by moving toward and into the millimeter wave spectrum (from 30 to 300 GHz);

3. 3. increased spectral efficiency, primarily through advances in MIMO, to support more bits/s/Hz per node.

In this chapter, we will elaborate exclusively on item 1 above, namely massive MIMO, which represents a disruptive technological paradigm and is considered by many experts as the “next big thing in wireless” [3, 4]. We will first delineate the basic principles behind the operation of massive MIMO and then review some of its applications. Finally, we will conclude this chapter by presenting some directions for future work along with open challenges in the general field of massive MIMO. Table 8.1 lists the nomenclature used in this chapter.

Massive MIMO: Basic Principles

The massive MIMO technology originates from the seminal paper of Marzetta [5], and since then has been at the forefront of wireless communications research, with numerous papers reported in the literature along with huge industrial investments. Generally speaking, in a massive MIMO topology, a number K of user terminals (UTs) communicate simultaneously with a base station (BS) over the same time–frequency resources.

Introduction

To meet the dramatic growth in wireless data traffic driven by the popularity of new mobile devices and mobile applications, the fifth generation (5G) of cellular technology has recently attracted a lot of research interest from both academia and industry (see, e.g., [1]). As compared with its fourth generation (4G) counterpart, 5G is expected to achieve a roughly 1000 times data rate increase via dense base station (BS) deployments and advanced physical layer communication techniques [1]. However, the large number of BSs will lead to large energy consumption and high electricity bills for cellular operators, which amounts to a large portion of their operational expenditure. For example, China Mobile owned around 920000 BSs in 2011 and the total energy cost per year was almost 3 billion US dollars, given that the annual cost for each BS is about 3000 US dollars [2]. Therefore, in the 5G era, it is becoming necessary for these cellular operators to reduce their energy costs by employing new cost-saving solutions in the design of cellular BSs, which are our main focus in this chapter.

In general, these cost-saving solutions can be categorized into two classes, which manage the energy supply and the communication demand of cellular BSs, respectively [2–5]. On the supply side, one commonly adopted solution is to use energy harvesting devices (e.g., solar panels and wind turbines) at cellular BSs, which can harvest cheap and clean renewable energy to reduce or even substitute for the energy purchased from the grid [5]. However, since renewable energy is often randomly distributed in both time and space and cellular BSs are very energy-hungry, it is very difficult to solely use different BSs’ individually harvested energy to power their operation. As a result, the power grid is still needed to provide reliable energy to BSs. Besides serving as a reliable energy supply, the power grid also provides new opportunities for saving the BSs’ costs with its ongoing paradigm shift from the traditional grid to the smart grid. Unlike the traditional grid, which uses a one-way energy flow to deliver power from central generators to electricity users, the smart grid deploys smart meters at end users to enable both two-way information and two-way energy flows between the grid and the end users [6, 7].

The continuous growth in the numbers of mobile users and the increase in data communication make deploying new techniques with increased data rates a crucial need. In other words, modern network topologies are required to give the required performance boost. Since decreasing the number of users in each cell, as well as decreasing the distance between the users and the base station, increases the total achieved capacity, therefore, small cells are required. However, it is well known that large cells are the cost-effective way to serve fast-moving users. Therefore, one solution to match these demands is to employ Heterogeneous Networks (HetNets). The main idea of HetNets is to densify the existing macro-cell by adding a mix of pico, femto and relay base stations to which users will be offloaded from the macro base station. This will help in increasing the network capacity in hot spots and in giving better coverage for both outdoor and indoor areas not covered by the macro network.

Furthermore, both the efficiency of utilizing the available communication resources and the capacity achieved by HetNets can be further improved by deploying Full Duplex (FD). Theoretically speaking, allowing the network nodes to simultaneously transmit and receive data at the same channel and in the same time slot achieves double the capacity achieved by Half Duplex (HD) communication when using the same resources. However, it must be mentioned that FD was considered unfeasible for a long time as the increase in networks’ capacity offered by deploying FD is restricted by the ability to suppress the self-interference which is caused by the node's transmission on the node's reception and results in a great degradation in the received Signal-to-Interference Noise Ratio (SINR). Recent evolution in self-interference cancellation techniques revived the attention to FD. As mentioned, adding FD communication to HetNets will improve the network efficiency and capacity. Accordingly, efficient resource allocation techniques are needed to fully benefit from the available resources and to overcome the challenges that arise from deploying FD to HetNets.

In this chapter, we introduce Full Duplex (FD) cooperative networks, consisting of one source node, multiple relays, and one destination. We assume that the relay works in a full duplex mode. Due to the residual self-interference in FD relay nodes, the analysis of FD relay systems will become essentially different from conventional relay systems. We will analyze the performance of FD Amplify-and-Forward (FA) relay systems. The performance of FD AF relay systems is limited by the residual self-interference. To overcome such a limitation, we will introduce an effective X-duplex relaying protocol, which switches adaptively between FD and Half-Duplex (HD) spatial multiplexing modes based on the channel conditions, such that the limitations of FD and HD can be overcome through such an adaptive protocol. Finally, we consider a general setup of multi-relay FD systems and introduce a joint antenna and relay selection scheme for such a network.

Cooperative Communication Basics

Before introducing FD relay networks, in this section, we first briefly introduce some basics of cooperative communications and relaying protocols.

The concept of cooperative communications can actually be traced back to the early work of Cover and ElGamal on achievable capacity of a relay network in 1979; it was rediscovered recently in relation to great potential applications in cellular and wireless sensor networks. The distributed nature of wireless networks provides a unique opportunity for cooperation and distributed signal processing. The design of efficient cooperative protocols and distributed signal processing techniques has been an important issue in implementing cooperative communications in wireless networks. Therefore, recent research on cooperative wireless networks has focused on designing relaying protocols, signaling and distributed coding. In particular, the design of efficient relaying protocols and distributed coding schemes has attracted significant attention and a number of novel relaying protocols and distributed coding schemes have been developed in the past several years. Capacity-approaching performance has been achieved by some elegantly designed distributed coding schemes.

When we talk about “cooperative networks” and “relay networks,” they have the following distinctions. In cooperative networks, each node acts as both a source and a relay node. That is, each node not only transmits its own information but also helps other nodes to transmit signals. In relay networks, the relay nodes are explicitly built nodes only for the purpose of relaying and forwarding information.

Introduction

Direct communication between user equipment (UE) – termed device-to-device (D2D) communication – is envisioned as an intriguing solution to meet the growing demand for local wireless service in fifth generation (5G) networks. Taking advantage of physical proximity, D2D communication is blazing the trail for a flexible infrastructure and boasts the potential benefits of high spectral efficiency, low power consumption, and reduced end-to-end latency. Meanwhile, the heterogeneous network has been emerging as another promising technology for 5G, where by overlaying macrocells with a large number of small-cell access points (APs), it can provide higher coverage and throughput. The idea of using D2D communication to perform mobile relaying in a heterogeneous network is attractive, since together with the better link quality provided by the heterogeneous network in the first hop, D2D communication is able to provide flexible relay selection and enhanced link quality in the second hop, and an overall throughput improvement is therefore foreseeable. However, a problem of fairness arises as the UE relay (UER) needs to consume power to forward information to other UEs. One way to address this issue is to use energy harvesting (EH) technology, which enables devices to harvest energy from their surrounding environments. By adopting EH techniques at each UE, devices can harvest energy from the surrounding environment and use only the harvested energy for relaying, thus preventing power loss from their own battery. In this chapter, we try to coalesce EH technology, D2D communication, and the heterogeneous network into one called the D2D-communication-provided EH heterogeneous network (D2D-EHHN), and investigate the effect of different network parameters as well as provide design insights.

D2D communication has been proposed as a new way to enhance network performance by allowing UEs to communicate directly with their corresponding destinations instead of using a base station (BS) or AP [1–3]. To realize the potential advantages of D2D communication, efforts also need to be made to address the challenges that abound, including peer discovery, mode selection, and interference management in shared networks. In response, various solutions have been proposed. In particular, for resource management, methods to enhance the network throughput include allocating optimal proportions of time [4, 5] or spectrum [6] to activate D2D communication, and joint spectrum scheduling and power control [7].

Thanks to the various state-of-the-art approaches for self-IC schemes, SI is no longer a critical bottleneck when implementing a practical FD system. In this chapter, we introduce FD MIMO communications, including some key techniques and some performance analysis. The FD MIMO advantages can be summarized as: efficient and flexible utilization of wireless communication resources; increasing the capacity of the communication networks; and guaranteeing reliable communication. These have all become crucial claims for the next generation of cellular networks. Full Duplex (FD) is a very promising technique that allows for efficient use of wireless communication resources, given that the self-interference level can be suppressed to an acceptable level.

In the following, we describe a few signal processing techniques of the FD MIMO system where two nodes bidirectionally communicate with each other. Firstly, we describe the mode switching between FD and half-duplex spatial-multiplexing (HDSM). By configuring the antennas as either transmit or receive antennas, the MIMO system can be configured as either an FD system or an HD-SM system; these are considered two important techniques for improving the spectral efficiency of MIMO systems. FD transmission suffers from self interference, while the performance of an SM system is greatly affected by spatial correlation. Therefore, there is an optimal trade-off between FD and SM, depending on the system setting and channel conditions. Then, we introduce the antenna pairing strategy, where each node is equipped with two antennas, used for either transmission or reception. Specifically, one transmit and receive antenna combination is selected based on two system performance criteria: 1) maximum sum-rate (Max-SR), and 2) minimum symbol-error-rate (Min-SER). We further extend our strategy to the scenario where each node is equipped with an arbitrary number of antennas. We describe bidirectional link selection schemes by selecting a pair of transmit and receive antenna at both ends for communications in each direction, to maximize the weighted sum-rate or minimize the weighted sum SER. Then, we introduce an X-Duplex scheme, where the antenna is adaptively configured based on the channel conditions. The X-Duplex scheme aims to maximize the instantaneous sum-rate of the system. Finally, we conclude this chapter and discuss some of the key challenges in FD MIMO communications.

FD MIMO Signal Processing

Mode Switching between Full-Duplex and Half-Duplex

Typically, there are two kinds of baseline for FD and HD mode switching: a fixed number of antennas and a fixed number of RFs.