Introduction

Background. The degrees of freedom introduced by multiple antennas at the transmitters and receivers of wireless communication systems facilitate multiplexing gains and diversity gains. A wireless point-to-point link with M transmit and N receive antennas constitutes an M-by-N multiple-input multiple-output (MIMO) communication system. The ergodic capacity of an M-by-N MIMO fading channel increases almost linearly with min{M,N} provided that the fading meets certain mild conditions. Hence, it is not surprising that MIMO has attracted a lot of research interest since it enables significant performance and throughput gains without requiring extra transmit power and bandwidth. However, limitations on the number of antennas that a wireless device is able to support as well as the significant signal processing power and complexity required in MIMO tranceivers limit the gains that can be achieved in practice.

To overcome the limitations of traditional MIMO, the concept of cooperative communication has been proposed for wireless networks such as fixed infrastructure cellular networks and wireless ad-hoc networks. The basic idea of cooperative communication is that the single-antenna terminals of a multiuser system can share their antennas and create a virtual MIMO communication system. Thereby, three different types of cooperation may be distinguished, namely, user cooperation, base station (BS) cooperation, and relaying. Theoretically, user cooperation and BS cooperation are able to provide huge performance gains, when compared with noncooperative networks. However, the required information exchange between users and BSs may make these options less attractive in practice.

Introduction

Cellular communication systems provide wireless coverage to mobile users across potentially large geographical areas, where base stations (BSs) provide service to users as interfaces to the public telephone network. Cellular communication is based on the principle of dividing a large geographical area into cells which are serviced by separate BSs. Rather than covering a large area by using a single, high-powered BS, cellular systems employ many lower-powered BSs each of which covers a small area. This allows for the reuse of the frequency bands in cells which are not too close to each other, increasing mobile user capacity with a limited spectrum allocation.

Traditional narrowband cellular systems require the cochannel interference level to be low. Careful design of frequency reuse among cells is then crucial to maintain cochannel interference at the required low level. The price of low interference, however, is a low frequency reuse factor: only a small portion of the system frequency band can be used in each cell. More recent wideband approaches allow full frequency reuse in each cell, but the cost of that is increased intercell interference. In both approaches, the capacity of a cell in a cellular network, with six surrounding cells, is much less than that of a single cell operating in an intercell interference-free environment. In this chapter, we survey an approach that allows the cell with neighbors to achieve essentially the same capacity as the interference-free cell.

Introduction

The continually increasing number of users and the rise of resource-demanding services require a higher link data rate than the one that can be achieved in current wireless networks. Wireless cellular networks, in particular, have to be designed and deployed with unavoidable constraints on the limited radio resources such as bandwidth and transmit power. As the number of new users increases, finding a solution to meet the rising demand for high data rate services with the available resources has became a challenging research problem. The primary objective of such research is to find solutions that can improve the capacity and utilization of the radio resources available to the service providers. While in traditional infrastructure networks the upper limit of the source destination (S–D) link's data capacity is determined by the Shannon capacity, advances in radio transceiver techniques such as multiple-input multiple-output (MIMO) architectures and cooperative or relay-assisted communications have led an enhancement in the capacity of contemporary systems.

In the MIMO technique the diversity relies on uncorrelated channels, and is achieved by employing multiple antennas at the receiver side, the transmitter side, or both, and by sufficiently separating the multiple antennas (of same polarization). The MIMO technique can be used to increase the robustness of a link as well as the link's throughput. Unfortunately, the implementation of multiple antennas in most modern mobile devices may be challenging due to their small sizes.

Introduction

The spectral efficiency of existing cellular networks is limited by interference. In cellular mobile networks, the dominant interference comes from adjacent cells. This is especially true when the users are located near the cell edges where the interference from the adjacent cells is very strong. By getting the adjacent base stations (BS) to cooperate, spatial antenna diversity in each BS can be utilized to cancel the interference. To obtain BS cooperation, multiple BSs share information about the transmitted messages to their respective users and wireless channels via a backbone network. Each BS can transmit either a single symbol stream or multiple symbol streams to its respective mobile station (MS). Individual BSs and MSs are equipped with multiple transmit and receive antennas, respectively. Each BS transmitter uses the transmitted signal information from other BSs and wireless channel conditions to precode its own signal. The precoded signal for each BS is broadcast through all BS transmit antennas in the same frequency band and time slots. The precoding operation and transmit receive antenna coefficients are chosen in such a way as to minimize the interference coming from other BS transmissions. The calculated receive antenna coefficients are then sent from the transmitter to the receiver through a wireless channel prior to the data transmission. In this chapter, we consider the use of a cooperative BS system to eliminate the interference in cellular networks.

Introduction

Wireless cellular networks have to be designed and deployed with unavoidable constraints on the limited radio resources such as bandwidth and transmit power. With the boom in the number of new users and the introduction of new wireless cellular services that require a large bandwidth or data rate, the demand for these resources, however, is rising exponentially. Finding a solution to meet this increasing demand with the available resources is a challenging research problem. The primary objective of such research is to find solutions that can improve the capacity and utilization of the radio resources that are available to the service providers. Based on the concept of relay channels, cooperative communication has been found to greatly enhance the performance of a resource-constrained wireless network. It can achieve benefits similar to those of the multipleinput multiple-output (MIMO) system without the need for multiple antennas at each node. By allowing users to cooperate and relay each other's messages to the destination, cooperative communication also improves the transmission quality. Because of the limited power and bandwidth resources of the cellular networks and the multipath fading nature of the wireless channels, the idea of cooperation is particularly attractive for wireless cellular networks.

Proposed cooperative schemes or strategies, such as decode-and-forward (DF), amplify-and-forward (AF), and coded cooperation, usually involve two steps of operation.

Introduction

The cellular structure is a central concept in wireless network deployment. A wireless cellular network comprises base stations geographically located at the centre of each cell serving users within the cell boundary. The assignment of the users to the base stations depends on the relative channel propagation characteristics. As a mobile device can usually receive signals from multiple base stations, the mobile is typically assigned to the base station with the strongest channel gain. Signals from all other base stations are regarded as intercell interference. However, at the cell edge, it is often the case that the propagation path-losses from two or more base stations are similar. In this case, the signal-to-noise-and-interference ratio (SINR) could be close to 0 dB, even if the mobile is assigned to the strongest base station. To avoid excessive intercell interference in these cases, traditional cellular networks employ a fixed frequency reuse pattern so that neighboring base stations do not share the same frequency. In this manner, neighboring cells are separated in frequency so that cell-edge users do not interfere with each other.

The traditional fixed frequency reuse schemes are effective in minimizing intercell interference, but are also resource intensive in the sense that each cell requires a substantial amount of nonoverlapping bandwidth, so that only a fraction of the total bandwidth can be made available for each cell. Consequently, the standardization processes for future wireless systems have increasingly targeted maximal frequency reuse, where all cells use the same frequency everywhere.

Introduction

In conventional cellular systems, cochannel interference is a serious issue that degrades system performance. The spectral efficiency of cellular networks is fundamentally limited by the interference between cells and users sharing the same channel for both downlink and uplink. Generally, there are two kinds of cochannel interference: intracell interference and intercell interference.

The intracell interference can be resolved by allocating orthogonal frequency resources. To mitigate intercell interference, there are several general approaches such as frequency reuse, cell sectoring, and spread spectrum transmission. The most commonly used technique is to avoid using the same set of frequencies in neighboring cells. This approach leads to the decrease of the number of available channels within each cell.

Universal frequency reuse, i.e., reuse factor of 1, is preferred for future broadband wireless communications systems, such as the Third Generation Partnership Project (3GPP) long-term evolution (LTE) and worldwide interoperability for microwave access (WiMAX). In the orthogonal frequency-division multiple access (OFDMA) systems, which do not have processing gain as the code-division multiple access (CDMA) system, how to achieve the goal of both universal frequency reuse and reducing intercell interference is a key challenge.

The concept of fractional frequency reuse (FFR) has been suggested to improve spectrum efficiency by applying reuse partition techniques, in which the inner region of the cell is assigned the whole frequency spectrum and the outer region is only assigned a small fraction of the frequency spectrum.

Introduction

A cellular communication system is designed based on the concept of frequency reuse, in which the same frequency resources are reused at a certain distance from a cell site. Traditionally, a cellular system has been composed of cell sites operating independently except in some inevitable scenarios like handover. Independent operation of each cell site makes it possible to deploy a wireless system at a low cost, while maintaining the quality of the voice service. However, due to the large amount of interference from neighboring cell sites, cell-edge users experience bad channel conditions. Furthermore, the cell-edge interference becomes more severe, when the frequency reuse factor is 1, which is a common assumption for cellular systems designed for high capacity. Therefore, with the conventional cellular designs, it is difficult to achieve high data throughput for users located at cell edges.

However, as the need for high-speed data communication increases, cooperative communications between the neighboring cell sites and UEs are being more intensively studied not only in academia but also in industry. One of the main focuses of these studies is to increase the data throughput for the cell-edge UEs. To increase the throughput of the cell-edge UEs, neighboring eNodeBs cooperate to enhance the signal quality and/or decrease the interference level. Coverage extension through a wireless relay is another research focus of cooperative communication. Interference management and cooperation between eNodeBs are important issues in a heterogeneous network.

Introduction

In this chapter, we consider space-time coding strategies for multiple-relay cooperative systems that effectively harness available spatial diversity. More specifically, the goal is to examine ways to forward signals efficiently from multiple relays to the destination while addressing the important practical issue of synchronization among the relays. We assume a general two-phase transmission protocol as illustrated in Figure 6.1. In the first phase of the protocol, the source broadcasts a message which is received by the relays and (possibly) the destination. During the second transmission phase, a subset of the relays, possibly in conjunction with the source, transmits additional information to the destination. This protocol is useful in practical scenarios where signals received at the destination due to transmissions directly from the source (Phase 1) will not carry enough useful information because of noise, fading, and/or interference.

It is expected that Phase 2 will dramatically increase the reliability of the system, but if the symbols cannot be decoded correctly after the second phase, the protocol can restart by returning to Phase 1 or Phase 2.

The primary problem associated with forwarding information from multiple relays to the destination is determining how the information should be spread out among the relays over space and time. This is analogous to the classic spacetime coding problem in point-to-point multiple-transmit-antenna systems, and so it is often called the distributed space-time coding problem.

Introduction

Future communication systems will be decentralized and ad-hoc, hence allowing various types of network mobile terminals to join and leave. This makes the whole system vulnerable and susceptible to attacks. Anyone within communication range can listen to and possibly extract information. While these days we have numerous cryptographic methods to ensure high-level security, there is no system with perfect security on the physical layer. Therefore, the physical layer security is attracting renewed attention. Of special interest is so-called information-theoretic security since it concerns the ability of the physical layer to provide perfect secrecy of the transmitted data.

In this chapter, we present different scenarios of a decentralized system that protects the broadcasted data on the physical layer and makes it impossible for the eavesdropper to receive the packets no matter how computationally powerful the eavesdropper is. In approaches where information-theoretic security is applied, the main objective is to maximize the rate of reliable information from the source to the intended destination, while all malicious nodes are kept ignorant of that information. This maximum reliable rate under which a perfectly secret communication is possible is known as the secrecy capacity.

This line of work was pioneered by Aaron Wyner, who defined the wiretap channel and established the possibility of secure communication links without relying on private (secret) keys.

Introduction

Cooperation among wireless nodes has attracted significant attention as a novel networking paradigm for future wireless cellular networks. It has been demonstrated that, by using cooperation at different layers (physical layer, multiple access channel (MAC) layer, network layer), the performance of wireless systems such as cellular networks can be significantly improved. In fact, cooperation can yield significant performance improvement in terms of reduced bit error rate (BER), improved throughput, efficient packet forwarding, reduced energy, and so on. In order to reap the benefits of cooperation, efficient and distributed cooperation strategies must be devised in future wireless networks. Designing such cooperation protocols encounters many challenges. On the one hand, any cooperation algorithm must take into account not only the gains but also the costs from cooperation which can both be challenging to model. On the other hand, the wireless network users tend to be selfish in nature and aim at improving their own performance. Therefore, giving incentives for these users to cooperate is another major challenge. Hence, there is a strong need to design cooperative strategies that can be implemented by the wireless nodes, in a distributed manner, while taking into account the selfish goals of each user as well as all the gains and losses from this cooperation.

This chapter describes analytical tools from game theory that can be used to model the cooperative behavior in wireless cellular networks.

Cooperative communications and networking represent a new paradigm which uses both transmission and distributed processing to significantly increase the capacity in wireless communication networks. Current wireless networks face challenges in fulfilling users’ ever-increasing expectations and needs. This is mainly due to the following reasons: lack of available radio spectrum, the unreliable wireless radio link, and the limited battery capacity of wireless devices. The evolving cooperative wireless networking paradigm can tackle these challenges. The basic idea of cooperative wireless networking is that wireless devices work together to achieve their individual goals or one common goal following a common strategy. Wireless devices share their resources (i.e., radio link, antenna, etc.) during cooperation using short-range communications. The advantages of cooperation are as follows: first, the communications capability, reliability, coverage, and quality-of-service (QoS) of wireless devices can be enhanced by cooperation; second, the cost of information exchange (i.e., transmission power, transmission time, spectrum, etc.) can be reduced. Cooperative communication and networking will be a key component in next generation wireless networks. In this book we particularly focus on cooperative transmission techniques in cellular wireless networks.

Although cellular wireless systems are regarded as a highly successful technology, their potential in throughput and network coverage has not been fully realized. Cooperative communication is a key technique to harness the potential throughput and coverage gains in these networks.

Introduction

Since its inception in information theory, network coding has attracted a significant amount of research attention. After theoretical explorations in wired networks, the use of network coding in wireless networks to improve throughput has been widely recognized. In this chapter, we present a survey of advances in relay-based cellular networks with network coding. We begin with an introduction to network coding theory with a focus on wireless networks. We discuss various network coded cooperation schemes that apply network coding on digital bits of packets or channel codes in terms of, for example, outage probability and diversity–multiplexing tradeoff. We also consider physical-layer network coding which operates on the electromagnetic waves and its application in relay-based networks. Then we take a networking perspective, and present in detail some scheduling and resource allocation algorithms to improve throughput using network coding in relay-based networks with a cross-layer perspective. Finally, we conclude the chapter with an outlook into future developments.

Network coding was first proposed in for noiseless wireline communication networks to achieve the multicast capacity of the underlying network graph. The essential idea of network coding is to allow coding capability at network nodes (routers, relays, etc.) in exchange for capacity gain, i.e., an alternative tradeoff between computation and communication. This can be understood by considering the classic “butterfly” network example. In Figure 12.1, suppose the source S wants to multicast two bits a and b to two sinks D1 and D2 simultaneously.

Introduction

Direct transmission from source to destination often faces weaker channel conditions when a mobile is moving across the cell border, because of the large propagation loss due to path-loss and shadowing, and the power limitation not to cause undue interference. For this reason, attention has been given to the use of cooperative relaying to mitigate intercell interference to abtain an increased rate and extended coverage at cell edge.

There have been many proposals for cooperative relaying, such as amplify-and-forward (AF), decode-and-forward (DF), and compress-and-forward (CF), some of which were developed in. Such relaying schemes are mainly designed to exploit the multipath diversity for a power gain (or increased rate) that results from combining direct and relayed signals. However, these schemes do not fully utilize the asymmetric link capacity in direct (source–destination) and relay (source–relay) links, e.g., where the latter gives better results in the downlink if line-of-sight (LoS) transmission is realized in the link between the base station and a fixed relay.

A partial DF protocol has been proposed in that aims to exploit the asymmetric link capacity more efficiently by forwarding a part of the decoded information to the destination using superposition coding. Further, Popovski and de Carvalho investigated a power division between the basic data and the superposed data that result from superposition coding, for a maximum overall rate capacity.