Previous chapters in this book have focused on cellular wireless networks. These were originally developed for voice use almost exclusively, as extensions of the wire-based telephone networks worldwide. Second-generation systems have been tremendously successful in this regard, with the number of cellular users expanding at a remarkable rate throughout the world. As noted in Chapter 10, data traffic on these cellular networks has been slower to develop. Higher-capacity third-generation systems discussed in that chapter have been designed specifically to handle packet-switched data. Wireless local-area networks, WLANs, have been developed as well to handle data traffic and have been proliferating worldwide. It is these networks and their even-smaller relatives, personal- area networks, that we discuss in this last chapter.

More specifically, we discuss in Section 12.1 the widely popular IEEE 802.11 and 802.11b wireless LAN standards, running at transmission rates of up to 11 megabits per second (11 Mbps) over the 2.4 GHz unlicensed frequency band. We treat briefly as well the newer very-high bit rate WLAN standards IEEE 802.11g and 802.11a running at rates up to 54 Mbps over the 2.4 GHz and 5 GHz unlicensed bands, respectively. The 802.11g standard is specifically designed to support extension of the 802.11b LAN standard to much higher bit rates over the same band. In Section 12.2 we then discuss wireless personal-area networks, focusing on the Bluetooth system, standardized as IEEE 802.15.1. The IEEE 802.11b standard has also been dubbed “Wi-FI” and is frequently referred to as the wireless Ethernet.

We have noted a number of times that the mobile radio environment is a hostile one. This was particularly spelled out in Chapter 2, in our discussion of fading phenomena in that environment. We shall see, in our discussion of voice-oriented second-generation (2G) cellular systems in the next chapter, Chapter 8, that extensive use is made of coding techniques to mitigate the effect of transmission vagaries such as fading on the information transmitted. By “coding” is meant the purposeful introduction of additional bits in a digital message stream to allow correction and/or detection of bits in the message stream that may have been received in error. In Chapter 10, describing packet-switched data transmission in third-generation (3G) cellular systems, as well as Chapter 12 on wireless local-area networks, we shall see that coding techniques are commonly used as well. Three such techniques have been adopted for wireless systems: block coding for error detection, with error-detection bits added to each message to be transmitted; convolutional coding for error correction; and, in high-speed 3G systems, the use of turbo coding in place of the convolutional coding procedure. (Turbo coding is a relatively recent, and particularly effective, coding technique.) Although block codes are used extensively in telecommunications systems to correct errors, as we shall see in Section 7.1 following, wireless systems in particular use block codes most frequently to detect errors not corrected by such error-correction procedures as convolutional and turbo coding.

We have, in earlier chapters in this book, explicitly discussed the concept of the capacity of a cellular system. In particular, in Chapter 2 we used the well-known Erlang-B formula to relate the blocking probability of a call attempt to traffic intensity (the call arrival rate times the call holding time) and the capacity, in number of channels, each capable of handling one call. In Chapter 3, we showed how the introduction of the cellular concept increased the overall system capacity, in terms of the number of simultaneous calls that could be handled. We further showed in Chapter 4 that Dynamic Channel Assignment, DCA, could, in principle, be used to increase system capacity for the case of moderate traffic intensity. In Chapter 6 we then compared the capacity of three second-generation digital cellular systems, IS-136, IS-95, and GSM. In that chapter we noted as well that mobile calls had to be handed off to adjacent cells as mobiles moved through a specified system. In particular, we noted that the CDMA-based IS-95 offered an improved performance because of the possibility of using soft handoff. We followed up on this introduction to handoffs by commenting on the actual process of handoff in Chapter 8.

In this chapter we quantify our discussion of the handoff process by describing cellular admission control: new call attempts are accepted in a given cell only if capacity is available to handle the call. Otherwise the call attempt is blocked.

We used the term “channel” rather abstractly in our discussion of FCA and DCA in Chapters 3 and 4. In this chapter we make the concept more concrete and provide examples of different types of “channels” used in current cellular systems. The word channel refers to a system resource allocated to a given mobile user enabling that user to communicate with the network with tolerable interference from other users. Channels are thus implicitly orthogonal to one another. The most common types of channels adopted for cellular systems are frequency channels, time slots within frequency bands, and distinct codes. These three different ways of providing access by multiple users to a cellular system are termed, respectively, frequency-division multiple access or FDMA; time-division multiple access (TDMA); and code-division multiple access (CDMA). We describe these different multiple access techniques in this chapter, using the three most widely deployed second-generation digital cellular systems as examples. All three of these systems utilize FDMA as well. Two of the systems, GSM and D-AMPS or IS-136, are TDMA-based systems; the third system, IS-95, uses CDMA. Since FDMA underlies all of the cellular systems to be discussed in this book, including the third-generation systems discussed in Chapter 10, we describe the FDMA concept briefly first. We then devote separate sections to TDMA and CDMA systems, ending the chapter with a comparison of their “channel capacities,” or the number of users each multiple-access scheme can accommodate per cell in a specified frequency band.

Introduction

Previous chapters of this book have focused on second-generation (2G) wireless systems designed principally for wireless telephony, i.e., to carry voice calls, interfacing with wired telephone networks. We discuss in this chapter worldwide efforts to develop and deploy more advanced cellular networks, designed to provide higher bit rate wireless data services to interface with the Internet and other data networks. The objective is to provide wireless networks capable of carrying multimedia traffic such as voice, video, images, and data files interfacing with wired networks to present the user with seamless communication, where possible, end-to-end. These cellular networks extend the 2G systems into what is generally characterized as the third-generation (3G) or, for some cases, 2.5G systems. Much higher bit rate wireless local-area networks (W-LANs) have been designed as well to provide some of the same services, and are already beginning to pervade the business and academic sectors. These are discussed in Chapter 12. The new generation of cellular networks treats voice communication essentially the way the second generation does – as circuit-switched telephone traffic. Data, however, are to be carried in packet-switched format. The data bit rates used are higher than those currently available in the 2G systems. The wireless LANs (WLANs) discussed in Chapter 12 use packet switching exclusively. Studies are going on concurrently on fourth-generation cellular systems as well. Those systems would be expected to be all-packet-switched, interfacing seamlessly with packet-switched wired networks such as the Internet.

In Chapter 1, in which we provided an overview of the topics to be discussed in this book, we noted that radio propagation conditions play a critical role in the operation of mobile wireless systems. They determine the performance of these systems, whether used to transmit real-time voice messages, data, or other types of communication traffic. It thus behooves us to describe the impact of the wireless medium in some detail, before moving on to other aspects of the wireless communication process. This we do in this chapter. Recall also, from Chapter 1, that the radio or wireless path normally described in wireless systems corresponds to the radio link between a mobile user station and the base station with which it communicates. It is the base station that is, in turn, connected to the wired network over which communication signals will travel. Modern wireless systems are usually divided into geographically distinct areas called cells, each controlled by a base station. We shall have more to say about cells and cellular structures in later chapters. (An exception is made in Chapter 12, the last chapter of this book, in which we discuss small-sized wireless networks for which the concepts of base stations and cells generally play no role.) The focus here is on one cell and the propagation conditions encountered by signals traversing the wireless link between base station and mobile terminal.

In Chapter 3 we discussed channel allocation in a cellular environment with reuse constraints built in to keep interference to a tolerable level. The reuse constraints reduce the number of channels that can be allocated to each cell, reducing thereby the improvement in system traffic capacity expected through the use of the cell concept. Various strategies have been proposed and/or adopted to obtain further improvement in system performance. The use of directional antennas to reduce the number of interfering signals with which a desired signal has to contend is one possibility (Stüber, 2001). This procedure has, in fact, been adopted in current cellular systems. Another approach is that of reducing cell size, hence gaining more cells in a geographic region, with consequent increase in capacity, with a given reuse characteristic included. Cellular systems are, in fact, moving toward a hierarchy of cell sizes: larger cells, called macrocells; microcells, to be used, ideally, in the more crowded urban environments where the traffic load does dictate reducing the cell size (base station antennas and their attendant transmitter–receiver systems may be located at the reduced height of street lamp posts); and picocells, to be used principally in indoor cellular systems. One problem with the microcell approach is that handoffs, to be discussed in Chapter 9, become more frequent, increasing the need to handle handoff calls more effectively. Macrocell overlays on microcellular systems have been proposed as well to improve traffic-handling capability (Stüber, 2001).

This appendix is intended to help the reader by summarizing some known formulas and results from matrix algebra and probability theory. It is absolutely not a selfcontained treatment of linear algebra or probability theory, nor is it a replacement for a systematic treatment or a course on these topics. However, it may be helpful as a refresher for students who have not studied these subjects for some time.

All the results presented here are well-known and their proofs can be found in many textbooks on linear algebra, probability theory and statistics. Since our discussion is somewhat scattered, and since we believe that the proof of some of the results can constitute appropriate problems for students when this book is used in a classroom setting, we present most of the results in the form of exercises.

Complex Baseband Representation of Bandpass Signals

Most signals in a wireless communication system have a narrowband character, and such narrowband (real-valued) signals can be represented with complex variables in a neat way. The general theory for doing so is treated in most digital communication textbooks (see, e.g., [Proakis, 2001, Sec. 4.1]), but since the complex baseband representation is a foundation for all signal models used in this book we offer a brief treatment of the topic.

The book in hand is the result of merging an original plan of writing a monograph on orthogonal space-time block coding with the ambition of authoring a more textbook-like treatment of modern MIMO communication theory, and it may therefore have the attributes of both these categories of literature. A major part of the book has resulted from the development of graduate courses at the universities where the authors are active, whereas a smaller part is the outcome of the authors' own research.

Our text covers many aspects of MIMO communication theory, although not all topics are treated in equal depth. The discussion starts with a review of MIMO channel modeling along with error probability and information theoretical properties of such channels, from which we go on to analyze the concepts of receive and transmit diversity. We discuss linear space-time block coding for flat and frequency selective fading channels, along with its associated receiver structures, both in general and in particular with emphasis on orthogonal space-time block coding. We also treat several special topics, including space-time coding for informed transmitters and space-time coding in a multiuser environment. Furthermore we include material about the existence and design of amicable orthogonal designs, a topic which is relevant in the context of orthogonal space-time block coding.

The text focuses on principles rather than on specific practical applications, although we have tried to illustrate some results in the context of contemporary wireless standards.

The demand for capacity in cellular and wireless local area networks has grown in a literally explosive manner during the last decade. In particular, the need for wireless Internet access and multimedia applications require an increase in information throughput with orders of magnitude compared to the data rates made available by today's technology. One major technological breakthrough that will make this increase in data rate possible is the use of multiple antennas at the transmitters and receivers in the system. A system with multiple transmit and receive antennas is often called a multiple-input multiple-output (MIMO) system. The feasibility of implementing MIMO systems and the associated signal processing algorithms is enabled by the corresponding increase of computational power of integrated circuits, which is generally believed to grow with time in an exponential fashion.

Why Space-Time Diversity?

Depending on the surrounding environment, a transmitted radio signal usually propagates through several different paths before it reaches the receiver antenna. This phenomenon is often referred to as multipath propagation. The radio signal received by the receiver antenna consists of the superposition of the various multipaths. If there is no line-of-sight between the transmitter and the receiver, the attenuation coefficients corresponding to different paths are often assumed to be independent and identically distributed, in which case the central limit theorem [papoulis, 2002, ch. 7] applies and the resulting path gain can be modelled as a complex Gaussian random variable (which has a uniformly distributed phase and a Rayleigh distributed magnitude).

This chapter will take the first step towards exposing the reader to the concept of transmit diversity. We begin by studying a system where the channel is known to the transmitter. Next we assume that the channel is unknown at the transmitter site and show how transmit diversity can be achieved via repeated transmission of a single symbol, and how such transmit diversity is related to the receive diversity discussed in the previous chapter. Finally, we provide a more systematic discussion of space-time coding and also set the framework for the rest of this book. In most parts of this chapter, we assume a frequency flat channel (frequency selective channels will be discussed in Chapter 8).

Optimal Beamforming with Channel Known at Transmitter

As a preparation we shall begin by studying a system with nr ≥ 1 receive and nt > 1 transmit antennas, where both the transmitter and receiver know the propagation channel. This knowledge about the channel can be used to adapt the weights for each transmit antenna in such a way that the SNR at the receiver is maximized. Doing so is sometimes called “beamforming,” although it (like the beamforming in Section 5.1) may not have the physical interpretation of forming a beam. All the analysis presented in this section is straightforward; some of it can also be found in [ganesan and stoica, 2001b].