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1.4 Outline of the Book

In Section 1.3 we described the basic principles of signal reception for wireless systems. The purpose of this book is to delve into advanced methods for this problem in the contexts of the signaling environments that are of most interest in emerging wireless applications. The scope of the treatment includes advanced receiver techniques for key signaling environments, including multiple-access, MIMO, and OFDM systems, as well as methods that address unique physical issues arising in many wireless channels, including fading, impulsive noise, co-channel interference, and other channel impairments. This material is organized into nine chapters beyond the current chapter. The first five of these deal explicitly with multiuser detection (i.e., with the mitigation of multiple-access interference) combined with other channel features or impairments. The remaining four chapters deal with the treatment of systems involving narrowband co-channel interference, time-selective fading, or multiple carriers, and with a general technique for receiver signal processing based on Monte Carlo Bayesian techniques. These contributions are outlined briefly in the paragraphs below.

Chapter 2 is concerned with the basic problem of adaptive multiuser detection in channels whose principal impairments (aside from multiple-access interference) are additive white Gaussian noise and multipath distortion. Adaptivity is a critical issue in wireless systems because of the dynamic nature of wireless channels. Such dynamism arises from several sources, notably from mobility of the transmitter or receiver and from the fact that the user population of the channel changes due to the entrance and exit of users and interferers from the channels and due to the bursty nature of many information sources. This chapter deals primarily with blind multiuser detection, in which the receiver is faced with the problem of demodulating a particular user in a multiple-access system, using knowledge only of the signaling waveform (either the composite receiver waveform or the transmitted waveform) of that user. The “blind” qualifier means that the receiver algorithms to be described are to be adapted without knowledge of the transmitted symbol stream. In this chapter we introduce the basic methods for blind adaptation of the linear multiuser detectors discussed in Section 1.3 via traditional adaptation methods, including least-mean-squares (LMS), recursive least-squares (RLS), and subspace tracking. The combination of multiuser detection with estimation of the channel intervening the desired transmitter and receiver is also treated in this context, as is the issue of correlated noise.

The methods of Chapter 2 are of particular interest in downlink situations (e.g., base to mobile), in which the receiver is interested in the demodulation of only a single user in the system. Another scenario is that the receiver has knowledge of the signaling waveforms used by a group of transmitters and wishes to demodulate this entire group while suppressing the effects of other interfering transmitters. An example of a situation in which this type of problem occurs is the reverse, or mobile-to-base, link in a CDMA cellular telephony system, in which a given base station wishes to demodulate the users in its cell while suppressing interference from users in adjacent cells. Chapter 3 continues with the issue of blind multiuser detection, but in this more general context of group detection. Here, both linear and nonlinear methods are considered, and again the issues of multipath and correlated noise are examined.

Channels in which the ambient noise is assumed to be Gaussian are considered in Chapters 2 and 3. Of course, this assumption of Gaussian noise is a very common one in the design and analysis of communication systems, and there are often good reasons for this assumption, including tractability and a degree of physical reality stemming from phenomena such as thermal noise. However, many practical channels involve noise that is decidedly not Gaussian. This is particularly true in urban and indoor environments, in which there is considerable impulsive noise due to human-made ambient phenomena. Also, in underwater acoustic channels (which are not specifically addressed in this book but which are used for tetherless communications) the ambient noise tends to be non-Gaussian. In systems limited by multiple-access interference, the assumption of Gaussian noise is a reasonable one, since it allows the focus to be placed on the main source of error—multiple-access interference. However, as we shall see in Chapters 2 and 3, the use of multiuser detection can return such channels to channels limited by ambient noise. Thus, the structure of ambient noise is again important, particularly since the performance and design of receiver algorithms can be affected considerably by the shape of the noise distribution even when the noise energy is held constant. In Chapter 4 we consider the problem of adaptive multiuser detection in channels with non-Gaussian ambient noise. This problem is a particularly challenging one because traditional methods for mitigating non-Gaussian noise involve nonlinear front-end processing, whereas methods for mitigating MAI tend to rely on the linear separating properties of the signaling multiplex. Thus, the challenge for non-Gaussian multiple-access channels is to combine these two methodologies without destroying the advantages of either. A powerful approach to this problem based on nonlinear regression is described in Chapter 4. In addition to the design and analysis of basic algorithms for known signaling environments, blind and group-blind methods are also discussed. It is seen that these methods lead to methods for multiuser detection in non-Gaussian environments that perform much better than linear methods in terms of both absolute performance and robustness.

In Chapter 5 we introduce the issue of multiple antennas into the receiver design problem. In particular, we consider the design of optimal and adaptive multiuser detectors for MIMO systems. Here, for known channel and antenna characteristics, the basic sufficient statistic [analogous to (1.53)] is a space-time matched-filter bank, which forms a generic front end for a variety of space-time multiuser detection methods. For adaptive systems, a significant issue that arises beyond those in the single-antenna situation is lack of knowledge of the response of the receiving antenna array. This can be handled through a novel adaptive MMSE multiuser detector described in this chapter. Again, as in the scalar case, the issues of multipath and blind channel identification are considered as well.

In Chapter 6 we treat the problem of signal reception in channel-coded multiple-access systems. In particular, the problem of joint channel decoding and multiuser detection is considered. A turbo-style iterative technique is presented that mitigates the high complexity of optimal processing in this situation. The essential idea of this turbo multiuser detector is to consider the combination of channel coding followed by a multiple-access channel as a concatenated code, which can be decoded by iterating between the constituent decoders—the multiuser detector for the multiple-access channel and a conventional channel decoder for the channel codes—exchanging soft information between each iteration. The constituent algorithms must be soft-input/soft-output (SISO) algorithms, which implies MAP multiuser detection and decoding. In the case of convolutional channel codes, the MAP decoder can be implemented using the well-known Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm. However, the MAP multiuser detector is quite complex, and thus a SISO MMSE detector is developed to lessen this complexity. A number of issues are treated in this context, including a group-blind implementation to suppress interferers, multipath, and space-time coded systems.

In Chapter 7 we turn to the issue of narrowband interference suppression in spread-spectrum systems. This problem arises for many reasons. For example, in multimedia transmission, signals with different data rates make use of the same radio resources, giving rise to signals of different bandwidths in the same spectrum. Also, some emerging services are being placed in parts of the radio spectrum which are already occupied by existing narrowband legacy systems. Many other systems operate in license-free parts of the spectrum, where signals of all types can share the same spectrum. Similarly, in tactical military systems, jamming gives rise to narrowband interference. The use of spread-spectrum modulation in these types of situations creates a degree of natural immunity to narrowband interference. However, active methods for interference suppression can yield significant performance improvements over systems that rely simply on this natural immunity. This problem is an old one, dating to the 1970s. Here we review the development of this field, which has progressed from methods that exploit only the bandwidth discrepancies between spread and narrowband signals, to more powerful “code-aided” techniques that make use of ideas similar to those used in multiuser detection. We consider several types of narrowband interference, including tonal signals and narrowband digital communication signals, and in all cases it is seen that active methods can offer significant performance gains with relatively small increases in complexity.

Chapter 8 is concerned with the problem of Monte Carlo Bayesian signal processing and its applications in developing adaptive receiver algorithms for tasks such as multiuser detection, equalization, and related tasks. Monte Carlo Bayesian methods have emerged in statistics over the past few years. When adapted to signal processing tasks, they give rise to powerful low-complexity adaptive algorithms whose performance approaches theoretical optima for fast and reliable communications in the dynamic environments in which wireless systems must operate. The chapter begins with a review of the large body of methodology in this area that has been developed over the past decade. It then continues to develop these ideas as signal processing tools, both for batch processing using Markov chain Monte Carlo (MCMC) methods and for online processing using sequential Monte Carlo (SMC) methods. These methods are particularly well suited to problems involving unknown channel conditions, and the power of these techniques is illustrated in the contexts of blind multiuser detection in unknown channels and blind equalization of MIMO channels.

Although most of the methodology discussed in the preceding paragraphs can deal with fading channels, the focus of those methods has been on quasi-static channels in which the fading characteristics of the channel can be assumed to be constant over an entire processing window, such as a data frame. This allows representation of the fading with a set of parameters that can be well estimated by the receiver. An alternative situation arises when the channel fading is fast enough that it can change at a rate comparable to the signaling rate. For such channels, new techniques must be developed in order to mitigate the fast fading, either by tracking it simultaneously with data demodulation or by using modulation techniques that are impervious to fast fading. Chapter 9 is concerned with problems of this type. In particular, after an overview of the physical and mathematical modeling of fading processes, several basic methods for dealing with fast-fading channels are considered. In particular, these methods include application of the expectation-maximization (EM) algorithm and its sequential counterpart, decision-feedback differential detectors for scalar and space-time-coded systems, and sequential Monte Carlo methods for both coded and uncoded systems.

Finally, in Chapter 10, we turn to problems of advanced receiver signal processing for coded OFDM systems. As noted previously, OFDM is becoming the technique of choice for many high-data-rate wireless applications. Recall that OFDM systems are multicarrier systems in which the carriers are spaced as closely as possible while maintaining orthogonality, thereby efficiently using available spectrum. This technique is very useful in frequency-selective channels, since it allows a single high-rate data stream to be converted into a group of many low-rate data streams, each of which can be transmitted without intersymbol interference. The chapter begins with a review of OFDM systems and then considers receiver design for OFDM signaling through unknown frequency-selective channels. In particular, the treatment focuses on turbo receivers in several types of OFDM systems, including systems with frequency offset, a space-time block coded OFDM system, and a space-time coded OFDM system using low-density parity-check (LDPC) codes.

Taken together, the techniques described in these chapters provide a unified methodology for the design of advanced receiver algorithms to deal with the impairments and diversity opportunities associated with wireless channels. Although most of these algorithms represent very recent research contributions, they have generally been developed with an eye toward low complexity and ease of implementation. Thus, it is anticipated that they can be applied readily in the development of practical systems. Moreover, the methodology described herein is sufficiently general that it can be adapted as needed to other problems of receiver signal processing. This is particularly true of the Monte Carlo Bayesian methods described in Chapter 8, which provide a very general toolbox for designing low-complexity yet sophisticated adaptive signal processing algorithms.

Note to the Reader Each chapter of this book describes a number of advanced receiver algorithms. For convenience, the introduction to each chapter contains a list of the algorithms developed in that chapter. Also, the references cited for all chapters are listed near the end of the book. This set of references comprises an extensive, although not exhaustive, bibliography of the literature in this field.

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