Various signal processing techniques are actively used in communication systems to improve the performance. In particular, adaptive signal processing has a strong impact on communications. For example, various adaptive algorithms are applied to the channel equalization and interference rejection. Adaptive equalizers and interference cancellers can effectively mitigate interference and adapt to time-varying channel environments.

Even though iterative signal processing is not as advanced as adaptive signal processing, it plays a significant role in improving the performance of receivers, which may be limited by interfering signals. In addition, the estimation error of certain channel parameters, for example the channel impulse response, can degrade the performance. An improvement in interference cancelation or a better estimate of channel parameters may be available due to iterative signal processing. After each iteration, more information about interfering signals or channel parameters is available. Then, the interference cancelation is more precise and the channel parameters can be estimated more accurately. This results in an improvement in performance for each iteration.

It would be beneficial if we could study adaptive and iterative signal processing with respect to communications. There are a number of excellent books on adaptive signal processing and communication systems, though it is difficult to find a single book that covers both topics in detail. Furthermore, as iterative signal processing is less advanced, I have been unable to find a book that balances the subjects of signal processing and its applications in communication. My desire to locate such a book increased when I took a postgraduate course entitled “Adaptive Signal Processing in Telecommunications.”

In a multiuser communication system, a multiple access scheme is required to share a common channel resource, e.g. the frequency band. For mobile multiuser communication systems, especially cellular systems, a code division multiple access (CDMA) scheme is widely employed due to several advantages over other schemes. In CDMA systems, users can transmit signals simultaneously over the same frequency band. This makes CDMA systems interference-limited since interfering signals (i.e. other users' signals) degrade the performance. To improve the performance of CDMA systems, multiuser detection has been investigated. Multiuser detection attempts to detect the desired signal as well as other signals (this implies a joint detection) for a better performance. Indeed, this is an analogy of the MLSD, in which a symbol sequence (i.e. multiple symbols), rather than individual symbols, is detected. There are also a number of different approaches taken to mitigate or suppress interfering signals for multiuser detection. In this chapter, we introduce CDMA systems and multiuser detectors; adaptive multiuser detectors are also introduced.

Overview of cellular systems

Cellular systems are wireless communication systems that can provide services over a large area. As the range of radio signals is limited, a cellular structure is employed to cover a large area. A cellular structure is shown in Fig. 8.1, in which each hexagonal area is called a cell. There is a basestation at the center of each cell. The role of the basestation is to communicate with mobile subscribers (i.e. users) within a cell. Hence, the size of the cell is closely related to the range of the radio signals between the basestation and users.

In this chapter, we study the intersymbol interference channel. Intersymbol interference (ISI) is a self-noise introduced by a dispersive channel. Since the ISI channel can significantly degrade the performance of communication systems, we need to mitigate the ISI. One of the methods that can achieve this is channel equalization.

There are two different structures for channel equalization. Linear equalization is based on a linear filtering approach. Since an ISI channel can be seen as a linear filter, a linear equalizer at the receiver equalizes the frequency response of the ISI channel to mitigate the ISI. The other approach is called decision feedback equalization, in which linear filtering and cancelation are used to alleviate the ISI. In addition to these approaches, which are based on the structure of the equalizer, the performance criterion is important in the optimization of an equalizer. Two of the most popular criteria will be introduced in this chapter.

We also study adaptive equalizers with fundamental properties of adaptive algorithms. For a given ISI channel, an adaptive equalizer can adjust itself to achieve the optimum performance. Therefore, adaptive equalizers are important practically.

In this chapter, we assume that the reader is familiar with the fundamental concepts of signal processing, including convolution, linear filtering, and the sampling theorem. In addition, a working knowledge of probability and random processes is required.

ISI channels and the equalization problem

We will derive a model for the ISI channel in the discrete-time domain since most receiver operations are generally carried out with digital circuits or digital signal processors after sampling.

Orthogonal frequency division multiplexing (OFDM)was proposed in the 1960s (see Chang and Gibbey (1968)) and has been actively investigated since then. It can be used for both wired and wireless communications, providing several attractive features. One important feature of OFDM is that it is ISI-free. In OFDM, data symbols are transmitted by multiple orthogonal subcarriers. Each signal transmitted by a subcarrier has a narrow bandwidth and experiences flat fading without interfering with the other subcarriers' signals. From this, a simple one-tap equalizer can be used in the frequency domain to compensate for fading, while a complicated equalizer is required in a single-carrier system to overcome ISI.

It is generally known that OFDM will not outperform single-carrier systems (in terms of the average BER) when a single modulation scheme is used for all subcarriers. However, OFDM can offer a better performance if adaptive bit loading is employed. Since each subcarrier may experience different fading, the SNR varies among the subcarriers. A different number of bits per symbol can be transmitted using a different modulation scheme across subcarriers depending on the SNR for each subcarrier. For example, subcarriers with low SNR may transmit no signal or may use a lower-order modulation to stay below a certain BER ceiling, while more bits per symbol can be transmitted through subcarriers with high SNR. This approach of adaptive bit loading is used for wired communication systems (Bingham, 1990). Indeed, adaptive bit loading allows OFDM to outperform single-carrier systems. However, in some wireless communication systems, including digital terrestrial TV broadcasting, adaptive bit loading becomes impractical to implement in compensating for different fading across subcarriers.

This chapter's main objective is to discuss and to some extent dispel some common myths and misconceptions associated with interference mitigation solutions. Our goal is to shed some light on the lessons learned while researching and developing solutions.

A common path taken in the development of interference mitigation techniques often begins by identifying solutions developed for different purposes and applying to the problem at hand. In general, this path constitutes an extremely powerful approach, and examples given in Chapter 7, including time and spectral multiplexing, clearly demonstrate the effectiveness of the resulting solutions. However, applying solutions out of the original context for which they were developed is no simple task, since it requires a careful examination of all the parameters and the assumptions that come into play. It is often when this step is overlooked that myths are constructed.

Contrary to common belief, we show that some techniques often associated with interference mitigation do not constitute solutions. These techniques may in fact aggrevate the interference problem or have a negative impact on the overall system performance. They constitute what we call pitfalls that should be avoided if possible.

We find two recurring myths in most pitfalls studied, although this list is far from exhaustive.

1. Dealing with interference is similar to dealing with random noise and other wireless channel propagation properties and impairments.

2. A set of system parameters such as transmitted power, offered load, packet size, error correction scheme, and modulation techniques can be optimized in order to mitigate interference.

Our objectives in this chapter are to describe the basic building blocks in performance evaluation as we focus on identifying and understanding the effects of interference in wireless communications and its impact on system performance.

Since we set out to evaluate the effects of interference on performance, the first question we ask is what is interference? The term “interference” has been extensively used in the context of communication, in both wired and wireless systems. While an accurate definition may be dependent on the specifics of the context considered, the term generally refers to signal impairments due to factors in the environment such as channel propagation properties, other radiated power, and noise.

The second question is concerned with the performance evaluation of interference, namely, what are the quantitative measures that characterize interference, and consequently how should the resulting level of performance be quantified? One interference metric that has been used extensively includes the so-called signal to interference ratio. However, this measure does not characterize completely the resulting performance since performance is often tied to the quality of service requirements, which vary depending on the application considered. Our objective is to provide a list of performance metrics that can accurately quantify the network performance from an application perspective.

Since not all systems behave in the same way given the same level of interference, an important aspect of performance evaluation is to identify parameters that impact performance.