For high-speed OFDM MIMO multiplexing, a new coded layered space-time-frequency (LSTF) architecture (i.e. LSTF-c) with iterative signal processing at the receiver is proposed, where multiple encoders/decoders are designed and each independent codeword is threaded in the three-dimensional (3-D) space-time-frequency (STF) transmission resource array. The iterative receiver structure is adopted consisting of a joint MMSE-SIC detector and the maximum a posteriori (MAP) convolutional decoders. Simulation results show that the proposed LSTF architecture can achieve almost the same performance as the LSTF (i.e. LSTF-a) where single coding is applied across the whole information stream. However, due to its structure of multiple parallel lower speed encoders/decoders with shorter codeword length, the proposed LSTF architecture can be more easily implemented than the LSTF-a.

Introduction

The challenge of the detection of MIMO multiplexing is to design a low complexity detector, which can efficiently suppress multi-antenna interference and approach the interference-free performance. In this chapter, an iterative processing technique for joint detection and decoding is used in the coded MIMO multiplexing. The iterative receiver contains an MMSE-SIC detector [1], the complexity of which is much lower than that of the MAP detector especially when the number of transmit antennas Nt and modulation level are large. As a constituent code, the convolutional code is used due to the computationally efficient SISO MAP decoding. The performances of the joint iterative detection/decoding schemes for both turbo code and convolutional code are quite similar for different numbers of antennas [2], [3].

Engineered systems usually require finding the right tradeoff between cost and performance. Communications systems are no exception, and much theoretical work has been undertaken to find the limits of achievable performance for the transmission of information. For example, Shannon's celebrated channel capacity formula and coding theorems say that there is an upper limit on the average amount of information that can be transmitted in a channel for error-free communication. This limit can be increased if the power of the signal is increased, or the bandwidth in the channel is increased. However, nothing comes for free, and increasing either power or bandwidth can be expensive; hence there is a tradeoff between cost and performance in such a communications system – performance (measured by bit rates) can be increased by increasing the cost (power or bandwidth). This chapter discusses several problems related to the tradeoff between cost and performance in the SSR model. We are interested in the SSR model as a channel model, from an energy efficient neural coding point of view, as well as the lossy source coding model, where there is a tradeoff between rate and distortion.

Introduction

Chapter 8 introduces an extension to the suprathreshold stochastic resonance (SSR) model by allowing all thresholds to vary independently, instead of all having the same value. This chapter further extends the SSR model by introducing an energy constraint into the optimal stochastic quantization problem. We also examine the tradeoff between rate and distortion, when the SSR model is considered as a stochastic quantizer.

Wireless communications and internet services have been penetrating into our society and affecting our everyday life profoundly during the last decade far beyond any earlier expectations. In addition, the demand for wireless communications is still growing rapidly and wireless systems that support voice communications have already been deployed with great success. Further wireless mobile and personal communication systems are expected to support a variety of high-speed multimedia services, such as high-speed internet access, high-quality video transmission and so on. To meet the demand for high data rate services in broadband wireless systems, various systems and/or technologies have been proposed, such as the ultra-wideband (UWB) system, and evolved third generation (3G) and fourth generation (4G) mobile communications systems.

UWB communications

In the foreseeable future, the development of low-power, short-range and high-speed transmission systems is going to play a significant role in the area of wireless communication, due to a blooming growth in demand for information sharing and data distribution tools to be used in hot-spot layer and personal network layer communications. At the same time, the radio frequency (RF) spectrum suitable for wireless links is limited, so efficient spectrum utilization is a challenging problem in physical-layer communication engineering [1]. All these have motivated the exploration of the UWB transmission system.

Recently, there has been a growing interest in the research and development of novel technologies aimed at allowing new services to use the radio spectrum already allocated to established services, but without causing noticeable interference to existing users.

As discussed in the last chapter, in a Gaussian or flat fading channel, multicode channels are orthogonal. In a realistic wireless channel, however, the orthogonality no longer maintains. Thus, MCI is caused. In this chapter, a novel detection method, called hybrid MCI cancellation and MMSE detection, is proposed and compared with pure MMSE detection. The system performance is analytically studied with imperfect channel estimation to show how it is affected by parameters such as the window size in the channel estimation, Doppler shift, the number of stages of the hybrid detection, the power ratio of pilot to data channels, spreading factor, and so on.

Introduction

Although MMSE detection can suppress MCI as discussed in the last chapter, so far MMSE detection has not been investigated for the OFCDM system with interference cancellation. Figure 10.1 illustrates the situation when MMSE detection is employed with MCI cancellation. It can be seen that the input to the MCI canceller is a combination of the useful signal, MCI and background noise. After MCI cancellation, the useful signal and background noise remain unchanged, but MCI is reduced. The combined signal is input to the MMSE detector with estimated signal plus interference power. Then the weighted output is generated after MMSE detection. Since the weight of MMSE is related to the input signal plus interference power, it should be updated stage by stage because the input power of MCI changes due to MCI cancellation.

In this chapter we illustrate the relevance of stochastic resonance to auditory neural coding. This relates to natural auditory coding and also to coding by cochlear implant devices. Cochlear implants restore partial hearing to profoundly deaf people by trying to mimic, using direct electrical stimulation of the auditory nerve, the effect of acoustic stimulation.

Introduction

It is not surprising that the study of auditory neural coding involves stochastic phenomena, due to one simple fact – signal transduction in the ear is naturally a very noisy process. The noise arises from a number of sources but principally it is the Brownian motion of the stereocilia (hairs) of the inner hair cells that has the largest effect (Hudspeth 1989). Although the Brownian motion of the stereocilia appears small – typically causing displacements at the tips of the stereocilia of 2–3 nm (Denk and Webb 1992) – this is not small compared with the deflection of the tips at the threshold of hearing. Evidence exists that suggests, at threshold, the tip displacement is of the order of 0.3 nm (Sellick et al. 1982, Crawford and Fettiplace 1986), thus yielding a signal-to-noise ratio (SNR) of about –20 dB at threshold. Of course, under normal operating conditions the SNR will be greater than this, but, at the neural level, is typically of the order of 0 dB (DeWeese and Bialek 1995).

Given the level of noise and the fact that neurons are highly nonlinear makes the auditory system a prime candidate for observing stochastic resonance (SR) type behaviour.

Quantization of a signal or data source refers to the division or classification of that source into a discrete number of categories or states. It occurs, for example, when analogue electronic signals are converted into digital signals, or when data are binned into histograms. By definition, quantization is a lossy process, which compresses data into a more compact representation, so that the number of states in a quantizer's output is usually far fewer than the number of possible input values.

Most existing theory on the performance and design of quantization schemes specifies only deterministic rules governing how data are quantized. By contrast, stochastic quantization is a term intended to pertain to quantization where the rules governing the assignment of input values to output states are stochastic rather than deterministic. One form of stochastic quantization that has already been widely studied is a signal processing technique called dithering. However, the stochastic aspect of dithering is usually restricted so that it is equivalent to adding random noise to a signal prior to quantization. The term stochastic quantization is intended to be far more general, and applies to the situation where the rules of the quantization process are stochastic.

The inspiration for this study comes from a phenomenon known as stochastic resonance, which is said to occur when the presence of noise in a system provides a better performance than the absence of noise. Specifically, this book discusses a particular form of stochastic resonance – discovered by Stocks – known as suprathreshold stochastic resonance, and demonstrates how and why this effect is a form of stochastic quantization.

The coherent MRC reception of PSAM MQAM systems with antenna diversity is studied in this chapter. A general fast time varying fading channel model is assumed. Pilot symbols are periodically inserted during the transmission of data symbols, which are used to track the time varying fading, and to provide channel estimation for data decisions at the receiver. Based on a digital implementation, a coherent demodulation scheme is presented. Channel estimation error due to fast fading and additive noise is studied. System performance is evaluated in terms of BER. The analysis shows that in perfect channel estimation cases, with the antenna diversity technique, the BER performance improves significantly, and higher-order QAM can be employed for higher throughput. It is also found that inaccurate channel estimation limits the benefit of diversity when the modulation order is large. By increasing the length of the channel estimator and the amplitude of the pilot symbol, more accurate channel estimation can be achieved, so that the BER performance is improved. Moreover, when the Doppler frequency is less than 1 / 2ST, where S is the number of symbols per time slot and T is the symbol duration, the performance is flat since the channel estimator is robust to fading rate.

Introduction

During the past several decades, MQAM has been considered for high rate data transmission over wireless links due to its high spectral efficiency [1–4].

The initial research into suprathreshold stochastic resonance described in Chapter 4 considers the viewpoint of information transmission. As discussed briefly in Chapter 4, the suprathreshold stochastic resonance effect can also be modelled as stochastic quantization, and therefore results in nondeterministic lossy compression of a signal. The reason for this is that the effect of independently adding noise to a common signal before thresholding the result a number of times, with the same static threshold value, is equivalent to quantizing a signal with random thresholds. This observation leads naturally to measuring and describing the performance of suprathreshold stochastic resonance with standard quantization theory. In a context where a signal is to be reconstructed from its quantized version, this requires a reproduction value or reproduction point to be assigned to each possible state of a quantized signal. The quantizing operation is often known as the encoding of a signal, and the assignment of reproduction values as the decoding of a signal. This chapter examines various methods for decoding the output of the suprathreshold stochastic resonance model, and evaluates the performance of each technique as the input noise intensity and array size change. As it is the performance criterion most often used in conventional quantization theory, the measure used is the mean square error distortion between the original input signal and the decoded output signal.

Introduction

We begin this chapter by very briefly reviewing the SSR model introduced in Chapter 4. We then introduce the concept of decoding the output of a quantizer's encoding to reconstruct the input signal, and consider measuring the performance of such a reconstruction.

Interference suppression is important to allow UWB devices to operate over the spectrum occupied by narrowband systems. In this chapter, the use of a notch filter in TH-IR for UWB communication is considered, where a Gaussian monopulse is employed with pulse position modulation (PPM). A lognormal fading channel is assumed and a complete analytical framework is provided for performance evaluation using a transversal-type notch filter to reject narrowband interference. A closed-form expression of BER is derived, and the numerical results show that the use of a notch filter can significantly improve system performance. Furthermore, a performance comparison between TH-IR and multicarrier CDMA UWB systems is also made for the same transmit power, the same data rate and the same bandwidth. It is shown that, in the presence of narrowband interference, the TH-IR system with a notch filter can achieve a performance similar to multicarrier CDMA.

Introduction

Over the last decade, there has been a great interest in ultra-wideband (UWB) time-hopping (TH) impulse radio (IR) communication systems [1–8]. These systems make use of ultra-short duration pulses (monocycles), which yield ultra-wide bandwidth signals characterized by extremely low power densities. UWB systems are particularly promising for short-range high-speed wireless communications as they potentially combine reduced complexity with low power consumption, low probability of intercept, high accuracy positioning and immunity to multipath fading due to discontinuous transmission.