ARQ is a flexible and efficient technique for data transmissions. In hybrid ARQ, sub-packet schemes are more attractive for systems with burst errors than complete packet schemes. Although sub-packet schemes were proposed in ARQ systems, optimum sub-packet transmission is more effective to maximize throughput in a dynamic channel. Since convolutional codes are burst errors in decoding, the optimum sub-packet can be applied to convolutional codes. This chapter investigates the performance of sub-packet transmission for convolutionally coded systems. An efficient method is proposed to estimate the optimum number of sub-packets, and adaptive sub-packet schemes, i.e. schemes that enable a system to employ different optimum numbers of sub-packets under various conditions, are suggested to achieve maximum throughput in the system. Numerical and simulation results show that the adaptive sub-packet scheme is very effective for the convolutionally coded hybrid ARQ system, and it can provide higher throughput, smaller delay and lower dropping rate than complete packet schemes. Moreover, the adaptive sub-packet scheme can be flexibly used with packet combining techniques to further improve system throughput.

Introduction

In high-speed data communication systems, information sequences are transmitted usually in packets with fixed length. At a receiver, error correction and detection are carried out on the whole packet. If the packet is found in error, the receiver sends a request to the transmitter via a feedback channel, and then the whole packet is retransmitted. However, such a conventional complete-packet ARQ scheme is inefficient in the presence of burst errors.

In many of the systems and models in which stochastic resonance has been observed, the essential nonlinearity is effectively a single threshold. Usually SR occurs when an entirely subthreshold signal is subjected to additive noise, which allows threshold crossings to occur that otherwise would not have. In such systems, it is generally thought that when the input signal is suprathreshold, then the addition of noise will not have any beneficial effect on the system output.

However, the 1999 discovery of a novel form of SR in simple threshold-based systems showed that this is not the case. This phenomenon is known as suprathreshold stochastic resonance, and occurs in arrays of identical threshold devices subject to independent additive noise. In such arrays, SR can occur regardless of whether the signal is entirely subthreshold or not, hence the name suprathreshold SR. The SSR effect is quite general, and is not restricted to any particular type of signal or noise distribution.

This chapter reviews the early theoretical work on SSR. Recent theoretical extensions are also presented, as well as numerical analysis of previously unstudied input and noise signals, a new technique for calculating the mutual information by integration, and an investigation of a number of channel capacity questions for SSR. Finally, this chapter shows how SSR can be interpreted as a stochastic quantization scheme.

Introduction

Suprathreshold stochastic resonance (SSR) is a form of stochastic resonance (SR) that occurs in arrays of identical threshold devices. A schematic model of the system is shown in Fig. 4.1, and is described in detail in Section 4.3.

Ultra-wideband impulse radio is a promising radio technology for networks delivering extremely high data rates at short ranges. The use of extremely short duration pulses, however, makes the synchronization task more difficult. In this chapter a two-stage acquisition with serial search noncoherent correlator for time-hopping impulse radio is proposed. The proposed two-stage acquisition scheme gets chip timing synchronization, and aligns the phase of the local time-hopping code in two successive stages. With the aid of the flow-graph approach, analytical expressions are presented for the mean acquisition time and the probability of acquisition. Numerical results in a slow fading channel show that the proposed two-stage acquisition method can offer a much shorter mean acquisition time or much higher probability of acquisition than that delivered by conventional acquisition.

Introduction

As explained in Section 1.1, one of the design challenges provided by the wide bandwidth property of IR-UWB signals is timing acquisition, so a rapid acquisition algorithm is very important in IR-UWB communications. A few papers have focused on acquisition for TH IR-UWB signals. In [1] the authors analyze the acquisition performance of the IR-UWB signal. In [2] a generalized analysis of various serial search strategies is presented for reducing the mean acquisition time for IR-UWB signals in a dense multipath environment, and finally in [3] hybrid schemes for IR-UWB signal acquisition are proposed to trade off the speed of parallel schemes with the simplicity of serial search schemes.

The aim of this chapter is to find asymptotic large N approximations to the mean square error distortion for the suprathreshold stochastic resonance model. In particular, we are interested in how the distortion varies with noise intensity and how it scales with the number of threshold devices. That is, does the distortion become asymptotically small for large N?

Introduction

Chapter 6 developed the idea of treating the SSR model as a lossy source coding or quantization model. We saw that such a treatment requires specification of reproduction points corresponding to each of the N + 1 discrete output states. Once specified, an approximate reconstruction of the input signal can be made from a decoding, and the average error between this approximation and the original signal subsequently measured by the mean square error (MSE) distortion. We saw also in Chapter 5 that asymptotic approximations to the output probability mass function, Py(n), output entropy, average conditional output entropy, and mutual information can be found if the number of threshold devices, N, in the SSR model is allowed to become very large. The aim of this chapter is to again allow N to become very large, and develop asymptotic approximations to the MSE distortion for the cases of optimal linear and optimal nonlinear decodings of the SSR model.

Chapter structure

This chapter has three main sections. We begin in Section 7.2 by letting N become large in the formulas derived in Chapter 6 and analyzing the result. Next, Section 7.3 takes the same approach from the estimation theory perspective.

In this chapter, TD performance is studied by assuming that the ideal channel state information is available at the receiver side. Under a frequency-selective Rayleigh fading environment, the performance of EGC and GSC 2D-Rake receivers is analyzed for either mutually independent or mutually correlated pairs of channels. The spatial diversity gain provided by TD-STBC is compared with that of a system deploying only one transmit antenna.

Introduction

It is assumed that the ideal channel state information (CSI) is available at the receiver, in other words, the channel estimation is perfect. The performance of the TD-STBC scheme is investigated in terms of the BER (bit error rate). In the next chapter, a common pilot signal transmission is employed to assist receivers in estimating the channel fading coefficients; hence, the impact of imperfect channel estimation on system performance can be investigated through comparing the results obtained in Chapters 5 and 6. The effect of correlation between the pair of channels from two transmit antennas to receive antenna is studied. In order to emphasize the spatial diversity gain from using TD-STBC, the performance of the corresponding Rake receiver of the conventional CDMA system with only one transmit antenna is evaluated and compared.

The rest of this chapter is organized as follows. In Section 5.2, the transmitter, channel and receiver models are described. The performance of coherent reception for downlink of the CDMA system with and without TD-STBC is analyzed in Section 5.3, and the closed-form expressions of BER are obtained.

In this chapter, a common pilot signal transmission scheme is utilized to assist receivers to estimate the channel fading coefficients; hence, the effect of imperfect channel estimation on the TD-STBC system performance is investigated for the independent pair of channels. The power ratio of pilot to data channels and the lowpass filter used to improve the channel estimation are addressed.

Introduction

In this chapter, the TD-STBC in the DS-CDMA system with an imperfect channel estimation scheme based on the 3GPP standard [1] is studied. In the downlink of the WCDMA system, two common pilot channel signals are transmitted simultaneously from two antennas, which are employed to assist mobile stations to estimate the channel fading coefficients. In this chapter, however, it is merely assumed that the pair of channels corresponding to two transmit antennas are independent from each other. The impact of imperfect channel estimation on the system performance can be investigated through comparing the results obtained in Chapters 5 and 6. In terms of the resultant BER and system capacity, the effect of some important parameters on the system performance is also evaluated.

The rest of this chapter is organized as follows. In Section 6.2, the transmitter, channel and receiver models are described. The performance of coherent reception in the downlink of the CDMA system with and without TD-STBC are analyzed in Section 6.3. In Section 6.4, the numerical results of the system with various parameters and consequent discussions are presented. Finally, Section 6.5 summarizes and draws some conclusions.

The performances of optimum and per sub-carrier MMSE (or pcMMSE) detectors for OFCDM systems are compared in this chapter. In OFCDM systems, the existence of MCI in the frequency domain due to a frequency selective channel on different sub-carriers dramatically degrades the system performance. To suppress MCI, an optimum or MMSE detector is employed in this chapter. A quasi-analytical BER expression is derived in the presence of imperfect channel estimation. Numerical results show that with a linear computation complexity, the MMSE detector can improve the system performance by suppressing MCI, although it cannot perform as well as the optimum detector. Thus, in systems with a small number of code channels, the optimum detector can be employed to achieve better performance, whereas the MMSE detector is more suitable for systems with a large number of code channels. The MMSE detector is also more robust to different configurations of system parameters than the optimum detector. Moreover, it is found that pilot channel power should be carefully determined by making trade-off between the channel estimation quality and received SNR for each data channel.

Introduction

OFCDM is proposed for future broadband wireless communications. Inherited from OFDM, in OFCDM systems a high-speed data stream is divided into several parallel low-speed substreams, whose bandwidth is far smaller than the channel bandwidth, so each substream can be regarded as passing through a frequency nonselective (flat) fading. As a result, the multipath interference in frequency selective fading channels is effectively avoided.

Background and motivation

We begin by briefly outlining the background and motivation for this book, before giving an overview of each chapter, and pointing out the most significant questions addressed.

Although the methodology used is firmly within the fields of signal processing and mathematical physics, the motivation is interdisciplinary in nature.

The initial open questions that inspired this direction were:

1. (i) How might neurons make use of a phenomenon known as stochastic resonance?

2. (ii) How might a path towards engineering applications inspired by these studies be initiated?

Stochastic resonance and sensory neural coding

Stochastic resonance (SR) is a counter-intuitive phenomenon where the presence of noise in a nonlinear system is essential for optimal system performance. It is not a technique. Instead, it is an effect that might be observed and potentially exploited or induced. It has been observed to occur in many systems, including in both neurons and electronic circuits.

A motivating idea is that since we know the brain is far better at many tasks compared to electronic and computing devices, then maybe we can learn something from the brain. If we can ultimately better understand the possible exploitation of SR in the brain and nervous system, we may also be able to improve aspects of electronic systems.

Although it is important to have an overall vision, in practical terms it is necessary to consider a concrete starting point. This book is particularly focused on an exciting new development in the field of SR, known as suprathreshold stochastic resonance (SSR) (Stocks 2000c). Suprathreshold stochastic resonance occurs in a parallel array of simple threshold devices.

Wireless communications services are penetrating into our society at an explosive growth rate, and demands for a variety of high-speed wireless multimedia services continue to increase. It is everyone's wish that wireless could act like a wired connection with the same quality as fixed networks. To realize true high-speed wireless systems, sustained technical innovation on many fronts will be required. The physical limitations on and problems with wireless channels (bandwidth and power constraints, multipath fading, noise and interference) present a fundamental technical challenge to reliable high-speed wireless communications. This book is an ideal reference for graduate students and practitioners in the wireless industry.

The text of this book has been developed through years of research by the author and his graduate students. The aim of this book is to provide an R&D perspective on the field of high-speed wireless multimedia communications by describing the recent research developments in this area and also by identifying key areas in which further research will be needed.

The book is organized into four parts: introduction, ultra-wideband (UWB) communications, evolved 3G mobile communications and 4G mobile communications, with twelve chapters.

The authors have produced a breakthrough treatise with their new book Stochastic Resonance. The work synthesizes and extends several threads of noise-benefit research that have appeared in recent years in the growing literature on stochastic resonance. It carefully explores how a wide variety of noise types can often improve several types of nonlinear signal processing and communication. Readers from diverse backgrounds will find the book accessible because the authors have patiently argued their case for nonlinear noise benefits using only basic tools from probability and matrix algebra.

Stochastic Resonance also offers a much-needed treatment of the topic from an engineering perspective. The historical roots of stochastic resonance lie in physics and neural modelling. The authors reflect this history in their extensive discussion of stochastic resonance in neural networks. But they have gone further and now present the exposition in terms of modern information theory and statistical signal processing. This common technical language should help promote a wide range of stochastic resonance applications across engineering and scientific disciplines. The result is an important scholarly work that substantially advances the state of the art.