Jerry D. Gibson

The Mobile Communications Handbook

From the Publisher: The Mobile Communications Handbook covers the entire field - from principles of analog and digital communications to cordless telephones, wireless local area networks (LANs), and international technology standards. The amazing scope of the handbook ensures that it will be the primary reference for every aspect of mobile communications. Organized in two sections, The Mobile Communications Handbook first introduces the basic principles of analog and digital communications. Consisting of tutorial articles, this section provides background information and technical details, offering a solid foundation for the spectrum of mobile communications technology. The second part of the handbook consists of articles covering such topics as cellular mobile radio, personal communication systems, user location and addressing, wireless data, wireless LANs, and technology standards. New to the Second Edition: Discussions on: Rayleigh fading channelsSpace-time processingRadiolocation techniquesEnhancements in second generation systemsWireless video communicationsWireless ATMEvolving third generation standards Plus updates on: Wireless dataPower controlChannel assignmentDiversity techniquesError correction codingPseudonoise sequences

A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems

The use of multiple transmit/receive antennas forming a multiple-input multiple-output (MIMO) system can significantly enhance channel capacity. This paper considers a V-BLAST-type combination of orthogonal frequency-division multiplexing (OFDM) with MIMO (MIMO-OFDM) for enhanced spectral efficiency and multiuser downlink throughput. A new joint data detection and channel estimation algorithm for MIMO-OFDM is proposed which combines the QRD-M algorithm and Kalman filter. The individual channels between antenna elements are tracked using a Kalman filter, and the QRD-M algorithm uses a limited tree search to approximate the maximum-likelihood detector. A closed-form symbol-error rate, conditioned on a static channel realization, is presented for the M=1 case with QPSK modulation. An adaptive complexity QRD-M algorithm (AC-QRD-M) is also considered which assigns different values of M to each subcarrier according to its estimated received power. A rule for choosing M using subcarrier powers is obtained using a kernel density estimate combined with the Lloyd-Max algorithm.

Filtering of colored noise for speech enhancement and coding

Scalar and vector Kalman filters are implemented for filtering speech contaminated by additive white noise or colored noise, and an iterative signal and parameter estimator which can be used for both noise types is presented. Particular emphasis is placed on the removal of colored noise, such as helicopter noise, by using state-of-the-art colored-noise-assumption Kalman filters. The results indicate that the colored noise Kalman filters provide a significant gain in signal-to-noise ratio (SNR), a visible improvement in the sound spectrogram, and an audible improvement in output speech quality, none of which are available with white-noise-assumption Kalman and Wiener filters. When the filter is used as a prefilter for linear predictive coding, the coded output speech quality and intelligibility are enhanced in comparison to direct coding of the noisy speech. >

Lossy source coding

Lossy coding of speech, high-quality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called rate-distortion theory. For the first 25 years of its existence, rate-distortion theory had relatively little impact on the methods and systems actually used to compress real sources. Today, however, rate-distortion theoretic concepts are an important component of many lossy compression techniques and standards. We chronicle the development of rate-distortion theory and provide an overview of its influence on the practice of lossy source coding.

Adaptive prediction in speech differential encoding systems

The design of speech coders that produce high-quality highly intelligible speech at 6 to 16 kb/s while retaining robustness to background and transmission impairments is an area of current research interest. Differential encoding structures employing adaptive quantization and adaptive prediction constitute one of the most promising approaches to achieving these design objectives. This paper focuses on the design and analysis of adaptive predictors for differential encoders. Several differential encoding systems, including adaptive predictive coding, differential pulse-code modulation, noise feedback coding, direct feedback coding, and prediction error coding, are described and related. Adaptive quantizers are briefly discussed and quantitative and qualitative indicators of speech coder performance are defined. The channel model, the speech model, and the research problem statements used in the design of differential encoders and adaptive predictors are presented. The nomenclature and theory of forward and backward adaptive prediction are developed, and several new backward adaptive algorithms based on various assumptions are presented. A detailed survey of theoretical and simulation results on adaptive prediction for speech differential encoders is given, and the effects of background and transmission impairments on these systems are discussed, Finally, the impact of adaptive predictors on rate distortion theory motivated coders is indicated. Numerous areas for future research are highlighted.

Uniform and piecewise uniform lattice vector quantization for memoryless Gaussian and Laplacian sources

Lattice vector quantizer design procedures for nonuniform sources are presented. The procedures yield lattice vector quantizers with excellent performance and retaining the structure required for fast quantization. Analytical methods for truncating and scaling lattices to be used in vector quantizations are given, and their utility is demonstrated for independent and identically distributed (i.i.d.) Gaussian and Laplacian sources. An analytical technique for piecewise linear multidimensional compandor designs is evaluated for i.i.d. Gaussian and Laplacian sources by comparing its performance to that of the other vector quantizers. >

Channel estimation and data detection for MIMO-OFDM systems

The use of multiple antennas at both the transmitter and receiver can significantly increase the channel capacity. These systems are called the multiple-input multiple-output (MIMO) systems. By using orthogonal frequency division multiplexing (OFDM) transmission techniques, the MIMO-OFDM system can achieve high spectral efficiency, which makes it an attractive candidate for high-data-rate wireless applications. In this paper, we propose a convolutionally coded MIMO-OFDM system with EM-based channel estimation and a QRD-M data detection algorithm. In our systems, one training symbol is transmitted from each transmit antenna for the MIMO channel estimation at the receiver. With the channel estimates available, we apply the QRD-M algorithm on the estimated channel matrix for suboptimal data detection with reasonable computational cost. The bit error rate (BER) and packet error rate (PER) performance of the MIMO-OFDM systems are compared. In the simulations, the bit error rate performance of our systems is 9 (or 5) dB better than that of uncoded (or coded) BLAST systems.

A constrained joint source/channel coder design

The design of joint source/channel coders in situations where there is residual redundancy at the output of the source coder is examined. It has previously been shown that this residual redundancy can be used to provide error protection without a channel coder. In this paper, this approach is extended to conventional source coder/convolutional coder combinations. A family of nonbinary encoders is developed which more efficiently use the residual redundancy in the source coder output. It is shown through simulation results that the proposed systems outperform conventional source-channel coder pairs with gains of greater than 9 dB in the reconstruction SNR at high probability of error. >

Sequentially Adaptive Prediction and Coding of Speech Signals

A new method of speech digitization called residual encoding is introduced, and its application to the speech digitization problem is studied. The residual encoding system is a form of differential pulse code modulation which utilizes both an adaptive quantizer and an adaptive predictor. The residual encoder differs from previous systems in two ways. First, a sequential estimation method is used to continuously update the predictor coefficients, and second, the predictor coefficients are not transmitted, but are extracted from the estimate of the speech signal at both the transmitter and receiver. No form of pitch extraction is employed. The residual encoding system with a Kalman filter or a stochastic approximation algorithm for identifying the predictor coefficients has produced good quality speech at a data rate of 16 kbit/s.

Speech coding methods, standards, and applications

Voice is the preferred method of human communication. Although there have been times when it seemed that the voice communications problem was solved, such as when the PSTN was our primary network or later when digital cellular networks reached maturity, such is not the case today. This paper addresses the challenges and opportunities starting from the basic issues in speech coder design, developing the important speech coding techniques and standards, discussing current and future applications, outlining techniques for evaluating speech coder performance, and identifying research directions. The most prominent speech coding standards are presented and their properties, such as performance, complexity, and coding delay, analyzed. Particular networks and applications for each standard are included. Further, reflecting upon the issues and developments highlighted in this paper, it becomes evident that there is a diverse set of challenges and opportunities for research and innovation in speech coding and voice communications.