Iterative joint design of source codes and multiresolution channel codes

We propose an iterative design algorithm for jointly optimizing source and channel codes. The joint design combines channel-optimized vector quantization (COVQ) for the source code with rate-compatible punctured convolutional (RCPC) coding for the channel code. Our objective is to minimize the average end-to-end distortion. For a given channel SNR and transmission rate, our joint source and channel code design achieves an optimal allocation of bits between the source and channel coders. This optimal allocation can reduce distortion by up to 6 dB over suboptimal allocations for the source data set considered. We also compare the distortion of our joint iterative design with that of two suboptimal design techniques: COVQ optimized for a given channel bit-error-probability, and RCPC channel coding optimized for a given vector quantizer. We conclude by relaxing the fixed transmission rate constraint and jointly optimizing the transmission rate, source code, and channel code

MSAT-X: A technical introduction and status report

A technical introduction and status report for the Mobile Satellite Experiment (MSAT-X) program is presented. The concepts of a Mobile Satellite System (MSS) and its unique challenges are introduced. MSAT-X's role and objectives are delineated with focus on its achievements. An outline of MSS design philosophy is followed by a presentation and analysis of the MSAT-X results, which are cast in a broader context of an MSS. The current phase of MSAT-X has focused notably on the ground segment of MSS. The accomplishments in the four critical technology areas of vehicle antennas, modem and mobile terminal design, speech coding, and networking are presented. A concise evolutionary trace is incorporated in each area to elucidate the rationale leading to the current design choices. The findings in the area of propagation channel modeling are also summarized and their impact on system design discussed. To facilitate the assessment of the MSAT-X results, technology and subsystem recommendations are also included and integrated with a quantitative first-generation MSS design

The design of finite-state machines for quantization using simulated annealing

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 121-125In this thesis, the combinatorial optimization algorithm known as simulated annealing (SA) is applied to the solution of the next-state map design problem of data compression systems based on finite-state machine decoders. These data compression systems which include finite-state vector ciuantization (FSVQ), trellis waveform coding (TWC), predictive trellis waveform coding (PTWC), and trellis coded quantization (TCQ) are studied in depth. Incorporating generalized Lloyd algorithm for the optimization of output map to SA, a finite-state machine decoder design algorithm for the joint optimization of output map and next-state map is constructed. Simulation results on several discrete-time sources for FSVQ, TWC and PTWC show that decoders with higher performance are obtained by the SA-I-CLA algorithm, when compared to other related work in the literature. In TCQ, simulation results are obtained for sources with memory and new observations are made.Kuruoğlu, Ercan EnginM.S

Combined source-channel coding for a power and bandwidth constrained noisy channel

This thesis proposes a framework for combined source-channel coding under power and bandwidth constrained noisy channel. The framework is then applied to progressive image coding transmission using constant envelope M-ary Phase Shift Key (MPSK) signaling over an Additive White Gaussian Channel (AWGN) channel. First the framework for uncoded MPSK signaling is developed. Then, its extended to include coded modulation using Trellis Coded Modulation (TCM) for MPSK signaling. Simulation results show that coded MPSK signaling performs 3.1 to 5.2 dB better than uncoded MPSK signaling depending on the constellation size. Finally, an adaptive TCM system is presented for practical implementation of the proposed scheme, which outperforms uncoded MPSK system over all signal to noise ratio (Es/No) ranges for various MPSK modulation formats. In the second part of this thesis, the performance of the scheme is investigated from the channel capacity point of view. Using powerful channel codes like Turbo and Low Density Parity Check (LDPC) codes, the combined source-channel coding scheme is shown to be within 1 dB of the performance limit with MPSK channel signaling

Time and frequency domain algorithms for speech coding

The promise of digital hardware economies (due to recent advances in VLSI technology), has focussed much attention on more complex and sophisticated speech coding algorithms which offer improved quality at relatively low bit rates. This thesis describes the results (obtained from computer simulations) of research into various efficient (time and frequency domain) speech encoders operating at a transmission bit rate of 16 Kbps. In the time domain, Adaptive Differential Pulse Code Modulation (ADPCM) systems employing both forward and backward adaptive prediction were examined. A number of algorithms were proposed and evaluated, including several variants of the Stochastic Approximation Predictor (SAP). A Backward Block Adaptive (BBA) predictor was also developed and found to outperform the conventional stochastic methods, even though its complexity in terms of signal processing requirements is lower. A simplified Adaptive Predictive Coder (APC) employing a single tap pitch predictor considered next provided a slight improvement in performance over ADPCM, but with rather greater complexity. The ultimate test of any speech coding system is the perceptual performance of the received speech. Recent research has indicated that this may be enhanced by suitable control of the noise spectrum according to the theory of auditory masking. Various noise shaping ADPCM configurations were examined, and it was demonstrated that a proposed pre-/post-filtering arrangement which exploits advantageously the predictor-quantizer interaction, leads to the best subjective performance in both forward and backward prediction systems. Adaptive quantization is instrumental to the performance of ADPCM systems. Both the forward adaptive quantizer (AQF) and the backward oneword memory adaptation (AQJ) were examined. In addition, a novel method of decreasing quantization noise in ADPCM-AQJ coders, which involves the application of correction to the decoded speech samples, provided reduced output noise across the spectrum, with considerable high frequency noise suppression. More powerful (and inevitably more complex) frequency domain speech coders such as the Adaptive Transform Coder (ATC) and the Sub-band Coder (SBC) offer good quality speech at 16 Kbps. To reduce complexity and coding delay, whilst retaining the advantage of sub-band coding, a novel transform based split-band coder (TSBC) was developed and found to compare closely in performance with the SBC. To prevent the heavy side information requirement associated with a large number of bands in split-band coding schemes from impairing coding accuracy, without forgoing the efficiency provided by adaptive bit allocation, a method employing AQJs to code the sub-band signals together with vector quantization of the bit allocation patterns was also proposed. Finally, 'pipeline' methods of bit allocation and step size estimation (using the Fast Fourier Transform (FFT) on the input signal) were examined. Such methods, although less accurate, are nevertheless useful in limiting coding delay associated with SRC schemes employing Quadrature Mirror Filters (QMF)

Design of joint source/channel coders

The need to transmit large amounts of data over a band limited channel has led to the development of various data compression schemes. Many of these schemes function by attempting to remove redundancy from the data stream. An unwanted side effect of this approach is to make the information transfer process more vulnerable to channel noise. Efforts at protecting against errors involve the reinsertion of redundancy and an increase in bandwidth requirements. The papers presented within this document attempt to deal with these problems from a number of different approaches

Global Maximum Likelihood Decoding with Hidden Markov Models

This thesis presents a summary of research in areas related to speech communications on degraded channels using very low data rate (VLR) digital voice coders. Background is presented on the nature of voice encoding, problems encountered with real world communications channels and some traditional solutions to these problems. Recent developments which use the Hidden Markov Model (HMM) and Vector Quantization (VQ) to enhance performance are reviewed. A proposal for a new channel decoding technique is then presented. This proposed technique uses the Hidden Markov Model in conjunction with a VLR voice encoder using Vector Quantization. It performs globally maximum likelihood estimates of received vectors over the joint region of received channel signals and possible vector decisions. Finally experimental results which are based on a simulation of the concept are presented.Electrical Engineerin

Adaptive equalisation for fading digital communication channels

This thesis considers the design of new adaptive equalisers for fading digital communication channels. The role of equalisation is discussed in the context of the functions of a digital radio communication system and both conventional and more recent novel equaliser designs are described. The application of recurrent neural networks to the problem of equalisation is developed from a theoretical study of a single node structure to the design of multinode structures. These neural networks are shown to cancel intersymbol interference in a manner mimicking conventional techniques and simulations demonstrate their sensitivity to symbol estimation errors. In addition the error mechanisms of conventional maximum likelihood equalisers operating on rapidly time-varying channels are investigated and highlight the problems of channel estimation using delayed and often incorrect symbol estimates. The relative sensitivity of Bayesian equalisation techniques to errors in the channel estimate is studied and demonstrates that the structure's equalisation capability is also susceptible to such errors. Applications of multiple channel estimator methods are developed, leading to reduced complexity structures which trade performance for a smaller computational load. These novel structures are shown to provide an improvement over the conventional techniques, especially for rapidly time-varying channels, by reducing the time delay in the channel estimation process. Finally, the use of confidence measures of the equaliser's symbol estimates in order to improve channel estimation is studied and isolates the critical areas in the development of the technique — the production of reliable confidence measures by the equalisers and the statistics of symbol estimation error bursts