We present a source-channel coding system for operation over code-division multiple-access channels with time-varying conditions. The proposed system consists of a multistage vector quantizer (MSVQ) in conjunction with unequal protection against channel errors. The receiver estimates the channel conditions and decodes as many stages of the quantizer as can be reliably decoded. The approach to system design and optimization is first derived and evaluated for a system that employs hard decoding of stage indices. The approach is then extended to the more general case of weighted decoding. Simulation results are given for transmission of Gauss-Markov sources over broadcast and slow fading channels. Consistent and substantial improvement is achieved over the standard MSVQ with equal error protection, and the gains, in terms of source signal-to-noise ratio, are in the range of 3-5 dB.

This work is concerned with the problem of designing robust, vector quantizer (VQ)-based communication systems for operation over time-varying Gaussian channels. Transmission energy allocation to VQ codeword bits, according to their error sensitivities, is a powerful tool for improving robustness to channel noise. The power of this technique can be further enhanced by appropriately combining it with index assignment methods. We pose the corresponding joint optimization problem and suggest a simple iterative algorithm for finding a locally optimal solution. The susceptibility of the solution to poor local minima is significantly reduced by an enhanced version of the algorithm which invokes the method of noisy channel relaxation whereby the VQ system is optimized while gradually decreasing the assumed level of channel noise. In a series of experiments, the resulting combined technique is shown to outperform standard pseudo-Gray coding by up to 3.5 dB and to exhibit graceful degradation at mismatched channel conditions. Finally, we extend these ideas to the case where both the transmitter and the receiver have information on the current state of a time-varying channel. The proposed method is based on switched encoding and adaptive decoding. Experimental results show that the proposed system achieves close to optimal performance.

This paper is concerned with the design problem of coded modulation schemes which provide unequal error protection (UEP) against Gaussian noise. Starting with the case of two bit streams, we consider two classes of UEP schemes: time-division coded modulation (TDCM) and superposition coded modulation (SCM). The early result of Bergmanns and Cover (1974) ensures the asymptotic superiority of SCM. However, accounting for the fact that practical channel codes do not achieve capacity, we show that the validity of this result depends on the prescribed degree of inequality in protection of the streams. To complement the straightforward design and use of TDCM schemes, we propose methods to decode and design SCM schemes. The design examples substantiate the conclusions obtained from performance analysis. We extend the results to the case of UEP of multiple (more than two) bit streams, where a hybrid of SCM and TDCM must also be considered. We show that, for a large range of unequal protection levels, the hybrid scheme outperforms both SCM and TDCM.

This article proposes a method to design a vector quantizer (VQ) for robust performance under noisy channel conditions. By re-optimizing the quantizer at progressively lower levels of assumed channel noise, the design is less susceptible to poor local optima. The method is applied to: (1) channel-optimized VQ design; and (2) index assignment for a source-optimized VQ. For both problems, we demonstrate substantial performance improvements over commonly used techniques.

We present a source-channel coding system consisting of a multi-stage vector quantizer in conjunction with unequal protection against channel errors. The receiver estimates the channel conditions and decodes as many stages of the quantized signal as can be reliably decoded. The proposed overall system is optimized for robustness to time-varying noisy channels. Two versions of the system are designed and evaluated, which involve: (i) hard, and (ii) weighted decoding of the stage indices. Simulation results are given for Gauss-Markov sources transmitted over broadcast and fading channels. Consistent and substantial improvement is achieved over the standard multi-stage vector quantizer with equal error protection, and the gains are in the range of 3 to 5 dB.

We attack the problem of robust and efficient image compression for transmission over noisy channels. To achieve the dual goals of high compression efficiency and low sensitivity to channel noise we introduce a multimode coding framework. Multimode coders are quasi-fixed length in nature, and allow optimization of the tradeoff between the compression capability of variable-length coding and the robustness to channel errors of fixed length coding. We apply our framework to develop multimode image coding (MIC) schemes for noisy channels, based on the adaptive DCT. The robustness of the proposed MIC is further enhanced by the incorporation of a channel protection scheme suitable for the constraints on complexity and delay. To demonstrate the power of the technique we develop two specific image coding algorithms optimized for the binary symmetric channel. The first, MIC1, incorporates channel optimized quantizers and the second, MIC2, uses rate compatible punctured convolutional codes within the multimode framework. Simulations demonstrate that the multimode coders obtain significant performance gains of up to 6 dB over conventional fixed length coding techniques.

In this work we address the problem of designing robust, vector quantizer (VQ) based communication systems for operation over time-varying Gaussian channels. Transmission energy allocation (TEA) to VQ codeword bits, according to their error sensitivities, is a powerful tool for improving robustness to channel noise. The power of this technique can be further enhanced by appropriately combining it with index assignment methods. We pose the corresponding joint optimization problem and suggest a simple iterative algorithm for finding a locally optimal solution. The susceptibility of the solution to poor local minima is significantly reduced by an enhanced version of the algorithm which invokes the the method of noisy channel relaxation, whereby, the VQ system is optimized while gradually decreasing the assumed level of channel noise. The resulting combined technique is shown to outperform standard pseudo-Gray coding by up to 3.5 dB, and to exhibit graceful degradation at mismatched channel conditions. We conclude with a brief discussion of the impact of TEA on the peak-to-average energy ratio of the transmitted modulated signals.

We attack the problem of designing robust image compression schemes for transmission over noisy channels. To achieve the dual goals of high compression efficiency and low sensitivity to channel noise, we introduce a multimode coding framework. Multimode coders are quasi-fixed length in nature, and this enables optimal tradeoff between the compression capability of variable-length coding and the robustness of fixed length coding. The framework lends itself to a natural incorporation of conventional source-channel coding mechanisms, and thus provides a joint design methodology for the source and channel coding components. We first derive a general Lagrangian based algorithm to design the multimode coder which optimizes the overall rate-distortion performance. We then apply our framework to develop multimode image coding (MIC) schemes for noisy channels. We demonstrate the benefits of the multimode approach by developing two specific image compression algorithms in conjunction with the following source-channel coding mechanisms: (i) channel optimized quantizers, and (ii) transmission energy allocation. Simulations demonstrate that substantial gains of up to 7 dB can be achieved by MICs over conventional approaches.

We study the structure and design of a vector quantizer (VQ) for robust performance under noisy channel conditions. We develop some new insights about the geometrical structure of such a VQ, and introduce noisy channel relaxation-a novel design approach. In particular, the approach is used to attack two basic problems: (1) optimize the VQ for a given noisy channel; and (2) optimize index assignment while maintaining optimality for the noiseless channel. For problem (1) we show consistent improvements over descent methods at the cost of manageable increase in complexity (by a factor of up to three). For problem (2) we obtain index assignment with a quality significantly better than known methods, but at computational complexity that grows many times slower.