Scalar Quantization Algorithms for the Robust Transmission of Correlated Sources Over One- and Two-Way Channels
The problem of lossy transmission of correlated sources over discrete two-way channels (TWCs) is considered. The objective is to develop a robust low delay and low complexity source-channel coding scheme without using error correction. A simple full-duplex channel optimized scalar quantization (COSQ) scheme that implicitly mitigates TWC interference is designed. Numerical results for sending Gaussian bivariate sources over binary additive-noise TWCs with either additive or multiplicative user interference show that, in terms of signal-to-distortion ratio performance, the proposed full-duplex COSQ scheme compares favourably with half-duplex COSQ. Moreover, our numerical results illustrate that one can achieve significant gain when the proposed two-user COSQ is optimally designed for a discrete TWC with additive Markov noise compared to the case when the TWC is fully interleaved. Furthermore, it is demonstrated that correlation between sources can be useful in order to reduce quantization distortion and boost the decoders' reconstruction reliability. Also, we investigated the effects of feedback in the design of a COSQ over a discrete one-way channel with additive Markov noise by proposing an adaptive COSQ (ACOSQ) where the channel input sequence are adaptively generated based on the received symbols over the feedback link. Numerical results indicate that one can achieve a lower overall distortion by employing feedback in the design of a quantizer for a noisy channel compared to the case where feedback information is not available. Similar to our proposed two-user COSQ, our numerical results demonstrate that one can obtain significant improvement in terms of signal-to-distortion ratio when the ACOSQ scheme is optimally designed for a discrete one-way channel with memory compared to the case where channel is rendered memoryless by interleaving.