Forward-Aware Information Bottleneck-Based Vector Quantization for Noisy Channels

摘要

The main focus will be on the indirect Joint Source-Channel Coding problem in which a noisy observation of the source has to be quantized ahead of transmission over an error-prone forward link to a remote processing unit. To that end, we present here a complete extension to the preliminary Information Bottleneck method by providing the formal optimal solution to this newly established Variational Principle , together with an algorithm, the Forward-Aware Vector Information Bottleneck (FAVIB) , to pragmatically tackle its underlying non-convex design optimization. FAVIB extends the current state-of-the-art approaches via capacitating a full sweep over the entire gamut of the trade-off parameter. Consequently, the trajectory of all achievable points in the Information-Compression plane becomes traversable via soft mappings. It will be shown that, by enjoying an inherent error protection, this novel compression scheme can obviate the call for separate channel coding on the forward path.