Forward-Aware Information Bottleneck-Based Vector Quantization for Noisy Channels
Date Issued
2020-08-25
Abstract
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.
Subjects
Channel quantization
;
Error-prone forward channel
;
Information bottleneck
;
Mutual Information
;
Noisy Channels
Publisher
IEEE
Institution
Institute
Arbeitsbereich Nachrichtentechnik
Type
journal article
Journal/Edited collection
Band
68
Issue
12
Start page
7911
End page
7926
Secondary publication
Yes
Document version
Postprint
License
Alle Rechte vorbehalten
Language
English
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Hassanpour_Dekorsy et al_Forward-Aware Information Bottleneck-Based Vector Quantization_2020-accepted-version.pdf
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