A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle

In this article, we concentrate on a generic multiterminal joint source-channel coding scenario, appearing in a wide variety of real-world applications. Specifically, several noisy observations from a source signal must be compressed at some intermediate nodes, before getting forwarded over multiple...

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Vydáno v:IEEE open journal of the Communications Society Ročník 6; s. 4462 - 4475
Hlavní autoři: Hassanpour, Shayan, Hummert, Matthias, Wubben, Dirk, Dekorsy, Armin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
6G mobile communication
auto-encoders
Channel coding
Channels
Coding
Decoding
Deep learning
Distortion
information bottleneck
joint source-channel coding
Mutual information
Noise measurement
Optimization
Source coding
Vectors
ISSN:2644-125X, 2644-125X
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Shrnutí:In this article, we concentrate on a generic multiterminal joint source-channel coding scenario, appearing in a wide variety of real-world applications. Specifically, several noisy observations from a source signal must be compressed at some intermediate nodes, before getting forwarded over multiple error-prone and rate-limited channels towards a (remote) processing unit. The imperfections of the forward channels should be integrated into the design of (local) compressor units. By following the Information Bottleneck principle, the Mutual Information is selected here as the fidelity criterion, and a novel (data-driven) design approach is presented for two distinct types of processing flow/strategy at the remote unit. To that end, tractable objective functions are developed, together with the pertinent learning architectures, generalizing the concepts of Variational Auto-Encoders and (Distributed) Deep Variational Information Bottleneck for (remote) source coding to the context of distributed joint source-channel coding. Unlike the conventional approaches, the proposed schemes here work based upon a finite sample set, thereby obviating the call for full prior knowledge of the joint statistics of input signals. The effectiveness of these novel sample-based compression schemes is substantiated as well by a couple of simulations over typical transmission setups.
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ISSN:2644-125X
2644-125X
DOI:10.1109/OJCOMS.2025.3570446