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2023

Semantic Multi-Resolution Communications

Mortaheb, Matin, Khojastepour, Mohammad A. Amir, Chakradhar, Srimat T. et al.

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Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC).

This superiority arises from the suboptimality of SSCC when dealing with finite block-length data.
Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data.
To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL).

Semantic Multi-Resolution Communications · Around

Built on

A mathematical theory of communication
C. E. Shannon · 1948
Earlier work this paper cites.
Source and channel coding for correlated sources over multiuser channels
D. Gunduz, E. Erkip, A. Goldsmith, and H. V. Poor · 2009
Earlier work this paper cites.
Autoencoders, unsupervised learning, and deep architectures
P. Baldi · 2012
Earlier work this paper cites.
Model-free training of end-to-end communication systems
F. A. Aoudia and J. Hoydis · 2019
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