Transfer Learning Model for Joint Semantic and Channel Coding/Decoding in Wireless Systems

With significant increase in data and demand for large bandwidth, the wireless research community has identified the need to focus on ‘what’ to transmit rather than focusing on only ‘how’ to transmit. Inspired by this, in the current article a view of wireless systems based on semantic communication...

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Vydané v:Wireless personal communications Ročník 138; číslo 1; s. 475 - 495
Hlavný autor: Iyer, Sridhar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.09.2024
Springer Nature B.V
Predmet:
Bandwidths
Communications Engineering
Communications systems
Computer Communication Networks
Data compression
Decoding
Deep learning
Design
Digital transmission
Engineering
Errors
Networks
Semantics
Signal,Image and Speech Processing
Transmitters
Deep learning
Semantic communication
Deep neural networks
Transfer learning
Wireless systems
ISSN:0929-6212, 1572-834X
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Shrnutí:With significant increase in data and demand for large bandwidth, the wireless research community has identified the need to focus on ‘what’ to transmit rather than focusing on only ‘how’ to transmit. Inspired by this, in the current article a view of wireless systems based on semantic communications is put-forth. Specifically, a semantic wireless system enabled by deep learning is proposed which aims to maximize the system capacity. As opposed to evaluating only the bit/symbol errors, proposed technique is able to recover the meaning of sentences and is hence able to minimize the semantic errors. Further, transfer learning is implemented for accelerating the process of re-training. Extensive simulations to validate performance of the proposed technique demonstrate that it is able to (i) maintain enhanced robustness to variations in the channel, and (ii) achieve higher performance. Overall, the current study makes it evident that the proposed technique is a good candidate for implementation in the semantic wireless systems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-024-11517-1