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

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Title: Transfer Learning Model for Joint Semantic and Channel Coding/Decoding in Wireless Systems.
Authors: Iyer, Sridhar
Source: Wireless Personal Communications; Sep2024, Vol. 138 Issue 1, p475-495, 21p
Subject Terms: ARTIFICIAL neural networks, CHANNEL coding, SCIENTIFIC community, INSTRUCTIONAL systems, BANDWIDTHS, DEEP learning
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:09296212
DOI:10.1007/s11277-024-11517-1