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

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Bibliographic Details
Title: Machine Learning Model for Joint Semantic and Channel Coding/Decoding in Wireless Systems.
Authors: Iyer, Sridhar
Source: IETE Journal of Research; May2024, Vol. 70 Issue 5, p4489-4499, 11p
Subject Terms: ARTIFICIAL neural networks, MACHINE learning, CHANNEL coding, SCIENTIFIC community, INSTRUCTIONAL systems, DEEP learning
Abstract: With a significant increase in the data and demand for large bandwidth, the wireless research community has identified the need to pay attention to transmitting the context rather than focusing on only the manner of transmission. Inspired by this, in the current article 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, the proposed technique is able to recover the meaning of sentences and is hence able to minimize the semantic errors. Furthermore, transfer learning is implemented to accelerate the process of re-training. Extensive simulations to validate the performance of the proposed technique demonstrate that it is able to (i) maintain enhanced robustness to channel fluctuations and (ii) achieve higher performance. Overall, the current study makes it evident that the proposed technique is a good candidate for implementation in semantic wireless systems. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:With a significant increase in the data and demand for large bandwidth, the wireless research community has identified the need to pay attention to transmitting the context rather than focusing on only the manner of transmission. Inspired by this, in the current article 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, the proposed technique is able to recover the meaning of sentences and is hence able to minimize the semantic errors. Furthermore, transfer learning is implemented to accelerate the process of re-training. Extensive simulations to validate the performance of the proposed technique demonstrate that it is able to (i) maintain enhanced robustness to channel fluctuations and (ii) achieve higher performance. Overall, the current study makes it evident that the proposed technique is a good candidate for implementation in semantic wireless systems. [ABSTRACT FROM AUTHOR]
ISSN:03772063
DOI:10.1080/03772063.2023.2252384