Joint Source-Channel Coding for a Multivariate Gaussian over a Gaussian MAC using Variational Domain Adaptation

With the development of the distributed learning and edge computing, servers must often receive information from multiple terminal devices; thus, the importance of source-channel coding for distributed sources over multiple access channels (MACs) becomes more and more significant. This study present...

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Vydáno v:	IEEE transactions on cognitive communications and networking Ročník 9; číslo 6; s. 1
Hlavní autoři:	Li, Yishen, Chen, Xuechen, Deng, Xiaoheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptation Adaptation models Coding Correlation coefficients Decoding Deep joint source-channel coding Delays domain adaptation Domains Edge computing Encoding Feature extraction Learning multiple-access channel Multivariate analysis multivariate Gaussian Regularization Symbols variational autoencoder
ISSN:	2332-7731, 2332-7731
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Abstract	With the development of the distributed learning and edge computing, servers must often receive information from multiple terminal devices; thus, the importance of source-channel coding for distributed sources over multiple access channels (MACs) becomes more and more significant. This study presents a deep joint source-channel coding (JSCC) design for a multivariate Gaussian source over a Gaussian MAC. The widely used autoencoder based deep-JSCC cannot perform stably under such conditions due to their easiness to fall into local optimum. Therefore we propose the variational domain adaptation (VDA)-JSCC scheme. Firstly, the loss function with an additional regularization term is introduced through variational analysis. The crucial prior distribution related to this item is obtained by domain adaptation, which is a transfer learning method. The proposed fine-tuning technique during the training process yields further performance improvement. Experiment results show that VDA-JSCC can always learn reasonable coding structures without artificial design and outperforms other state-of-the-art methods under different channel signal-to-noise ratios (CSNRs). We have also analyzed the reason why the performance of VDA-JSCC deteriorates in high CSNR range and then replace the encoder of VDA-JSCC with Mixture-of-Experts to improve its performance in high CSNR range. Finally, VDA-JSCC exhibits considerable robustness when the channel quality or correlation coefficient varies.
AbstractList	With the development of the distributed learning and edge computing, servers must often receive information from multiple terminal devices; thus, the importance of source-channel coding for distributed sources over multiple access channels (MACs) becomes more and more significant. This letter presents a deep joint source-channel coding (JSCC) design for a multivariate Gaussian source over a Gaussian MAC. The widely used autoencoder based deep-JSCC cannot perform stably under such conditions due to their easiness to fall into local optimum. Therefore we propose the variational domain adaptation (VDA)-JSCC scheme. Firstly, the loss function with an additional regularization term is introduced through variational analysis. The crucial prior distribution related to this item is obtained by domain adaptation, which is a transfer learning method. The proposed fine-tuning technique during the training process yields further performance improvement. Experiment results show that VDA-JSCC can always learn reasonable coding structures without artificial design and outperforms other state-of-the-art methods under different channel signal-to-noise ratios (CSNRs). We have also analyzed the reason why the performance of VDA-JSCC deteriorates in high CSNR range and then replace the encoder of VDA-JSCC with Mixture-of-Experts to improve its performance in high CSNR range. Finally, VDA-JSCC exhibits considerable robustness when the channel quality or correlation coefficient varies. With the development of the distributed learning and edge computing, servers must often receive information from multiple terminal devices; thus, the importance of source-channel coding for distributed sources over multiple access channels (MACs) becomes more and more significant. This study presents a deep joint source-channel coding (JSCC) design for a multivariate Gaussian source over a Gaussian MAC. The widely used autoencoder based deep-JSCC cannot perform stably under such conditions due to their easiness to fall into local optimum. Therefore we propose the variational domain adaptation (VDA)-JSCC scheme. Firstly, the loss function with an additional regularization term is introduced through variational analysis. The crucial prior distribution related to this item is obtained by domain adaptation, which is a transfer learning method. The proposed fine-tuning technique during the training process yields further performance improvement. Experiment results show that VDA-JSCC can always learn reasonable coding structures without artificial design and outperforms other state-of-the-art methods under different channel signal-to-noise ratios (CSNRs). We have also analyzed the reason why the performance of VDA-JSCC deteriorates in high CSNR range and then replace the encoder of VDA-JSCC with Mixture-of-Experts to improve its performance in high CSNR range. Finally, VDA-JSCC exhibits considerable robustness when the channel quality or correlation coefficient varies.
Author	Chen, Xuechen Li, Yishen Deng, Xiaoheng
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SubjectTerms	Adaptation Adaptation models Coding Correlation coefficients Decoding Deep joint source-channel coding Delays domain adaptation Domains Edge computing Encoding Feature extraction Learning multiple-access channel Multivariate analysis multivariate Gaussian Regularization Symbols variational autoencoder
Title	Joint Source-Channel Coding for a Multivariate Gaussian over a Gaussian MAC using Variational Domain Adaptation
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