Low Complexity OFDM-Guided DJSCC for Multipath Fading Channels using Tensor Train Decomposition with Fine-Tuning

Man Xu, Chan Tong Lam, Yuanhui Liang, Benjamin K. Ng, Sio Kei Im

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We proposed a low complexity OFDM-Guided deep joint source-channel coding (DJSCC) model for wireless multipath fading channels using fine-tuned tensor train (TT) decomposition. Using the optimal rank of the TT decomposition, we fine-tuned the small number of intermediate channels that lead to incomplete feature extraction after the TT decomposition. The original OFDM-guided DJSCC model is first hierarchically TT decomposition, followed by fine-tuning the size of the intermediate convolution layers to improve the performance. From the experimental results, we found that the smaller the signal-to-noise ratio (SNR) value, the smaller the loss of peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) performance. The best compressed model using TT decomposition with fine-tuning loses about 4% of PSNR performance and 2% of the SSIM performance, with a compression ratio of about 17% and a reduction of the number of parameters by 6 times of the original OFDM-guided DJSCC model.

Original languageEnglish
Title of host publication2023 8th International Conference on Signal and Image Processing, ICSIP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages853-859
Number of pages7
ISBN (Electronic)9798350397932
DOIs
Publication statusPublished - 2023
Event8th International Conference on Signal and Image Processing, ICSIP 2023 - Wuxi, China
Duration: 8 Jul 202310 Jul 2023

Publication series

Name2023 8th International Conference on Signal and Image Processing, ICSIP 2023

Conference

Conference8th International Conference on Signal and Image Processing, ICSIP 2023
Country/TerritoryChina
CityWuxi
Period8/07/2310/07/23

Keywords

  • deep joint source-channel coding
  • multipath fading channel
  • orthogonal frequency division multiplexing
  • tensor train decomposition

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