UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Xu, J; Ai, B; Chen, W; Yang, A; Sun, P; Rodrigues, M; (2022) Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules. IEEE Transactions on Circuits and Systems for Video Technology , 32 (4) pp. 2315-2328. 10.1109/TCSVT.2021.3082521. Green open access

[thumbnail of bare_jrnl.pdf]
Preview
Text
bare_jrnl.pdf - Accepted Version

Download (2MB) | Preview

Abstract

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.

Type: Article
Title: Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TCSVT.2021.3082521
Publisher version: https://doi.org/10.1109/TCSVT.2021.3082521
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Joint source channel coding, deep learning, deep neural network, attention mechanism
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10147005
Downloads since deposit
99Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item