Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

By Jia-lin Xu, Bo Ai, Wei Chen, Ang Yang, Peng Sun, Miguel L. Rodrigues. IEEE transactions on circuits and systems for video technology (Print), 2020

477 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/1d3900c44eb9840205927dcfbde2d370a574c0b9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Danlan Huang
2. F. Gao
3. Qiyuan Du
4. Jianhua Lu
5. Xiaoming Tao
6. Kailin Tan
7. Sixian Wang
8. Ping Zhang

Citations in the corpus

• Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “It is later shown in [242] that, when the CSI is available at the transmitter and the receiver, an attention mechanism can be used to train a single network that can”
• Toward Semantic Communications: Deep Learning-Based Image Semantic Coding (2022, methodology): “The attention DL-based JSCC for image transmission [11] successfully operates with different SNR levels during transmission.”
• Nonlinear Transform Source-Channel Coding for Semantic Communications (2021, methodology): “The deep JSCC scheme employed in this paper is consistent with [10] and [13].”
• Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, unknown): “In particular, the model with the soft attention mechanism can achieve more than 35 dB PSNR when the SNR is high.”