SNR-EQ-JSCC: Joint Source-Channel Coding with SNR-Based Embedding and Query
This work addresses channel adaptation in semantic communication systems, offering a practical solution for improving transmission efficiency in dynamic environments, though it appears incremental as it builds on existing Transformer-based methods.
The paper tackles the problem of dynamic channels in joint source-channel coding for semantic communication by proposing SNR-EQ-JSCC, a lightweight channel-adaptive architecture that outperforms state-of-the-art methods in PSNR and perception metrics with only 0.05% storage overhead and 6.38% computational complexity for channel adaptation.
Coping with the impact of dynamic channels is a critical issue in joint source-channel coding (JSCC)-based semantic communication systems. In this paper, we propose a lightweight channel-adaptive semantic coding architecture called SNR-EQ-JSCC. It is built upon the generic Transformer model and achieves channel adaptation (CA) by Embedding the signal-to-noise ratio (SNR) into the attention blocks and dynamically adjusting attention scores through channel-adaptive Queries. Meanwhile, penalty terms are introduced in the loss function to stabilize the training process. Considering that instantaneous SNR feedback may be imperfect, we propose an alternative method that uses only the average SNR, which requires no retraining of SNR-EQ-JSCC. Simulation results conducted on image transmission demonstrate that the proposed SNR-EQJSCC outperforms the state-of-the-art SwinJSCC in peak signal-to-noise ratio (PSNR) and perception metrics while only requiring 0.05% of the storage overhead and 6.38% of the computational complexity for CA. Moreover, the channel-adaptive query method demonstrates significant improvements in perception metrics. When instantaneous SNR feedback is imperfect, SNR-EQ-JSCC using only the average SNR still surpasses baseline schemes.