SA-RA-JSCC: SNR-Adaptive and Semantic-Rate-Aware Joint Source-Channel Coding

2026-06-16

AI Summary

This work addresses the challenge of reliably transmitting image semantics over time-varying channels in joint source-channel coding (JSCC) by proposing a novel channel-adaptive semantic communication model. The model uniquely maps signal-to-noise ratio (SNR) to a unified semantic vector and achieves consistent channel adaptation through global, one-time feature reweighting. Additionally, it incorporates a semantic rate-aware module that jointly responds to variations in both channel quality and semantic information rate. Experimental results demonstrate that the proposed method significantly outperforms existing semantic communication schemes across multiple datasets and channel conditions, achieving superior performance in terms of PSNR and MS-SSIM while exhibiting enhanced robustness and coordination over a wide SNR range.

Abstract

In joint source-channel coding (JSCC)-based semantic communication systems, achieving stable and reliable image semantic transmission under channel constraints remains a key challenge. In most channel adaptation modules, the signal-to-noise ratio (SNR) is often injected into each layer of a channel-adaptation model in an independent and layer-wise manner, which undermines global coordination across layers. Therefore, consistent noise-robust representations may fail to be learned throughout the model. To address this problem, we propose SA-RA-JSCC, a novel channel-adaptive JSCC model. SA-RA-JSCC maps SNR into a unified semantic vector in the feature space and then applies a one-shot global reweighting to the encoded features, thereby enabling globally consistent and learnable channel adaptation. Moreover, in order to further enhance the anti-channel capability of semantic information, a semantic-rate-aware module is introduced, enabling the adaptive policy to respond simultaneously to fluctuations in channel quality and changes in semantic-rate constraints, thereby enhancing global network coordination and channel adaptivity. Extensive experiment results across multiple channels and datasets demonstrate that SA-RA-JSCC significantly outperforms existing semantic communication models in terms of reconstruction metrics such as PSNR and MS-SSIM, exhibiting stronger robustness across a broad range of SNR regimes.

Problem

• semantic communication
• joint source-channel coding
• channel adaptation
• SNR robustness
• image transmission

Innovation

• SNR-adaptive
• semantic-rate-aware
• joint source-channel coding
• global reweighting
• semantic communication