SNR-Independent Joint Source-Channel Coding for wireless image transmission

TL;DR

This paper introduces SNR-Independent Joint Source-Channel Coding (SIJSCC) for wireless image transmission, enabling robust performance across various SNR levels without needing SNR estimation, using novel deep learning modules.

Contribution

The paper proposes a new SNR-independent JSCC method with a specialized IRAB module and mixed encoding, outperforming existing SNR-dependent approaches.

Findings

SIJSCC outperforms SNR-dependent methods in experiments.

SNR estimation offers limited benefits for SIJSCC.

The model shows strong adaptability across different domains.

Abstract

Significant progress has been made in wireless Joint Source-Channel Coding (JSCC) using deep learning techniques. The latest DL-based image JSCC methods have demonstrated exceptional performance during transmission, while also avoiding cliff effects. However, current channel adaptive JSCC methods rely on channel SNR information, which can lead to performance degradation in practical applications due to channel mismatch effects. This paper proposes a novel approach for image transmission, called SNR Independent Joint Source-Channel Coding (SIJSCC), which utilizes Deep Learning techniques to achieve exceptional performance across various signal-to-noise ratio (SNR) levels without SNR estimating. We have designed an Inverted Residual Attention Bottleneck (IRAB) module for the model, which can effectively reduce the number of parameters while expanding the receptive field. In addition, we have incorporated a convolution and self-attention mixed encoding module to establish long-range dependency relationships between channel symbols. Our experiments have shown that SIJSCC outperforms existing channel adaptive DL-based JSCC methods that rely on SNR information. Furthermore, we found that SNR estimation does not significantly benefit SIJSCC, which provides insights for the future design of DL-based JSCC methods. The reliability of the proposed method is further demonstrated through an analysis of the model bottleneck and its adaptability to different domains, as shown by our experiments.