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Significant progress has been made in wireless Joint Source-Channel Coding(JSCC) using deep learning techniques. The latest DL-based image JSCC methodshave demonstrated exceptional performance during transmission, while alsoavoiding cliff effects. However, current channel adaptive JSCC methods rely onchannel SNR information, which can lead to performance degradation in practicalapplications due to channel mismatch effects. This paper proposes a novelapproach for image transmission, called SNR Independent Joint Source-ChannelCoding (SIJSCC), which utilizes Deep Learning techniques to achieve exceptionalperformance across various signal-to-noise ratio (SNR) levels without SNRestimating. We have designed an Inverted Residual Attention Bottleneck (IRAB)module for the model, which can effectively reduce the number of parameterswhile expanding the receptive field. In addition, we have incorporated aconvolution and self-attention mixed encoding module to establish long-rangedependency relationships between channel symbols. Our experiments have shownthat SIJSCC outperforms existing channel adaptive DL-based JSCC methods thatrely on SNR information. Furthermore, we found that SNR estimation does notsignificantly benefit SIJSCC, which provides insights for the future design ofDL-based JSCC methods. The reliability of the proposed method is furtherdemonstrated through an analysis of the model bottleneck and its adaptabilityto different domains, as shown by our experiments.

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