  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.
