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 across various signal-to-noise ratio
(SNR) levels during transmission, while also avoiding cliff effects. However,
current channel adaptive JSCC methods rely heavily on channel prior knowledge,
which can lead to performance degradation in practical applications due to
channel mismatch effects. This paper proposes a novel approach for image
transmission, called Channel Blind Joint Source-Channel Coding (CBJSCC). CBJSCC
utilizes Deep Learning techniques to achieve exceptional performance across
various signal-to-noise ratio (SNR) levels during transmission, without relying
on channel prior information. 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 CBJSCC outperforms existing channel adaptive
DL-based JSCC methods that rely on feedback information. Furthermore, we found
that channel estimation does not significantly benefit CBJSCC, 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