  We present a learning-based channel-adaptive joint source and channel coding
(CA-JSCC) scheme for wireless image transmission over multipath fading
channels. The proposed method is an end-to-end autoencoder architecture with a
dual-attention mechanism employing orthogonal frequency division multiplexing
(OFDM) transmission. Unlike the previous works, our approach is adaptive to
channel-gain and noise-power variations by exploiting the estimated channel
state information (CSI). Specifically, with the proposed dual-attention
mechanism, our model can learn to map the features and allocate
transmission-power resources judiciously based on the estimated CSI. Extensive
numerical experiments verify that CA-JSCC achieves state-of-the-art performance
among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel
conditions and can better exploit the limited channel resources by transmitting
critical features over better subchannels.
