  Recently, deep learning-enabled joint-source channel coding (JSCC) has
received increasing attention due to its great success in image transmission.
However, most existing JSCC studies only focus on single-input single-output
(SISO) channels. In this paper, we first propose a JSCC system for wireless
image transmission over multiple-input multiple-output (MIMO) channels. As the
complexity of an image determines its reconstruction difficulty, the JSCC
achieves quite different reconstruction performances on different images.
Moreover, we observe that the images with higher reconstruction qualities are
generally more robust to the noise, and can be allocated with less
communication resources than the images with lower reconstruction qualities.
Based on this observation, we propose an adaptive channel state information
(CSI) feedback scheme for precoding, which improves the effectiveness by
adjusting the feedback overhead. In particular, we develop a performance
evaluator to predict the reconstruction quality of each image, so that the
proposed scheme can adaptively decrease the CSI feedback overhead for the
transmitted images with high predicted reconstruction qualities in the JSCC
system. We perform experiments to demonstrate that the proposed scheme can
significantly improve the image transmission performance with much-reduced
feedback overhead.
