Learned Image Transmission over MIMO Fading Channels

Learned image transmission (LIT) has shown promising progress in recent years to boost the end-to-end transmission performance in semantic communications. To further enhance the system efficiency, in this paper, we propose a novel LIT framework built on multiple-input multiple-output (MIMO) fading channels. In particular, the proposed framework supports concurrent transmission of multiple streams, which can maximize the multiplexing gain in end-to-end semantic communication systems. By jointly considering the entropy distribution of the image semantic features and the wireless MIMO channel states, we design a spatial multiplexing mechanism that can adaptively realize coding rate allocation and stream mapping. As a result, source content and channel environment will be seamlessly coupled, which maximizes the coding gain. Moreover, the proposed LIT model is versatile: a single model can support various transmission rates. The whole model is optimized under the constraint of transmission rate-distortion (RD) tradeoff. Experimental results verify that our scheme substantially increases the throughput of semantic communication systems, and outperforms traditional MIMO communication systems under realistic fading channels.