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This paper introduces a vision transformer (ViT)-based deep joint source andchannel coding (DeepJSCC) scheme for wireless image transmission overmultiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. Weconsider DeepJSCC-MIMO for adaptive image transmission in both open-loop andclosed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses theclassical separation-based benchmarks with robustness to channel estimationerrors and showcases remarkable flexibility in adapting to diverse channelconditions and antenna numbers without requiring retraining. Specifically, byharnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligentlylearns feature mapping and power allocation strategies tailored to the uniquecharacteristics of the source image and prevailing channel conditions.Extensive numerical experiments validate the significant improvements intransmission quality achieved by DeepJSCC-MIMO for both open-loop andclosed-loop MIMO systems across a wide range of scenarios. Moreover,DeepJSCC-MIMO exhibits robustness to varying channel conditions, channelestimation errors, and different antenna numbers, making it an appealingsolution for emerging semantic communication systems.

Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels