This paper introduces a vision transformer (ViT)-based deep joint source and
channel coding (DeepJSCC) scheme for wireless image transmission over
multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We
consider DeepJSCC-MIMO for adaptive image transmission in both open-loop and
closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the
classical separation-based benchmarks with robustness to channel estimation
errors and showcases remarkable flexibility in adapting to diverse channel
conditions and antenna numbers without requiring retraining. Specifically, by
harnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligently
learns feature mapping and power allocation strategies tailored to the unique
characteristics of the source image and prevailing channel conditions.
Extensive numerical experiments validate the significant improvements in
transmission quality achieved by DeepJSCC-MIMO for both open-loop and
closed-loop MIMO systems across a wide range of scenarios. Moreover,
DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel
estimation errors, and different antenna numbers, making it an appealing
solution for emerging semantic communication systems.Comment: arXiv admin note: text overlap with arXiv:2210.1534