  This paper investigates robust semantic communications over multiple-input
multiple-output (MIMO) fading channels. Current semantic communications over
MIMO channels mainly focus on channel adaptive encoding and decoding, which
lacks exploration of signal distribution. To leverage the potential of signal
distribution in signal space denoising, we develop a diffusion model over MIMO
channels (DM-MIMO), a plugin module at the receiver side in conjunction with
singular value decomposition (SVD) based precoding and equalization.
Specifically, due to the significant variations in effective noise power over
distinct sub-channels, we determine the effective sampling steps accordingly
and devise a joint sampling algorithm. Utilizing a three-stage training
algorithm, DM-MIMO learns the distribution of the encoded signal, which enables
noise elimination over all sub-channels. Experimental results demonstrate that
the DM-MIMO effectively reduces the mean square errors (MSE) of the equalized
signal and the DM-MIMO semantic communication system (DM-MIMO-JSCC) outperforms
the JSCC-based semantic communication system in image reconstruction.
