  Though achieving marvelous progress in various scenarios, existing semantic
communication frameworks mainly consider single-input single-output Gaussian
channels or Rayleigh fading channels, neglecting the widely-used multiple-input
multiple-output (MIMO) channels, which hinders the application into practical
systems. One common solution to combat MIMO fading is to utilize feedback MIMO
channel state information (CSI). In this paper, we incorporate MIMO CSI into
system designs from a new perspective and propose the learnable CSI fusion
semantic communication (LCFSC) framework, where CSI is treated as side
information by the semantic extractor to enhance the semantic coding. To avoid
feature fusion due to abrupt combination of CSI with features, we present a
non-invasive CSI fusion multi-head attention module inside the Swin
Transformer. With the learned attention masking map determined by both source
and channel states, more robust attention distribution could be generated.
Furthermore, the percentage of mask elements could be flexibly adjusted by the
learnable mask ratio, which is produced based on the conditional variational
interference in an unsupervised manner. In this way, CSI-aware semantic coding
is achieved through learnable CSI fusion masking. Experiment results testify
the superiority of LCFSC over traditional schemes and state-of-the-art Swin
Transformer-based semantic communication frameworks in MIMO fading channels.
