  In this paper, we propose two deep joint source and channel coding (DJSCC)
structures with attention modules for the multi-input multi-output (MIMO)
channel, including a serial structure and a parallel structure. With singular
value decomposition (SVD)-based precoding scheme, the MIMO channel can be
decomposed into various sub-channels, and the feature outputs will experience
sub-channels with different channel qualities. In the serial structure, one
single network is used at both the transmitter and the receiver to jointly
process data streams of all MIMO subchannels, while data steams of different
MIMO subchannels are processed independently via multiple sub-networks in the
parallel structure. The attention modules in both serial and parallel
architectures enable the system to adapt to varying channel qualities and
adjust the quantity of information outputs in accordance with the channel
qualities. Experimental results demonstrate the proposed DJSCC structures have
improved image transmission performance, and reveal the phenomenon via
non-parameter entropy estimation that the learned DJSCC transceivers tend to
transmit more information over better sub-channels.
