  In the realm of reconfigurable intelligent surface (RIS)-assisted wireless
communications, efficient channel state information (CSI) feedback is
paramount. This paper introduces RIS-CoCsiNet, a novel deep learning-based
framework designed to greatly enhance feedback efficiency. By leveraging the
inherent correlation among proximate user equipments (UEs), our approach
strategically categorizes RIS-UE CSI into shared and unique data sets. This
nuanced understanding allows for significant reductions in feedback overhead,
as the shared data is no longer redundantly relayed. Setting RIS-CoCsiNet apart
from traditional autoencoder systems, we incorporate an additional decoder and
a combination neural network at the base station. These enhancements are tasked
with the precise retrieval and fusion of shared and individual data. And
notably, all these innovations are achieved without modifying the UEs. For
those UEs boasting multiple antennas, our design seamlessly integrates long
short-term memory modules, capturing the intricate correlations between
antennas. With a recognition of the non-sparse nature of the RIS-UE CSI phase,
we pioneer two magnitude-dependent phase feedback strategies. These strategies
adeptly weave in both statistical and real-time CSI magnitude data. The potency
of RIS-CoCsiNet is further solidified through compelling simulation results
drawn from two diverse channel datasets.
