  In communications theory, the capacity of multiple input multiple
output-orthogonal frequency division multiplexing (MIMO-OFDM) systems is
fundamentally determined by wireless channels, which exhibit both diversity and
correlation in spatial, frequency and temporal domains. It is further
envisioned to exploit the inherent nature of channels, namely representation,
to achieve geolocation-based MIMO transmission for 6G, exemplified by the
fully-decoupled radio access network (FD-RAN). Accordingly, this paper first
employs self-supervised learning to obtain channel representation from
unlabeled channel, then proposes a channel generation assisted approach for
determining MIMO precoding matrix solely based on geolocation. Specifically, we
exploit the small-scale temporal domain variations of channels at a fixed
geolocation, and design an ingenious pretext task tailored for contrastive
learning. Then, a Transformer-based encoder is trained to output channel
representations. We further develop a conditional diffusion generator to
generate channel representations from geolocation. Finally, a
Transformer-encoder-based decoder is utilized to reconstruct channels from
generated representations, where the optimal channel is selected for
calculating the precoding matrix for both single and dual BS transmission. We
conduct experiments on a public ray-tracing channel dataset, and the extensive
simulation results demonstrate the effectiveness of our channel representation
method, and also showcase the performance improvement in geolocation-based MIMO
transmission.
