  In this paper, we propose a neural-network-based realistic channel model with
both the similar accuracy as deterministic channel models and uniformity as
stochastic channel models. To facilitate this realistic channel modeling, a
multi-domain channel embedding method combined with self-attention mechanism is
proposed to extract channel features from multiple domains simultaneously. This
'one model to fit them all' solution employs available wireless channel data as
the only data set for self-supervised pre-training. With the permission of
users, network operators or other organizations can make use of some available
user specific data to fine-tune this pre-trained realistic channel model for
applications on channel-related downstream tasks. Moreover, even without
fine-tuning, we show that the pre-trained realistic channel model itself is a
great tool with its understanding of wireless channel.
