  With the depletion of spectrum, wireless communication systems turn to
exploit large antenna arrays to achieve the degree of freedom in space domain,
such as millimeter wave massive multi-input multioutput (MIMO), reconfigurable
intelligent surface assisted communications and cell-free massive MIMO. In
these systems, how to acquire accurate channel state information (CSI) is
difficult and becomes a bottleneck of the communication links. In this article,
we introduce the concept of channel extrapolation that relies on a small
portion of channel parameters to infer the remaining channel parameters. Since
the substance of channel extrapolation is a mapping from one parameter subspace
to another, we can resort to deep learning (DL), a powerful learning
architecture, to approximate such mapping function. Specifically, we first
analyze the requirements, conditions and challenges for channel extrapolation.
Then, we present three typical extrapolations over the antenna dimension, the
frequency dimension, and the physical terminal, respectively. We also
illustrate their respective principles, design challenges and DL strategies. It
will be seen that channel extrapolation could greatly reduce the transmission
overhead and subsequently enhance the performance gains compared with the
traditional strategies. In the end, we provide several potential research
directions on channel extrapolation for future intelligent communications
systems.
