  In multiple-input multiple-output (MIMO) orthogonal frequency division
multiplexing (OFDM) systems, representing the whole channel only based on
partial subchannels will significantly reduce the channel acquisition overhead.
For such a channel mapping task, inspired by the intrinsic coupling across the
space and frequency domains, this letter proposes to use interleaved learning
with partial antenna and subcarrier characteristics to represent the whole
MIMO-OFDM channel. Specifically, we design a complex-domain multilayer
perceptron (MLP)-Mixer (CMixer), which utilizes two kinds of complex-domain MLP
modules to learn the space and frequency characteristics respectively and then
interleaves them to couple the learned properties. The complex-domain
computation facilitates the learning on the complex-valued channel data, while
the interleaving tightens the coupling of space and frequency domains. These
two designs jointly reduce the learning burden, making the physics-inspired
CMixer more effective on channel representation learning than existing
data-driven approaches. Simulation shows that the proposed scheme brings
4.6~10dB gains in mapping accuracy compared to existing schemes under different
settings. Besides, ablation studies show the necessity of complex-domain
computation as well as the extent to which the interleaved learning matches the
channel properties.
