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In multiple-input multiple-output (MIMO) orthogonal frequency divisionmultiplexing (OFDM) systems, representing the whole channel only based onpartial subchannels will significantly reduce the channel acquisition overhead.For such a channel mapping task, inspired by the intrinsic coupling across thespace and frequency domains, this letter proposes to use interleaved learningwith partial antenna and subcarrier characteristics to represent the wholeMIMO-OFDM channel. Specifically, we design a complex-domain multilayerperceptron (MLP)-Mixer (CMixer), which utilizes two kinds of complex-domain MLPmodules to learn the space and frequency characteristics respectively and theninterleaves them to couple the learned properties. The complex-domaincomputation facilitates the learning on the complex-valued channel data, whilethe interleaving tightens the coupling of space and frequency domains. Thesetwo designs jointly reduce the learning burden, making the physics-inspiredCMixer more effective on channel representation learning than existingdata-driven approaches. Simulation shows that the proposed scheme brings4.6~10dB gains in mapping accuracy compared to existing schemes under differentsettings. Besides, ablation studies show the necessity of complex-domaincomputation as well as the extent to which the interleaved learning matches thechannel properties.

Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer