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

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 channel characteristics respectively and then couple the learned properties by interleaving. The complex-domain computation well preserves the specific intra-domain channel structures, while the interleaving facilitates the inter-domain knowledge exchanges. The resultant physics-inspired CMixer greatly reduces the learning burden, exhibiting extremely high efficiency on channel mapping than existing data-driven approaches. Simulation shows that under typical scenarios and with only light-weighted networks, it brings 4.610dB gains in normalized channel error and up to 67.3% channel data reduction compared to existing schemes while keeping better than 95% cosine correlations w.r.t. the original channels.