基于复数域MLP-Mixer交错学习的信道映射
Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer
AI总结：
针对MIMO-OFDM系统信道映射开销问题，提出复数域MLP-Mixer（CMixer），通过复数域MLP模块交错学习部分天线与子载波的空间及频率特征，实现低负担的完整信道表示，映射精度提升4.6~10dB。
摘要：
在多输入多输出（MIMO）正交频分复用（OFDM）系统中，仅基于部分子信道表示整个信道将显著降低信道获取开销。针对此类信道映射任务，受空间和频域间内在耦合的启发，本信提出使用部分天线和子载波特征的交错学习来表示整个MIMO-OFDM信道。具体而言，我们设计了一种复数域多层感知机（MLP）-Mixer（CMixer），它利用两种复数域MLP模块分别学习空间和频率特征，然后将它们交错以耦合所学属性。复数域计算促进了对复数值信道数据的学习，而交错则收紧了空间和频域的耦合。这两种设计共同减轻了学习负担，使得物理启发的CMixer在信道表示学习上比现有数据驱动方法更有效。仿真表明，与不同设置下的现有方案相比，所提方案在映射精度上带来了4.6~10dB的增益。此外，消融研究表明了复数域计算的必要性，以及交错学习与信道属性匹配的程度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
