This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO) communication systems. Especially, it is shown that by training the transmitter and receiver jointly, the transmitter can learn such constellation shapes for the spatial streams which facilitate completely blind separation and detection by the simultaneously learned receiver. To the best of our knowledge, this is the first time ML-based spatial multiplexing without channel estimation pilots is demonstrated. The results show that the learned pilotless scheme can outperform a conventional pilot-based system by as much as 15-20% in terms of spectral efficiency, depending on the modulation order and signal-to-noise ratio.

翻译：本文研究了基于机器学习的多输入多输出（MIMO）通信系统中无导频空间复用的可行性。特别地，研究表明，通过联合训练发射机和接收机，发射机能够学习到适用于空间流的星座形状，从而使同时训练的接收机实现完全盲分离与检测。据我们所知，这是首次展示无需信道估计导频的基于机器学习的空间复用技术。结果表明，根据调制阶数和信噪比的不同，所学习的无导频方案在频谱效率上可比传统基于导频的系统提升15%-20%。