Robust Symbol-Level Precoding Beyond CSI Models: A Probabilistic-Learning Based Approach

The use of large-scale antenna arrays poses great difficulties in obtaining perfect channel state information (CSI) in multi-antenna communication systems, which is essential for precoding optimization. To tackle this issue, in this paper we propose a probabilistic-learning based approach (PLA), aiming at alleviating the requirement of perfect CSI. The rationale is that the existing precoding algorithms that output a single precoder are often overconfident in their abilities and the obtained CSI. To avoid overconfidence, we incorporate the idea of regularization in machine learning (ML) into precoding models, so as to limit representative abilities of the precoding models. Compared to the state-of-the-art robust precoding designs, an important advantage of PLA is that CSI uncertainty models are not required. As a specific application of PLA, we design an efficient robust symbollevel hybrid precoding algorithm for the millimeter wave system and confirm the effectiveness of PLA via simulations.