Learning Robust Beamforming for MISO Downlink Systems

TL;DR

This paper proposes a deep learning-based robust beamforming method for MISO downlink systems that effectively handles imperfect CSI, improving transmission strategies in real-world environments.

Contribution

It introduces a novel robust training algorithm for DNNs to optimize beamforming using only imperfect CSI and statistical knowledge, enhancing practical system performance.

Findings

The proposed method outperforms traditional schemes in simulations.

Deep neural networks can effectively learn robust beamforming strategies.

The approach is practical with imperfect channel information.

Abstract

This paper investigates a learning solution for robust beamforming optimization in downlink multi-user systems. A base station (BS) identifies efficient multi-antenna transmission strategies only with imperfect channel state information (CSI) and its stochastic features. To this end, we propose a robust training algorithm where a deep neural network (DNN), which only accepts estimates and statistical knowledge of the perfect CSI, is optimized to fit to real-world propagation environment. Consequently, the trained DNN can provide efficient robust beamforming solutions based only on imperfect observations of the actual CSI. Numerical results validate the advantages of the proposed learning approach compared to conventional schemes.