Robust Symbol-Level Precoding Via Autoencoder-Based Deep Learning

This paper proposes an autoencoder-based symbol-level precoding (SLP) scheme for a massive multiple-input multiple-output (MIMO) system operating in a limited-scattering environment. By recognizing that only imperfect channel state information (CSI) is available in practice, the goal of the proposed approach is to design the downlink SLP system robust to such imperfect CSI. Toward this goal, this paper leverages the concept of autoencoder wherein the end-to-end communications system is modeled by a deep neural network. By end-to-end training the proposed autoencoder, this paper shows that the downlink symbol-level precoder as well as the receivers’ decision rule can be jointly designed in ways that are robust to channel uncertainty. Moreover, this paper introduces a novel two-step training procedure to design a robust precoding scheme for conventional modulations such as quadrature amplitude modulation (QAM) and phase shift keying (PSK). Numerical results indicate that the proposed autoencoder-based framework, either trained by the end-to-end approach in which the receive constellation is a design variable or by the proposed two-step training approach with QAM constellation, can efficiently design a SLP scheme for massive MIMO system which is robust to channel uncertainty.