{'fraction_non_alphanumeric': 0.030124908155767818, 'fraction_numerical': 0.0, 'mean_word_length': 5.276497695852535, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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 down-link 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.