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arXiv · 2006.15495

Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding

Hengtao He·Mengjiao Zhang·Shi Jin·Chao-Kai Wen·Geoffrey Ye Li
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Abstract

Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges for precoding design. To circumvent these obstacles, we develop a model-driven deep learning (DL) network for massive MU-MIMO with finite-alphabet precoding in this article. The architecture of the network is specially designed by unfolding an iterative algorithm. Compared with the traditional state-of-the-art techniques, the proposed DL-based precoder shows significant advantages in performance, complexity, and robustness to channel estimation error under Rayleigh fading channel.

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Hengtao He, Mengjiao Zhang, Shi Jin, Chao-Kai Wen, Geoffrey Ye Li. 2020-06-28. Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding. https://arxiv.org/abs/2006.15495

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