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

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Document pages: 5 pages

Abstract: Massive multiuser multiple-input multiple-output (MU-MIMO) has been themainstream technology in fifth-generation wireless systems. To reduce highhardware costs and power consumption in massive MU-MIMO, low-resolutiondigital-to-analog converters (DAC) for each antenna and radio frequency (RF)chain in downlink transmission is used, which brings challenges for precodingdesign. To circumvent these obstacles, we develop a model-driven deep learning(DL) network for massive MU-MIMO with finite-alphabet precoding in thisarticle. The architecture of the network is specially designed by unfolding aniterative 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 fadingchannel.

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