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Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities

Abstract : This paper introduces a new efficient autoprecoder (AP) based deep learning approach for massive multiple-input multiple-output (mMIMO) downlink systems in which the base station is equipped with a large number of antennas with energy-efficient power amplifiers (PAs) and serves multiple user terminals. We present AP-mMIMO, a new method that jointly eliminates the multiuser interference and compensates the severe nonlinear (NL) PA distortions. Unlike previous works, AP-mMIMO has a low computational complexity, making it suitable for a global energy-efficient system. Specifically, we aim to design the PA-aware precoder and the receive decoder by leveraging the concept of autoprecoder, whereas the end-to-end massive multiuser (MU)-MIMO downlink is designed using a deep neural network (NN). Most importantly, the proposed AP-mMIMO is suited for the varying block fading channel scenario. To deal with such scenarios, we consider a two-stage precoding scheme: 1) a NN-precoder is used to address the PA non-linearities and 2) a linear precoder is used to suppress the multiuser interference. The NN-precoder and the receive decoder are trained off-line and when the channel varies, only the linear precoder changes on-line. This latter is designed by using the widely used zero-forcing precoding scheme or its lowcomplexity version based on matrix polynomials. Numerical simulations show that the proposed AP-mMIMO approach achieves competitive performance with a significantly lower complexity compared to existing literature. Index Terms-multiuser (MU) precoding, massive multipleinput multiple-output (MIMO), energy-efficiency, hardware impairment, power amplifier (PA) nonlinearities, autoprecoder, deep learning, neural network (NN)
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https://hal.archives-ouvertes.fr/hal-03551457
Contributor : Nicolas Audebert Connect in order to contact the contributor
Submitted on : Wednesday, February 2, 2022 - 3:37:20 PM
Last modification on : Monday, February 21, 2022 - 3:38:20 PM

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  • HAL Id : hal-03551457, version 1
  • ARXIV : 2202.03190

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Xinying Cheng, Rafik Zayani, Marin Ferecatu, Nicolas Audebert. Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities. IEEE Wireless Communications and Networking Conference, Apr 2022, Austin, United States. ⟨hal-03551457⟩

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