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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 autopre-coder (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 multi-user 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 multi-user (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 multi-user 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 low-complexity 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.
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https://hal-cnam.archives-ouvertes.fr/hal-03761405
Contributor : Marie-Liesse Bertram Connect in order to contact the contributor
Submitted on : Friday, August 26, 2022 - 10:14:21 AM
Last modification on : Wednesday, September 28, 2022 - 5:58:50 AM

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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. 2022 IEEE Wireless Communications and Networking Conference (WCNC), Apr 2022, Austin, United States. pp.1039-1044, ⟨10.1109/WCNC51071.2022.9771695⟩. ⟨hal-03761405⟩

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