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

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 termin...

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Published in:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 1039 - 1044
Main Authors: Cheng, Xinying, Zayani, Rafik, Ferecatu, Marin, Audebert, Nicolas
Format: Conference Proceeding
Language:English
Published: IEEE 10.04.2022
Subjects:
Artificial neural networks
autoprecoder
Channel estimation
Deep learning
Downlink
energy-efficiency
Fading channels
hardware impairment
Interference
massive multipleinput multiple-output (MIMO)
multi-user (MU) precoding
neural network (NN)
power amplifier (PA) nonlinearities
Precoding
ISSN:1558-2612
Online Access:Get full text
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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.
AbstractList 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.
Author Ferecatu, Marin
Audebert, Nicolas
Zayani, Rafik
Cheng, Xinying
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  organization: Conservatoire National des Arts et Métiers,CEDRIC (EA4329),Paris,France,75003
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Snippet This paper introduces a new efficient autopre-coder (AP) based deep learning approach for massive multiple-input multiple-output (mMIMO) downlink systems in...
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SubjectTerms Artificial neural networks
autoprecoder
Channel estimation
Deep learning
Downlink
energy-efficiency
Fading channels
hardware impairment
Interference
massive multipleinput multiple-output (MIMO)
multi-user (MU) precoding
neural network (NN)
power amplifier (PA) nonlinearities
Precoding
Title Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities
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