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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| Vydáno v: | IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 1039 - 1044 |
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10.04.2022
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| ISSN: | 1558-2612 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Xinying surname: Cheng fullname: Cheng, Xinying organization: Conservatoire National des Arts et Métiers,CEDRIC (EA4329),Paris,France,75003 – sequence: 2 givenname: Rafik surname: Zayani fullname: Zayani, Rafik organization: Univ. Grenoble Alpes,CEA-Leti,Grenoble,France,F-38000 – sequence: 3 givenname: Marin surname: Ferecatu fullname: Ferecatu, Marin organization: Conservatoire National des Arts et Métiers,CEDRIC (EA4329),Paris,France,75003 – sequence: 4 givenname: Nicolas surname: Audebert fullname: Audebert, Nicolas 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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