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

Autor: Xinying Cheng, Rafik Zayani, Marin Ferecatu, Nicolas Audebert
Přispěvatelé: Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), CEDRIC. Traitement du signal et architectures électroniques (CEDRIC - LAETITIA), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), CEDRIC. Données complexes, apprentissage et représentations (CEDRIC - VERTIGO)
Rok vydání: 2022
Předmět:
FOS: Computer and information sciences
Computer Science - Artificial Intelligence
energy-efficiency
neural network (NN)
Channel estimation
Fading channels
power amplifier (PA) nonlinearities
[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Computer Science - Networking and Internet Architecture
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
multi-user (MU) precoding
massive multipleinput multiple-output (MIMO)
[INFO]Computer Science [cs]
Neural and Evolutionary Computing (cs.NE)
hardware impairment
Networking and Internet Architecture (cs.NI)
Artificial neural networks
Downlink
ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS
Computer Science - Neural and Evolutionary Computing
Precoding
deep learning
Artificial Intelligence (cs.AI)
Interference
autoprecoder
Zdroj: IEEE Wireless Communications and Networking Conference
IEEE Wireless Communications and Networking Conference, Apr 2022, Austin, United States
2022 IEEE Wireless Communications and Networking Conference (WCNC)
2022 IEEE Wireless Communications and Networking Conference (WCNC), Apr 2022, Austin, United States. pp.1039-1044, ⟨10.1109/WCNC51071.2022.9771695⟩
DOI: 10.48550/arxiv.2202.03190
Popis: International audience; 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)
Databáze: OpenAIRE
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