Limited-Fronthaul Cell-Free Hybrid Beamforming with Distributed Deep Neural Network

• URL: http://arxiv.org/abs/2106.16194v1
• Date: Wed, 30 Jun 2021 16:42:32 GMT
• Title: Limited-Fronthaul Cell-Free Hybrid Beamforming with Distributed Deep Neural Network
• Authors: Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon, and Francois Leduc-Primeau
• Abstract summary: Near-optimal solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC) We propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform coordinated hybrid beamforming with zero or limited communication overhead between APs and NC.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In addition, the use of hybrid beamforming in each AP reduces the number of power hungry RF chains, but imposes a large computational complexity to find near-optimal precoders. In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform coordinated hybrid beamforming with zero or limited communication overhead between APs and NC, while achieving near-optimal sum-rate with a reduced computational complexity compared to conventional near-optimal solutions.

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