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Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink

Bashar, Manijeh, Akbari, Ali, Cumanan, Kanapathippillai, Quoc Ngo, Hien, Burr, Alister G., Xiao, Pei, Debbah, Merouane and Kittler, Josef (2020) Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink IEEE Journal on Selected Areas in Communications.

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Abstract

A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combinequantize- and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing largescale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Bashar, Manijehm.bashar@surrey.ac.uk
Akbari, Aliali.akbari@surrey.ac.uk
Cumanan, Kanapathippillai
Quoc Ngo, Hien
Burr, Alister G.
Xiao, PeiP.Xiao@surrey.ac.uk
Debbah, Merouane
Kittler, JosefJ.Kittler@surrey.ac.uk
Date : 19 February 2020
Uncontrolled Keywords : Bussgang decomposition, cell-free massive MIMO, convex optimization, convolutional neural network, deep learning.
Depositing User : James Marshall
Date Deposited : 10 Apr 2020 13:53
Last Modified : 10 Apr 2020 13:53
URI: http://epubs.surrey.ac.uk/id/eprint/854141

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