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

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 combine-qu...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 38; no. 8; pp. 1678 - 1697
Main Authors: Bashar, Manijeh, Akbari, Ali, Cumanan, Kanapathippillai, Ngo, Hien Quoc, Burr, Alister G., Xiao, Pei, Debbah, Merouane, Kittler, Josef
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Series:Lecture Notes in Computer Science
Subjects:
Artificial neural networks
Bussgang decomposition
cell-free massive MIMO
Central Processing Unit
Central processing units
Channel estimation
Computer Science
convex optimization
convolutional neural network
CPUs
Deep learning
Fading
Machine learning
Mapping
Massive MIMO
Maximization
MIMO (control systems)
Optimization
Power control
Uplink
ISSN:0733-8716, 1558-0008
Online Access:Get full text
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Summary: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 combine-quantize-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 large-scale-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.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.3000812