University of Oulu

N. Rajapaksha, K. B. Shashika Manosha, N. Rajatheva and M. Latva-Aho, "Deep Learning-based Power Control for Cell-Free Massive MIMO Networks," ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1-7, doi: 10.1109/ICC42927.2021.9500734

Deep learning-based power control for cell-free massive MIMO networks

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Author: Rajapaksha, Nuwanthika1; Manosha, Shashika1; Rajatheva, Nandana1;
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021102151889
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-21
Description:

Abstract

A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Max-min rate optimization problem in a cell-free massive MIMO uplink setup is formulated, where user power allocations are optimized in order to maximize the minimum user rate. Instead of modeling the problem using mathematical optimization theory, and solving it with iterative algorithms, our proposed solution approach is using DL. Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate. This novel unsupervised learning-based approach does not require optimal power allocations to be known during model training as in previously used supervised learning techniques, hence it has a simpler and flexible model training stage. Numerical results show that the proposed DNN achieves a performance-complexity trade-off with around 400 times faster implementation and comparable performance to the optimization-based algorithm. An online learning stage is also introduced, which results in near-optimal performance with 4–6 times faster processing.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-7281-7122-7
ISBN Print: 978-1-7281-7123-4
Pages: 1 - 7
DOI: 10.1109/ICC42927.2021.9500734
OADOI: https://oadoi.org/10.1109/ICC42927.2021.9500734
Host publication: 2021 IEEE International Conference on Communications, ICC 2021
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported by the Academy of Finland 6Genesis Flagship (grant no. 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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