Deep learning-based power control for cell-free massive MIMO networks
Rajapaksha, Nuwanthika; Manosha, Shashika; Rajatheva, Nandana; Latva-aho, Matti (2021-08-06)
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
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2021102151889
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [31941]