Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm

The letter describes an online deep learning algorithm (ODL) for adaptive modulation and coding in massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then incrementally retrained by the service feedback...

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Bibliographic Details
Published in:IEEE communications letters Vol. 26; no. 4; pp. 818 - 822
Main Authors: Bobrov, Evgeny, Kropotov, Dmitry, Lu, Hao, Zaev, Danila
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Adaptive modulation and coding
Algorithms
Coding
Deep learning
Interference
link adaptation
Machine learning
Massive MIMO
Modulation
Neural networks
Olla
online training
reinforcement learning
Signal to noise ratio
Throughput
Training
wireless communications
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Summary:The letter describes an online deep learning algorithm (ODL) for adaptive modulation and coding in massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA, the algorithm shows a 10% to 20% improvement in user throughput in the full-buffer case.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2021.3132947