Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm

The paper 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 o...

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Bibliographic Details
Published inarXiv.org
Main Authors Bobrov, Evgeny, Kropotov, Dmitry, Lu, Hao, Zaev, Danila
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.09.2024
Subjects
Adaptive algorithms
Algorithms
Coding
Computer Science - Networking and Internet Architecture
Deep learning
Machine learning
Modulation
Neural networks
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Summary:The paper 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2105.12827