Year: 2021
Type: article
Abstract: 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.
Source: IEEE Communications Letters
Authors: Evgeny Bobrov, Dmitry Kropotov, Hao Lu, Danila Zaev
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Huawei Technologies
OpenAlex ID: W3209752572