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Journal Article Multi-Agent Deep Reinforcement Learning for Interference-Aware Channel Allocation in Non-Terrestrial Networks
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Authors
Yeongi Cho, Wooyeol Yang, Daesub Oh, Han-Shin Jo
Issue Date
2023-03
Citation
IEEE Communications Letters, v.27, no.3, pp.936-940
ISSN
1089-7798
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/LCOMM.2023.3237207
Project Code
22HH3900, Development of the spectrum utilizaiotn technology for non-geostationary satellite orbit system, Oh Dae Sub
Abstract
Non-terrestrial network (NTN) services using low-Earth-orbit (LEO) satellites are expanding. Interference management of NTN services with other terrestrial wireless services is emerging as a critical issue owing to the inherent international and vast coverage nature of NTN. This study develops a multi-agent deep reinforcement learning (DRL) framework to establish a multi-beam uplink channel allocation strategy that minimizes interference with incumbent stations under the given quality of service (QoS) constraints. We propose a novel framework with the sequential training of agents in a specific order to overcome the inherent non-stationarity of multi-agent DRL. To improve learning efficiency, we design the training sequence in accordance with reward function and initial state. As a result, taking actions in the order of the largest interference to the incumbent station provides superior performance to taking actions in an arbitrary order. Moreover, the proposed channel allocation performs close to the optimal exhaustive search and outperforms conventional greedy graph coloring method.
KSP Keywords
Arbitrary order, Channel Allocation, Deep reinforcement learning, Interference-aware, Learning efficiency, Low earth orbit(LEO), Multi-beam, Non-stationarity, Reinforcement Learning(RL), Terrestrial network, allocation strategy