상세정보
학술지
Multi-Agent Deep Reinforcement Learning for Interference-Aware Channel Allocation in Non-Terrestrial Networks
- 저자
- 조연기, 양우열, 오대섭, 조한신
- 발행일
- 202303
- 출처
- IEEE Communications Letters, v.27 no.3, pp.936-940
- ISSN
- 1089-7798
- 출판사
- IEEE
- 협약과제
- 22HH3900, 비정지궤도 위성망 주파수 간섭 평가/공유 기술 개발, 오대섭
- 초록
- 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 제안 키워드
- 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