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동적 저궤도 위성 네트워크에서 온보드 강화학습 기반 라우팅을 위한 이종 프로세서 기반 추론 병렬화 기술
김도형(Dohyung Kim),이민준(Minjoon Lee),이헌철(Heoncheol Lee),원동식(Dongshik Won),한명훈(Myoung-Hun Han) 한국정보기술학회 2023 한국정보기술학회논문지 Vol.21 No.7
This paper addresses the routing problem in dynamic low-orbit(LEO) satellite networks for on-board computer (OBC). Deep reinforcement learning can be applied for routing in networks with dynamic connectivity between LEO satellites. However, it is difficult to apply the inference process with deep reinforcement learning models to real-time OBCs because it causes excessive execution time due to the calculation of multiple convolution layers. To solve the problem, we propose a practical method based on heterogeneous processors which can reduce the execution time by parallelizing the inference process, which is performed sequentially in a Central Processing Unit. The performance of the proposed method was evaluated using an actual OBC based on heterogeneous processors, and the routing result was the same as that of the existing method, but the overall execution time was significantly reduced.