The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
조회수
141
본 논문에서는 높은 사전 지식을 요구하고 적시성이 약한 기존 알고리즘의 문제점을 해결하기 위해 심층 강화학습 기반의 비상 통신 네트워크 토폴로지 계획 방법을 제안합니다. 비상통신망의 특성을 바탕으로 체스를 그려 네트워크 계획에 있어서의 노드 레이아웃과 토폴로지 계획 문제를 체스 게임 문제에 매핑합니다. 네트워크 계획을 위한 평가 기준을 구성하기 위해 네트워크 범위와 연결성의 두 가지 요소가 고려됩니다. 네트워크 계획 샘플 데이터 생성을 구현하기 위해 몬테카를로 트리 탐색과 셀프 게임을 결합하는 방법을 사용하고 잔여 네트워크를 기반으로 한 네트워크 계획 전략 네트워크와 가치 네트워크 구조를 설계합니다. 이를 바탕으로 Tensorflow 라이브러리를 기반으로 모델을 구축하고 학습시켰습니다. 시뮬레이션 결과는 제안된 계획 방법이 네트워크 토폴로지의 지능적 계획을 효과적으로 구현할 수 있으며 적시성과 타당성이 우수하다는 것을 보여줍니다.
Changsheng YIN
National University of Defense Technology
Ruopeng YANG
National University of Defense Technology
Wei ZHU
National University of Defense Technology
Xiaofei ZOU
National University of Defense Technology
Junda ZHANG
Naval Aviation University
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부
Changsheng YIN, Ruopeng YANG, Wei ZHU, Xiaofei ZOU, Junda ZHANG, "Optimal Planning of Emergency Communication Network Using Deep Reinforcement Learning" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 1, pp. 20-26, January 2021, doi: 10.1587/transcom.2020EBP3061.
Abstract: Aiming at the problems of traditional algorithms that require high prior knowledge and weak timeliness, this paper proposes an emergency communication network topology planning method based on deep reinforcement learning. Based on the characteristics of the emergency communication network, and drawing on chess, we map the node layout and topology planning problems in the network planning to chess game problems; The two factors of network coverage and connectivity are considered to construct the evaluation criteria for network planning; The method of combining Monte Carlo tree search and self-game is used to realize network planning sample data generation, and the network planning strategy network and value network structure based on residual network are designed. On this basis, the model was constructed and trained based on Tensorflow library. Simulation results show that the proposed planning method can effectively implement intelligent planning of network topology, and has excellent timeliness and feasibility.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBP3061/_p
부
@ARTICLE{e104-b_1_20,
author={Changsheng YIN, Ruopeng YANG, Wei ZHU, Xiaofei ZOU, Junda ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Optimal Planning of Emergency Communication Network Using Deep Reinforcement Learning},
year={2021},
volume={E104-B},
number={1},
pages={20-26},
abstract={Aiming at the problems of traditional algorithms that require high prior knowledge and weak timeliness, this paper proposes an emergency communication network topology planning method based on deep reinforcement learning. Based on the characteristics of the emergency communication network, and drawing on chess, we map the node layout and topology planning problems in the network planning to chess game problems; The two factors of network coverage and connectivity are considered to construct the evaluation criteria for network planning; The method of combining Monte Carlo tree search and self-game is used to realize network planning sample data generation, and the network planning strategy network and value network structure based on residual network are designed. On this basis, the model was constructed and trained based on Tensorflow library. Simulation results show that the proposed planning method can effectively implement intelligent planning of network topology, and has excellent timeliness and feasibility.},
keywords={},
doi={10.1587/transcom.2020EBP3061},
ISSN={1745-1345},
month={January},}
부
TY - JOUR
TI - Optimal Planning of Emergency Communication Network Using Deep Reinforcement Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 20
EP - 26
AU - Changsheng YIN
AU - Ruopeng YANG
AU - Wei ZHU
AU - Xiaofei ZOU
AU - Junda ZHANG
PY - 2021
DO - 10.1587/transcom.2020EBP3061
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2021
AB - Aiming at the problems of traditional algorithms that require high prior knowledge and weak timeliness, this paper proposes an emergency communication network topology planning method based on deep reinforcement learning. Based on the characteristics of the emergency communication network, and drawing on chess, we map the node layout and topology planning problems in the network planning to chess game problems; The two factors of network coverage and connectivity are considered to construct the evaluation criteria for network planning; The method of combining Monte Carlo tree search and self-game is used to realize network planning sample data generation, and the network planning strategy network and value network structure based on residual network are designed. On this basis, the model was constructed and trained based on Tensorflow library. Simulation results show that the proposed planning method can effectively implement intelligent planning of network topology, and has excellent timeliness and feasibility.
ER -