期刊
IEEE COMMUNICATIONS LETTERS
卷 26, 期 8, 页码 1809-1813出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2022.3179235
关键词
Throughput; Relays; Mobile ad hoc networks; Training; Mobile nodes; Costs; Power demand; Curriculum reinforcement learning; mobile ad hoc networks; mobile nodes; network formation; self-organizing networks
资金
- Institute of Information & communications Technology Planning & Evaluation (IITP) - Korea government (MSIT) [2021-0-00739]
- ITRC (Information Technology Research Center) support program [IITP-2021-2020-0-01602]
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2020R1F1A1069182, NRF2020R1A2B5B01002528]
- LG Yonam Foundation of Korea
- National Research Foundation of Korea [2020R1F1A1069182] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This letter proposes a distributed solution for building mobile ad hoc networks using reinforcement learning. The mobile nodes autonomously determine their positions and form a cohesive team to maximize network throughput. By designing a curriculum, nodes converge quickly and work in optimal locations, improving learning efficiency and team performance.
In emergency scenarios, such as disaster or military situations, ad hoc networks should be deployed as no central coordination is available. In this letter, we propose a distributed solution for building mobile ad hoc networks, where the mobile nodes determine their positions as a team autonomously based on reinforcement learning. We propose a special design of a decentralized partially observable Markov decision process to build a cohesive team of mobile nodes in a distributed manner. Each mobile node in the team learns an individual policy that determines movement under partial observation, with the common goal of maximizing network throughput. In the learning process, each node indirectly negotiates the role in the team while explicitly considering the locations of other neighboring nodes and network throughput. To improve learning efficiency, we design a curriculum that encourages nodes to disperse initially but reside in specific regions eventually. Such a curriculum enables each node to be placed in its best location, thereby expediting the collective convergence of all nodes as a cohesive team. Simulation results confirm that the proposed solution can successfully build a cohesive team that maintains high network throughput with low power consumption.
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