Joint Routing and Resource Scheduling Algorithm for Large-scale Multi-mode Mesh Networks Based on Reinforcement Learning

ZHU Xiaorong; HE Chuhong

doi:10.11999/JEIT231103

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2773-2782

ZHU Xiaorong, HE Chuhong. Joint Routing and Resource Scheduling Algorithm for Large-scale Multi-mode Mesh Networks Based on Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2773-2782. doi: 10.11999/JEIT231103

Citation:

ZHU Xiaorong, HE Chuhong. Joint Routing and Resource Scheduling Algorithm for Large-scale Multi-mode Mesh Networks Based on Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2773-2782. doi: 10.11999/JEIT231103

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Joint Routing and Resource Scheduling Algorithm for Large-scale Multi-mode Mesh Networks Based on Reinforcement Learning

doi: 10.11999/JEIT231103

ZHU Xiaorong^,,
HE Chuhong

College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (92367102, 92067101), The Key R&D Plan of Jiangsu Province (BE2021013-3)

Received Date: 2023-10-10
Rev Recd Date: 2024-02-04

Available Online: 2024-02-26

Publish Date: 2024-07-29

Abstract

Abstract

In order to balance the transmission reliability and efficiency of large-scale multi-mode mesh networks in the new power system, a two-stage algorithm is proposed based on reinforcement learning for joint routing selection and resource scheduling in large-scale multi-mode mesh networks, building upon the description and analysis of optimization problems. In the first stage, based on the network topology information and service requirements, a multi shortest path routing algorithm is utilized to generate all the shortest paths. In the second stage, a resource scheduling algorithm based on Multi-Armed Bandit (MAB) is proposed. The algorithm constructs the arms of the MAB based on the obtained set of shortest paths, then calculates the reward according to the service demands, and finally gives the optimal route selection and resource scheduling mode for service transmission. Simulation results show that the proposed algorithm can meet different service transmission requirements, and achieve an efficient balance between the average end-to-end path delay and the average transmission success rate.
- Mesh networks,
- Routing selection,
- Resource scheduling,
- Multi-Armed Bandit (MAB),
- Reinforcement learning

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References(20)

References

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