Research on Intelligent Spectrum Allocation Techniques for Incomplete Electromagnetic Information

ZHAO Haoqin; DUAN Guodong; SI Jiangbo; HUANG Rui; SHI Jia

doi:10.11999/JEIT231005

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2694-2702

ZHAO Haoqin, DUAN Guodong, SI Jiangbo, HUANG Rui, SHI Jia. Research on Intelligent Spectrum Allocation Techniques for Incomplete Electromagnetic Information[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2694-2702. doi: 10.11999/JEIT231005

Citation:

ZHAO Haoqin, DUAN Guodong, SI Jiangbo, HUANG Rui, SHI Jia. Research on Intelligent Spectrum Allocation Techniques for Incomplete Electromagnetic Information[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2694-2702. doi: 10.11999/JEIT231005

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Research on Intelligent Spectrum Allocation Techniques for Incomplete Electromagnetic Information

doi: 10.11999/JEIT231005

1.
Institute of Communications Engineering, Xidian University, Xi’an 710071, China
2.
Southwest China Research Institute of Electronic Equipment, Chengdu 610036, China

Funds: The Electromagnetic Space Warfare and Applications Key Laboratory Foundation (JJ2021-001), The National Natural Science Foundation of China (62425103)

Received Date: 2023-09-15
Rev Recd Date: 2024-04-24

Available Online: 2024-05-15

Publish Date: 2024-07-29

Abstract

Abstract

To solve the problem of low spectrum utilization of multi-node autonomous frequency decision-making in the dynamic electromagnetic countermeasure environment, the research on intelligent cooperative spectrum allocation technology for in complete electromagnetic information is carried out, which improves spectrum utilization through multi-node intelligent collaboration. Firstly, the spectrum allocation problem is modelled as an optimization problem to maximize the frequency-using equipment, and secondly, a resource decision-making algorithm based on the multi-node cooperative diversion experience repetition mechanism (Cooperation- Deep double Q-network, Co-DDQN) is proposed. This algorithm evaluates the historical experience data based on the cooperative diversion function and is trained by a hierarchical experience pool, so that each agent can form a lightweight cooperative decision-making ability under self-observation, and solve the problem of inconsistency between the optimization direction of multi-node decision-making and the overall optimization goal under low-visibility conditions. Besides, a hybrid reward function based on confidence allocation is designed, and each node considers itself when the decision is made, which can reduce the emergence of lazy nodes, explore a better overall action strategy, and further improve the system efficiency. Simulation results show that when the number of nodes is 20, the number of accessible devices of the proposed algorithm outperforms the global greedy algorithm and the genetic algorithm, and the difference with the centralized spectrum allocation algorithm with complete information sharing is within 5%, which is more suitable for cooperative spectrum allocation of low-visibility nodes.
- Spectrum resource allocation,
- Deep reinforcement learning,
- Incomplete electromagnetic information,
- Cooperative triage mechanism

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References(16)

References

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