Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
基于元平均场Q学习的大规模无人机组网抗干扰与 隐蔽通信机制. (Chinese) |
| Alternate Title: |
Meta mean-field Q-learning-based anti-jamming and covert communication mechanism for large-scale UAV networks. (English) |
| Συγγραφείς: |
钱玉文, 吴浩阳, 时龙, 曹阳, 王喆, 陈光霁, 马川, 韦康 |
| Πηγή: |
Journal on Communication / Tongxin Xuebao; Jun2026, Vol. 47 Issue 6, p173-189, 17p |
| Θεματικοί όροι: |
Interference suppression, Drone aircraft, Spectrum analysis instruments, Reinforcement learning, Deep reinforcement learning, Machine-to-machine communications |
| Abstract (English): |
In dynamic and complex electromagnetic environments where malicious jammers and passive eavesdroppers co‐ exist, large-scale UAV networks impose increasing demand on anti-jamming capability and communication covertness. Based on this, a meta-reinforcement-learning-based anti-jamming covert communication mechanism for large-scale multi-UAV networks was proposed. Firstly, a time-slotted spectrum-sensing model was designed for the multi-UAV network to support the communication process of UAV networks under jamming and eavesdropping. Secondly, a mean-field Q-learning (MFQ) algorithm was designed to achieve distributed channel-access policy learning in large-scale UAV scenarios. Finally, a meta-MFQ (MMFQ) algorithm was proposed to enhance the generalization performance of the channel-access policy to different jamming and eavesdropping scenarios, thereby jointly optimizing anti-jamming performance and covertness. Simulation results demonstrate that, compared with baselines, the proposed algorithm achieves average improvements of 15.41% in normalized reward and 44.56% in convergence speed over large-scale UAV communication scenarios. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
在恶意干扰和被动监听者并存的动态复杂电磁环境中, 大规模无人机组网系统对抗干扰能力与通信隐蔽 性的需求日益凸显. 为此, 提出了一种基于元强化学习的大规模多无人机组网抗干扰隐蔽通信机制. 首先, 设 计一种面向多无人机频谱感知的时隙模型, 以支撑无人机组网在干扰与监听环境下的通信; 其次, 设计一种平 均场 Q 学习算法, 以实现大规模无人机场景下的分布式信道接入策略学习; 最后, 进一步构建基于元强化学习 的平均场 Q 学习算法, 使信道接入策略在不同干扰与监听场景下具备良好泛化性, 从而实现抗干扰性与隐蔽性 的协同优化. 仿真结果表明, 在大规模无人机通信场景下, 相较于传统算法, 所提算法的归一化奖励值平均提 升 15.41%, 收敛速度平均提升 44.56%. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |