Smart Shield: Prevent Aerial Eavesdropping via Cooperative Intelligent Jamming Based on Multi-Agent Reinforcement Learning
Wang, Qubeijian Tang, Shiyue Sun, Wen Zhang, Yin Sun, Geng Dai, Hong-Ning Guizani, Mohsen
Wang, Qubeijian
Tang, Shiyue
Sun, Wen
Zhang, Yin
Sun, Geng
Dai, Hong-Ning
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
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Language
English
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Abstract
The spotlight on autonomous aerial vehicles (AAVs) is to enhance wireless communications while ignoring the potential risk of AAVs acting as adversaries. Due to their mobility and flexibility, AAV eavesdroppers pose an immeasurable threat to legitimate wireless transmissions. However, the existing fixed jamming scheme without cooperation cannot counter the flexible and dynamic AAV eavesdropping. In this article, a cooperative intelligent jamming scheme is proposed, authorizing ground jammers (GJs) to interfere with AAV eavesdroppers, generating specific jamming shields between AAV eavesdroppers and legitimate users. Toward this end, we formulate a secrecy capacity maximization problem and model the problem as a decentralized partially observable Markov decision process (Dec-POMDP). To address the challenge of the huge state space and action space with network dynamics, we leverage a deep reinforcement learning (DRL) algorithm with a dueling network and double-Q learning (i.e., dueling double deep Q-network) to train policy networks. Then, we propose a multi-agent mixing network framework (QMIX)-based collaborative jamming algorithm to enable GJs to independently make decisions without sharing local information. Additionally, we perform extensive simulations to validate the superiority of our proposed scheme and present useful insights into practical implementation by elucidating the relationship between the deployment settings of GJs and the instantaneous secrecy capacity.
Citation
Q. Wang et al., “Smart Shield: Prevent Aerial Eavesdropping Via Cooperative Intelligent Jamming Based on Multi-Agent Reinforcement Learning,” IEEE Trans Mob Comput, 2024, doi: 10.1109/TMC.2024.3505206.
Source
IEEE Transactions on Mobile Computing
Conference
Keywords
AAV, Anti-eavesdropping, collaborative jamming, MARL, power allocation, trajectory design
Subjects
Source
Publisher
IEEE