There is illegitimate transmission of information between suspicious users, whereas single legitimate monitor (LM) has finite capacity to satisfy eavesdropping. This letter studies a cooperative proactive eavesdropping(CPE) scheme, where two LMs eavesdrop on multiple suspicious links simultaneously with a cooperative jamming method under finite power constraint. Specifically, two LMs collaborate to emit jamming signals for the purpose of influencing the rate of suspicious links and facilitate successful eavesdropping at each LM. However, how to make jamming power decision over multiple suspicious links to maximize cumulative sum eavesdropping energy efficiency (EEE) in a long term for each LM under dynamic environment and continuous action space is a huge challenge. To solve the dynamic decision-making problem, we use multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the cooperative jamming problem. In simulation, the results show that our proposed CPE scheme can obtain the effective sum EEE compared with DDPG-based scheme and Random policy scheme.

中文翻译：

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基于深度强化学习的协作主动窃听


可疑用户之间存在非法信息传输，而单个合法监控器（LM）满足窃听的能力有限。这封信研究了一种协作主动窃听（CPE）方案，其中两个LM在有限功率约束下采用协作干扰方法同时窃听多个可疑链路。具体来说，两个 LM 协作发出干扰信号，以影响可疑链接的速率并促进成功窃听每个 LM。然而，如何在动态环境和连续行动空间下对多个可疑链路做出干扰功率决策，以最大化每个LM的长期累积和窃听能效（EEE）是一个巨大的挑战。为了解决动态决策问题，我们使用多智能体深度确定性策略梯度（MADDPG）算法来解决协作干扰问题。仿真结果表明，与基于DDPG的方案和随机策略方案相比，我们提出的CPE方案可以获得有效的EEE总和。