Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to secure wireless communications through the exploitation of the physical properties of the wireless channel. Cooperative Friendly Jamming (CFJ) is among the PLS techniques that have received attention in recent years. However, finding an optimal transmit power allocation that results in the highest secrecy is a complex problem that becomes more difficult to address as the size of the wireless network increases. In this paper, we propose an optimization approach to achieve CFJ in large Wi-Fi networks by using a Reinforcement Learning Algorithm. Obtained results show that our optimization approach offers better secrecy results and becomes more effective as the network size and the density of Wi-Fi access points increase.

翻译：无线数据通信始终面临窃听与截获的风险。基于加密的传统防护方案在无线物联网等场景中可能难以实际部署，或将在量子计算机面前迅速失效。物理层安全（Physical Layer Security, PLS）通过利用无线信道的物理特性，为保障无线通信安全提供了极具前景的途径。协同友好干扰（Cooperative Friendly Jamming, CFJ）是近年来备受关注的PLS技术之一。然而，寻找能实现最高保密性的最优发射功率分配是一个复杂问题，且随着无线网络规模扩大，解决难度急剧上升。本文提出一种基于强化学习算法的优化方法，用于实现大规模Wi-Fi网络中的CFJ。实验结果表明，该方法能够获得更优的保密性能，且随着网络规模及Wi-Fi接入点密度的增加，其有效性显著提升。