In this paper, a repeated coalition formation game (RCFG) with dynamic decision-making for physical layer security (PLS) in wireless communications with intelligent reflecting surfaces (IRSs) has been investigated. In the considered system, one central legitimate transmitter (LT) aims to transmit secret signals to a group of legitimate receivers (LRs) under the threat of a proactive eavesdropper (EV), while there exist a number of third-party IRSs (TIRSs) which can choose to form a coalition with either legitimate pairs (LPs) or the EV to improve their respective performances in exchange for potential benefits (e.g., payments). Unlike existing works that commonly restricted to friendly IRSs or malicious IRSs only, we study the complicated dynamic ally-adversary relationships among LPs, EV and TIRSs, under unpredictable wireless channel conditions, and introduce a RCFG to model their long-term strategic interactions. Particularly, we first analyze the existence of Nash equilibrium (NE) in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based algorithm for obtaining such equilibrium. Simulations examine the feasibility of the proposed algorithm and show its superiority over counterparts.

翻译：本文研究了面向含智能反射面的无线通信中物理层安全的动态决策重复联盟形成博弈。在所考虑系统中，一个中心合法发射器在主动窃听者的威胁下，试图向一组合法接收器传输秘密信号；同时存在多个第三方智能反射面，这些反射面可选择与合法对或窃听者组建联盟，以改善各自性能并换取潜在收益（如报酬）。与现有通常仅限于友好型或恶意型智能反射面的研究不同，本文研究了在不可预测无线信道条件下合法对、窃听者及第三方智能反射面间复杂的动态敌友关系，并引入重复联盟形成博弈对其长期策略交互进行建模。特别地，首先分析了所建重复联盟形成博弈中纳什均衡的存在性，进而提出一种基于切换操作的联盟选择算法，并辅以深度强化学习算法获取该均衡。仿真验证了所提算法的可行性，并展示了其相对于对比算法的优越性。