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A Three-Party Repeated Coalition Formation Game for PLS in Wireless Communications with IRSs

Haipeng Zhou, Ruoyang Chen, Changyan Yi, Juan Li, Jun Cai

TL;DR

This work first analyzes the existence of Nash equilibrium (NE) in the formulated RCFG, and then proposes a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based algorithm for obtaining such equilibrium.

Abstract

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.

A Three-Party Repeated Coalition Formation Game for PLS in Wireless Communications with IRSs

TL;DR

This work first analyzes the existence of Nash equilibrium (NE) in the formulated RCFG, and then proposes a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based algorithm for obtaining such equilibrium.

Abstract

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.
Paper Structure (9 sections, 16 equations, 3 figures, 1 algorithm)

This paper contains 9 sections, 16 equations, 3 figures, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. System Model and Problem Formulation
  3. System Model
  4. Problem formulation
  5. Game Analyses And DRL-based Approach
  6. Formulation and Analysis on RCFG
  7. Switch Operations-based Coalition Selection with DRL-based Solution for RCFG $\mathcal{G}$
  8. Simulation Results
  9. Conclusion

Figures (3)

  • Figure 1: PLS-aware wireless communication system with IRSs.
  • Figure 2: Convergence of the proposed algorithm.
  • Figure 3: Performance comparison in terms of LPs', EV's and TIRSs' utilities.

Theorems & Definitions (2)

  • proof
  • proof