MARL-Based Sequential RIS Auctions: A Physical-Layer Security Analysis

2026-08-22

AI Summary

本文通过构建基于MARL的序列RIS拍卖框架，解决了合法接收者与窃听者竞争RIS资源的问题，提高了物理层安全性能。

Abstract

Reconfigurable intelligent surfaces (RISs) hold great potential to enhance coverage, spectral efficiency, and communication security by intelligently configuring their reflecting elements. When owned by a neutral RIS operator, these elements can be offered as resources for which legitimate receivers and eavesdroppers compete. This paper investigates such competition and evaluates its impact on the physical-layer security performance of legitimate receivers. To model the competition, we develop a sequential RIS auction (SRA) framework, in which a bundle of RIS elements is auctioned in each round through a first-price sealed-bid mechanism, with each bidder submitting its bid based on the achievable rate gain and remaining budget. We then formulate the sequential bidding process as a Markov game by specifying its states, actions, rewards, and state transitions. To solve the game, we propose a multi-bidder deep deterministic policy gradient (MADDPG)-based multi-bidder reinforcement learning (MARL) approach under centralized training and decentralized execution (CTDE), enabling legitimate receivers and eavesdroppers to learn bidding strategies that maximize their long-term economic surplus. Numerical results show that, under the considered eavesdropper bidding strategies, the RL-based strategy enables legitimate receivers to achieve the highest secrecy rate per unit cost, outperforming random and fixed strategies and approaching the ideal physical-layer upper bound.

Problem

• Reconfigurable Intelligent Surfaces
• Physical-layer Security
• Sequential Auctions
• MARL

Innovation

• Sequential RIS Auctions
• Multi-Agent Reinforcement Learning
• Physical-Layer Security
• Markov Game
• Deep Deterministic Policy Gradient