Simultaneous Secrecy and Covert Communications (SSACC) in Mobility-Aware RIS-Aided Networks

TL;DR

This paper introduces a novel SSACC scheme in RIS-aided networks that balances secrecy and covert communication performance using advanced optimization techniques.

Contribution

It develops a new algorithm combining GDM and DRL to optimize power allocation, enhancing security and data rates in mobile RIS networks.

Findings

The proposed algorithm outperforms DDPG in convergence speed.

Closed-form expressions for AMDEP and ASC are derived.

Simulation confirms improved security and capacity balance.

Abstract

In this paper, we propose a simultaneous secrecy and covert communications (SSACC) scheme in a reconfigurable intelligent surface (RIS)-aided network with a cooperative jammer. The scheme enhances communication security by maximizing the secrecy capacity and the detection error probability (DEP). Under a worst-case scenario for covert communications, we consider that the eavesdropper can optimally adjust the detection threshold to minimize the DEP. Accordingly, we derive closedform expressions for both average minimum DEP (AMDEP) and average secrecy capacity (ASC). To balance AMDEP and ASC, we propose a new performance metric and design an algorithm based on generative diffusion models (GDM) and deep reinforcement learning (DRL). The algorithm maximizes data rates under user mobility while ensuring high AMDEP and ASC by optimizing power allocation. Simulation results demonstrate that the proposed algorithm achieves faster convergence and superior performance compared to conventional deep deterministic policy gradient (DDPG) methods, thereby validating its effectiveness in balancing security and capacity performance.