Reinforcement Learning-Based Secure Near-field Directional Modulation Enhanced by Rotatable RIS

TL;DR

This paper proposes a reinforcement learning-based approach to enhance secure near-field directional modulation using a rotatable RIS, addressing path loss issues and optimizing secrecy rate with novel algorithms.

Contribution

It introduces a new optimization framework and algorithms for RIS-assisted secure directional modulation, including a reinforcement learning scheme, to improve security performance.

Findings

Optimized RIS orientation yields 52.6% SR improvement.

Proposed algorithms outperform baseline methods.

Security gains are more significant with fewer antennas.

Abstract

This paper investigates secure Directional Modulation (DM) design enhanced by a rotatable active Reconfigurable Intelligent Surface (RIS). In conventional RIS-assisted DM networks, the security performance gain is limited due to the multiplicative path loss introduced by the RIS reflection path. To address this challenge, a Secrecy Rate (SR) maximization problem is formulated, subject to constraints including the eavesdropper's Direction Of Arrival (DOA) estimation performance, transmit power, rotatable range, and maximum reflection amplitude of the RIS elements. To solve this non-convex optimization problem, three algorithms are proposed: a multi-stream null-space projection and leakage-based method, an enhanced leakage-based method, and an optimization scheme based on the Distributed Soft Actor-Critic with Three refinements (DSAC-T). Simulation results validate the effectiveness of the proposed algorithms. A performance trade-off is observed between eavesdropper's DOA estimation accuracy and the achievable SR. The security enhancement provided by the RIS is more significant in systems equipped with a small number of antennas. By optimizing the orientation of the RIS, a 52.6\% improvement in SR performance can be achieved.