Robust SAC-Enabled UAV-RIS Assisted Secure MISO Systems With Untrusted EH Receivers

TL;DR

This paper develops a robust SAC-based approach for optimizing secure UAV-assisted RIS-enabled MISO systems, addressing challenges like imperfect CSI, discrete RIS phases, and untrusted energy-harvesting receivers.

Contribution

It introduces a novel SAC framework for joint UAV placement, power control, and RIS configuration, outperforming traditional optimization and DRL methods under practical constraints.

Findings

SAC method outperforms DDPG and TD3 in simulations.

Proposed approach maintains robustness to CSI uncertainty.

Joint optimization improves secrecy energy efficiency.

Abstract

Secure downlink transmission in UAV-assisted reconfigurable intelligent surface (RIS)-enabled multiuser MISO systems is challenging due to imperfect channel state information (CSI), untrusted energy-harvesting receivers (UEHRs), and the strong coupling among UAV deployment, transmit power control, and RIS configuration. In this paper, we study a secure UAV-assisted RIS-enabled multiuser MISO system with UEHRs, where a hovering UAV-mounted RIS is jointly optimized in terms of its location, transmit power allocation, and discrete RIS phase shifts. The objective is to maximize the worst-case secrecy energy efficiency (WCSEE) under imperfect CSI and practical discrete phase-shift constraints. The resulting problem is highly nonconvex due to the fractional objective, coupled design variables, discrete phase shifts, and CSI uncertainty. To address these challenges, we propose two complementary approaches. First, a block coordinate descent (BCD) framework combined with successive convex approximation (SCA) is developed to solve a secrecy energy efficiency (SEE) formulation, serving as a structured model-based benchmark. Second, for the more general WCSEE problem, we propose a tailored soft actor-critic (SAC) framework that captures the coupling among variables and avoids repeated iterative optimization. Simulation results show that the proposed SAC method consistently outperforms conventional optimization and deep reinforcement learning (DRL)-based benchmarks, including deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), while maintaining robustness to CSI uncertainty and stable performance across system configurations.