Quantization-Aware Learning for Secure and Resilient UAV-STAR-RIS-Enabled 6G Systems

Abstract

Secure and reliable wireless communication is a major requirement for upcoming 6G mission-critical applications, such as emergency response and public safety networks. In this respect, unmanned aerial vehicle (UAV)-mounted, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems are expected to play an important role in enabling secure, mission-critical 6G communications. However, practical implementations require discrete phase shifts, which can introduce performance degradation due to phase quantization effects. In this paper, we investigate phase quantization in a UAV-mounted STAR-RIS-assisted downlink non-orthogonal multiple access (NOMA) network to maximize secrecy rate. Two approaches are considered, namely, post-training quantization (PTQ) and quantization-aware training (QAT). A Twin Delayed Deep Deterministic Policy Gradient (TD3) agent is developed to jointly optimize STAR-RIS phase shifts and UAV positioning while enforcing minimum quality-of-service (QoS) requirements for users. Simulation results show that QAT consistently outperforms PTQ, particularly at low resolutions. While PTQ suffers significant performance degradation under coarse quantization, QAT maintains near-continuous performance at 3-bit resolution with less than 1% secrecy rate loss, and significantly reduces degradation at 2 bits. These results demonstrate that QAT enables efficient low-bit STAR-RIS operation, reducing hardware complexity and signaling overhead while preserving secrecy performance, thereby enhancing the practical viability of the proposed framework.