Computational Optimization of RIS-Enhanced Backscatter and Direct Communication for 6G IoT: A DDPG-Based Approach with Physical Layer Security

The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet of Things (IoT) applications, particularly in terms of ultra-reliable, secure, and energy-efficient communication. This study explores the integration of Reconfigurable Intelligent Surfaces (RIS) into IoT networks to enhance communication performance. Unlike traditional passive reflector-based approaches, RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes, addressing critical IoT challenges such as energy efficiency, limited communication range, and double-fading effects in backscatter communication. We propose a novel computational framework that combines RIS functionality with Physical Layer Security (PLS) mechanisms, optimized through the algorithm known as Deep Deterministic Policy Gradient (DDPG). This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges, including imperfect channel state information (CSI) and hardware limitations like quantized RIS phase shifts. By optimizing both RIS settings and beamforming in real-time, our approach outperforms traditional methods by significantly increasing secrecy rates, improving spectral efficiency, and enhancing energy efficiency. Notably, this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques, providing scalable solutions for large-scale RIS deployments. Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure, efficient and scalable 6G communication. This work offers valuable insights for the future of IoT networks, with a focus on computational optimization, high spectral efficiency and energy-aware operations.