Active RIS-NOMA Uplink in URLLC, Jamming Mitigation via Surrogate and Deep Learning

The integration of Non-Orthogonal Multiple Access (NOMA) and Reconfigurable Intelligent Surfaces (RIS) significantly enhances 5G across a variety of technologies such as the Internet of Things (IoT), smart cities, and industrial automation. This work explores an active RIS-assisted NOMA uplink system aimed at mitigating jamming attacks while ensuring the reliability and latency requirements of ultra-reliable low-latency communication (URLLC) applications. We investigate the potential of RIS with active elements that adjust the phase and amplitude of the received signals for robust jamming mitigation. The study incorporates finite blocklength (FBL) and Automatic Repeat Request (ARQ) strategies to handle real-world complex configurations effectively. A thorough examination of various network parameters is conducted, including user transmit powers, active RIS elements amplitude, and the number of RIS elements. The paper utilizes the surrogate optimization technique, particularly the Radial Basis Function (RBF), to address the non-convex optimization problem minimizing the power consumption. The complexity of the optimization problem, involving numerous interacting variables, leads us to develop a deep regression model to predict optimal network configurations, providing a computationally efficient approach as well as reducing the signaling overhead. The findings emphasize the delicate balance required in optimizing network parameters. For instance, increasing the blocklength from 100 to 150 increases the reliability feasibility by 12.19%. The results demonstrate an optimal range for the amplitude value of active RIS elements $(2\lt \beta \lt 15)$ . Exceeding this range results in over-amplification, high latency, and lower reliability, due to the interference related to NOMA cluster users. The deep regression model converges to a weighted mean square error (WMSE) of 10.6 for RIS with 25 elements and 15.8 for larger RIS size, highlighting the effectiveness of the deep regression model and RIS configuration’s importance.