Active RIS-Assisted URLLC NOMA-Based 5G Network with FBL under Jamming Attacks

TL;DR

This paper proposes an active RIS-assisted NOMA-based 5G network framework to improve reliability, energy efficiency, and latency under jamming attacks in URLLC scenarios with finite blocklength constraints.

Contribution

It introduces an active RIS design tailored for URLLC in NOMA 5G networks, analyzing the impact of RIS elements, blocklength, and traffic load on performance under jamming.

Findings

Increasing RIS elements from 4 to 400 improves SJNR by 13.64%.

Optimizing blocklength and packet rate enhances energy efficiency by 31.68%.

The approach effectively mitigates jamming effects in URLLC NOMA networks.

Abstract

In this paper, we tackle the challenge of jamming attacks in Ultra-Reliable Low Latency Communication (URLLC) within Non-Orthogonal Multiple Access (NOMA)-based 5G networks under Finite Blocklength (FBL) conditions. We introduce an innovative approach that employs Reconfigurable Intelligent Surfaces (RIS) with active elements to enhance energy efficiency while ensuring reliability and meeting latency requirements. Our approach incorporates the traffic model, making it practical for real-world scenarios with dynamic traffic loads. We thoroughly analyze the impact of blocklength and packet arrival rate on network performance metrics and investigate the optimal amplitude value and number of RIS elements. Our results indicate that increasing the number of RIS elements from 4 to 400 can improve signal-to-jamming-plus-noise ratio (SJNR) by 13.64\%. Additionally, optimizing blocklength and packet arrival rate can achieve a 31.68% improvement in energy efficiency and reduced latency. These findings underscore the importance of optimized settings for effective jamming mitigation.