Enhancing 5G Network Slicing Configurations for Ultra-Low Latency Road Safety Analytics

Abstract

The advent of 5G networks has ushered in a new era of \textit{ultra-reliable low-latency communications (URLLC)}, enabling transformative applications in road safety analytics. This paper focuses on enhancing network slicing configurations to meet the stringent latency and reliability requirements of road safety systems. Network slicing, a core feature of 5G, provides virtualized and isolated network segments tailored to specific application needs. However, existing slicing approaches often fail to optimize for dynamic, latency-critical scenarios inherent in road safety analytics, such as real-time hazard detection, collision avoidance, and vehicle-to-everything (V2X) communication. This study proposes an advanced framework for 5G network slicing optimization, leveraging machine learning-driven resource allocation, cross-layer orchestration, and adaptive quality of service (QoS) mechanisms. By analyzing the interplay of critical parameters such as resource block allocation, propagation delay, and scheduling algorithms, we demonstrate substantial improvements in latency, throughput, and reliability. Simulation results validate the efficacy of the proposed model, achieving latency reductions of up to 32% compared to baseline configurations while maintaining robust QoS guarantees under varying traffic loads. These findings underscore the potential of enhanced network slicing configurations to revolutionize road safety analytics, providing a foundation for safer, more efficient transportation ecosystems. Future research directions include exploring the integration of emerging 6G technologies and edge-intelligent architectures to further bolster real-time analytics capabilities.