Performance Optimization of 5G Networks for Ultra-Reliable Low-Latency Communication

Abstract

The advent of 5G networks introduces transformative capabilities in wireless communication, particularly addressing the demanding requirements of Ultra-Reliable Low-Latency Communication (URLLC). URLLC is critical for mission-critical applications such as autonomous driving, remote surgery, industrial automation, and smart grids, where latency must be minimized and reliability maximized. This paper investigates performance optimization techniques to meet URLLC standards within 5G infrastructure. Key challenges include reducing end-toend latency to under 1 millisecond, ensuring high reliability (99.999%), and managing network resources efficiently under dynamic traffic conditions. We present an in-depth analysis of advanced radio access network (RAN) enhancements, edge computing integration, network slicing, and intelligent resource allocation algorithms. A simulation-based methodology evaluates the impact of scheduling schemes, transmission time interval (TTI) shortening, and hybrid automatic repeat request (HARQ) improvements on latency and reliability metrics. The results demonstrate that employing a combination of dynamic scheduling with proactive resource reservation and multiconnectivity can significantly reduce latency and improve packet delivery ratios. Further, network slicing allows for dedicated resources to URLLC traffic, isolating it from other traffic types and guaranteeing performance. Edge computing reduces latency by offloading processing closer to end-users. We conclude that an integrated optimization framework combining these techniques offers the best approach to achieving the stringent URLLC requirements. Future work will focus on implementing machine learning models for adaptive resource management to further enhance network responsiveness. This study contributes to the ongoing evolution of 5G networks by providing practical insights and solutions for real-world URLLC deployments.