Optimizing Service Quality through Adaptive 5G Network Slice Management (Comprehensive Journal of Science, 2025)
This paper presents a comprehensive investigation into optimizing Quality of Service in 5G networks throughadaptive network slice management, with a concentrated focus on Ultra-Reliable Low Latency Communications(URLLC) and Massive Machine-Type Communications (mMTC), where the core contribution lies in thedevelopment and rigorous evaluation of dynamic resource allocation algorithms that intelligently andcontinuously adjust slice configurations in real time to satisfy the stringent and heterogeneous performancedemands of diverse applications. Leveraging advanced mathematical modeling, simulation experiments, and                 state-of-the-art machine learning techniques, the study achieves significant improvements in latency, throughput,and less packet loss over conventional static allocation strategies. Results demonstrate that adaptive slicing notonly maximizes network efficiency but also enhances user experience by effectively prioritizing critical URLLCtraffic and robustly supporting vast mMTC device deployments. This research lays a foundational framework fornext-generation 5G architectures, empowering service providers to deliver highly customized, superior-qualityconnectivity. By addressing the complex challenges posed by diverse service requirements, the workfundamentally advances flexible, reliable, and scalable 5G infrastructures, enabling transformative applicationsin industrial automation, smart cities, and the Internet of Things.
Keywords: 5G Network Slicing, Adaptive Resource Allocation, Ultra-Reliable Low Latency Communications (URLLC), Massive Machine Type Communications (mMTC), Quality of Service (QoS).