(untitled) (Comprehensive Journal of Science, 2025)
This paper presents a comprehensive investigation into optimizing Quality of Service in 5G networks through adaptive 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 the development and rigorous evaluation of dynamic resource allocation algorithms that intelligently and continuously adjust slice configurations in real time to satisfy the stringent and heterogeneous performance demands 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 not only maximizes network efficiency but also enhances user experience by effectively prioritizing critical URLLC traffic and robustly supporting vast mMTC device deployments. This research lays a foundational framework for next-generation 5G architectures, empowering service providers to deliver highly customized, superior-quality connectivity. By addressing the complex challenges posed by diverse service requirements, the work fundamentally advances flexible, reliable, and scalable 5G infrastructures, enabling transformative applications in industrial automation, smart cities, and the Internet of Things