Optimizing Service Quality through Adaptive 5G Network Slice Management
DOI:
https://doi.org/10.65405/.v10i37.431Keywords:
5G Network Slicing, Adaptive Resource Allocation, Ultra-Reliable Low Latency Communications (URLLC), Massive Machine Type Communications (mMTC), Quality of Service (QoS).Abstract
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.
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