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<p>5G networks introduce network slicing to meet the diverse requirements of emerging applications, such as ultra-low latency and high reliability. However, efficient resource provisioning remains a major challenge due to dynamic traffic demands, limited resources, and stringent Quality of Service (QoS) constraints. Ensuring optimal resource allocation while balancing performance trade-offs is crucial for maintaining service differentiation and network efficiency. In this paper, we propose a linear programming model that assigns resources to network slices based on demand while optimizing QoS. Our approach considers key 5G service categories, including Ultra-Reliable Low-Latency Communications (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC), each with unique performance requirements. Simulation results demonstrate the model’s effectiveness in improving resource utilization and service quality, paving the way for more adaptive and intelligent network slicing strategies.</p>

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Optimizing 5G Network Slicing for Heterogeneous Services

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Reza Mohammadi

摘要

5G networks introduce network slicing to meet the diverse requirements of emerging applications, such as ultra-low latency and high reliability. However, efficient resource provisioning remains a major challenge due to dynamic traffic demands, limited resources, and stringent Quality of Service (QoS) constraints. Ensuring optimal resource allocation while balancing performance trade-offs is crucial for maintaining service differentiation and network efficiency. In this paper, we propose a linear programming model that assigns resources to network slices based on demand while optimizing QoS. Our approach considers key 5G service categories, including Ultra-Reliable Low-Latency Communications (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC), each with unique performance requirements. Simulation results demonstrate the model’s effectiveness in improving resource utilization and service quality, paving the way for more adaptive and intelligent network slicing strategies.