Optimizing Resource Allocation in 5G Networks: An Integrated Approach with Mobile Edge Computing and Network Slicing

Abstract

In contemporary smart factory systems, the customization of services through advanced technologies such as Mobile Edge Computing (MEC) and Network Slicing is essential for managing diverse applications. These technologies enable the efficient segmentation and allocation of heterogeneous resources on a shared physical infrastructure, focusing primarily on supporting low-energy services. This paper introduces the Factory Resource Optimization (FRO) framework, a novel approach that leverages MEC, Network Slicing, and a modified Non-Orthogonal Multiple Access (NOMA) strategy called Layered Multiple Access (LMA) to optimize resource allocation in 5G networks within smart factories. Unlike previous approaches that often focus on either energy efficiency or latency optimization, FRO tackles both simultaneously by formulating a non-convex combinatorial optimization problem. The solution involves an iterative algorithm based on a decomposition methodology, a significant departure from traditional optimization techniques. This approach allows for the efficient allocation of resources while minimizing the energy consumption of Equipment Monitoring Units (EMUs) and guaranteeing reliable and low-latency communication for Factory Safety Detectors (FSDs). Simulation results demonstrate substantial energy savings for EMUs and adherence to FSD latency requirements, highlighting FRO's potential to enhance smart factory operations compared to existing methods.