Variability-Driven Iterative MLR-LP Approach to Resource Allocation in 5G Network Slicing: Minimizing Latency in Ride-Hailing Services

The advent of 5G networks has ushered in a new era of communication technology characterized by unprecedented speed, ultra-low latency, and higher reliability. However, intelligent and adaptive resource allocation in 5G network slicing is critical to meeting consistent sub-10 ms latency, a value that aligns with the performance benchmark of ultra-reliable low-latency communications such as ride-hailing. This study deployed a hybrid Multiple Linear Regression–Linear Programming (MLR-LP) framework for optimizing bandwidth, memory, and signal strength to achieve latency reduction. Real-time data were collected from ride-hailing sessions in 5G-covered areas of Benin City, Nigeria, capturing latency, bandwidth, memory, and signal strength. The MLR model established the predictive relationship between network resources and latency, achieving a strong R² value of 0.941. The regression equation was embedded as the objective function of an iterative LP model, which optimized bandwidth, memory, and signal strength allocations. The iteration process was guided by practical feasibility and variability analysis, particularly the unit step standard deviation, to progressively expand the bounds of resource variables in a controlled manner until feasible sub-10 ms latency was consistently obtained. The results demonstrate that the variability-driven iterative MLR-LP approach effectively minimizes latency to reliably support latency-sensitive services and enhancing 5G slicing performance. The study concludes that integrating predictive modeling with optimization techniques provides both theoretical and practical contributions, offering a possible solution for adaptive 5G resource management.