ADAPTIVE EXPERT SYSTEM FOR DYNAMIC RESOURCE ALLOCATION IN 5G NETWORKS

This paper investigates the problem of efficient radio resource management and allocation in fifth-generation (5G) mobile communication networks. An adaptive expert-system-based radio resource allocation algorithm is proposed, taking into account critical network parameters such as network load, signal quality, user density, traffic type, queue length, and latency requirements. The proposed approach consists of a knowledge base, rule engine, network monitoring module, and decision-making module, enabling dynamic resource management in real time. A mathematical model for calculating user priority coefficients is developed, and the proposed method is compared with conventional scheduling techniques, including Round Robin, Proportional Fair, and Max C/I algorithms. The results indicate that the proposed expert-system-based approach has the potential to increase network throughput, reduce latency, improve Quality of Service (QoS), and enhance spectral efficiency. The proposed solution also provides intelligent and explainable decision-making while maintaining relatively low computational complexity, making it suitable for practical deployment in dynamic 5G environments.