AI-driven resources allocation strategies for enhanced quality of service in 5G networks
2025, vol.17 , no.2, pp. 77-88
Article [2025-02-08]
This study explores AI-driven strategies for resource allocation in 5G networks to optimize Quality of Service (QoS). Given the growing demand for high-speed, low-latency connectivity, efficient bandwidth distribution is critical. The research analyses a dataset containing various application types, signal strengths, latencies, and bandwidth requirements to assess the efficiency of current resource allocation mechanisms. The study examines the impact of dynamic network conditions on resource distribution and proposes AI-based solutions, including neural networks and reinforcement learning, to enhance decision-making. Experimental results show that Gradient Boosting reduces RMSE by 18% compared to traditional heuristic-based allocation, while reinforcement learning models exhibit superior adaptability. The proposed framework leverages machine learning to optimize bandwidth allocation, reduce congestion, and enhance user experience in next-generation mobile networks.
5G networks, resource allocation, artificial intelligence, machine learning, Quality of Service, bandwidth management, network optimization, reinforcement learning, neural networks, latency reduction
https://doi.org/10.59035/CUPU8017
Abdullah Havolli, Majlinda Fetaji. AI-driven resources allocation strategies for enhanced quality of service in 5G networks. International Journal on Information Technologies and Security, vol.17 , no.2, 2025, pp. 77-88. https://doi.org/10.59035/CUPU8017