Abstract

The increasing number of mobile users and data-reliant applications in a densely populated area has created the need for better mobile networks. Static and semi-dynamic load distribution methods are usually incapable of coping with the pace of urban mobility and the evolving demands of a network. This paper proposes a dynamic load-balancing approach to improve resource allocation within heterogeneous mobile networks utilizing user mobility patterns, real-time traffic data, adaptive handover methods, and user-guided handover escalation policies. Selective handover initiation and load-shifting decisions are made from a continual assessment of network load vis-a-vis user distribution. Simulation has shown improved results over traditional methods concerning network performance metrics such as latency, throughput, and quality of service, as well as the overall satisfaction level of the users. The framework is instrumental in alleviating congestion and increasing services' reliability in highly populated urban environments, thus solving problems of 5G and next-generation mobile networks.