Machine learning based dynamic resource sharing and frequency reuse in 5G hetnets with dronecells

Abstract

As 5G mobile communication systems evolve to the growing demands for network capacity and coverage, innovative solutions are required to address challenges such as interference and spectrum efficiency. The provision of temporary cellular network coverage by unmanned aerial vehicles (UAVs), which have made significant progress in recent years, provides great advantages in industries like telecommunications, public safety, and disaster recovery. Instead of using traditional base stations, UAVs, which we call flying base stations or Dronecells, can reduce interference and costs. Drones are strategically positioned at the center of user clusters, determined using the widely adopted k-means clustering algorithm, an unsupervised machine learning technique. Additionally, we use the TOPSIS method to ascertain users' priorities in resource allocation. The main challenge in this work lies in determining the optimal location and the appropriate number for the Dronecells. The article introduces a Benefit-Based Resource Allocation Algorithm (BRSA), designed for dynamic resource sharing in dense heterogeneous urban networks with Dronecells. This algorithm aims to enhance spectrum efficiency, optimize user fairness and minimize intercell interference. The number of Dronecells varies based on user density, allowing adaptability to different scenarios. Another objective is to identify the optimal cell center and cell edge areas by utilizing Reference Signal Received Power (RSRP) threshold values to maximize throughput for both cell center and cell edge users. Extensive simulations show that the proposed BRSA method significantly improves performance, increasing average cell edge user throughput by up to 25% while also enhancing fairness across the entire cell.