Search for: All records

The need for continuous coverage, as well as low-latency, and ultrareliable communication in 5G and beyond cellular networks encouraged the deployment of high-altitude platforms and low-altitude drones as flying base stations (FBSs) to provide last-mile communication where high cost or geographical restrictions hinder the installation of terrestrial base stations (BSs) or during the disasters where the BSs are damaged. The performance of unmanned aerial vehicle (UAV)-assisted cellular systems in terms of coverage and quality of service offered for terrestrial users depends on the number of deployed FBSs, their 3-D location as well as trajectory. While several recent works have studied the 3-D positioning in UAV-assisted 5G networks, the problem of jointly addressing coverage and user data rate has not been addressed yet. In this article, we propose a solution for joint 3-D positioning and trajectory planning of FBSs with the objectives of the total distance between users and FBSs and minimizing the sum of FBSs flight distance by developing a fuzzy candidate points selection method. 

In this paper, we delve into the domain of heterogeneous drone-enabled aerial base stations, each equipped with varying transmit powers, serving as downlink wireless providers for ground users. A central challenge lies in strategically selecting and deploying a subset from the available drone base stations (DBSs) to meet the downlink data rate requirements while minimizing the overall power consumption. To tackle this, we formulate an optimization problem to identify the optimal subset of DBSs, ensuring wireless coverage with an acceptable transmission rate in the downlink path. Moreover, we determine their 3D positions for power consumption optimization. Assuming DBSs operate within the same frequency band, we introduce an innovative, computationally efficient beamforming method to mitigate intercell interference in the downlink. We propose a Kalai–Smorodinsky bargaining solution to establish the optimal beamforming strategy, compensating for interference-related impairments. Our simulation results underscore the efficacy of our solution and offer valuable insights into the performance intricacies of heterogeneous drone-based small-cell networks.