2021/05/01 by Muhammad Karam Shehzad, Muhammad K. Shehzad, Arsalan Ahmad +6
Engineering · #FOS: Electrical engineering #Satellite Communication Systems #Signal Processing (eess.SP) #Telecommunications and Broadcasting Technologies #UAV Applications and Optimization #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.00286
14 pages, 9 figures, 2 tables, IEEE Transactions on Network Science and Engineering, 2021
arxiv created 2021/05/01 · openalex publication_date 2021/05/01 · arxiv updated 2021/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The mushroom growth of cellular users requires novel advancements in the existing cellular infrastructure. One way to handle such a tremendous increase is to densely deploy terrestrial small-cell base stations (TSBSs) with careful management of smart backhaul/fronthaul networks. Nevertheless, terrestrial backhaul hubs significantly suffer from the dense fading environment and are difficult to install in a typical urban environment. Therefore, this paper considers the idea of replacing terrestrial backhaul network with an aerial network consisting of unmanned aerial vehicles (UAVs) to provide the fronthaul connectivity between the TSBSs and the ground core-network (GCN). To this end, we focus on the joint positioning of UAVs and the association of TSBSs such that the sum-rate of the overall system is maximized. In particular, the association problem of TSBSs with UAVs is formulated under communication-related constraints, i.e., bandwidth, number of connections to a UAV, power limit, interference threshold, UAV heights, and backhaul data rate. To meet this joint objective, we take advantage of the genetic algorithm (GA) due to the offline nature of our optimization problem. The performance of the proposed approach is evaluated using the unsupervised learning-based k-means clustering algorithm. We observe that the proposed approach is highly effective to satisfy the requirements of smart fronthaul networks.