Enhanced Base Station Communication System Based On Drone Deployment

With the rapid development of science and technology,we have seen the unprecedented development in unmanned aerial vehicles(UAVs)from different aspects.Accordingly,an increasing number of applications have emerged based on UAVs.Among which,placing UAVs as Aerial Base Stations(ABSs)has received considerable interest in both the industrial and academic community.Compared with Terrestrial Base Stations(TBSs),UAVs have the merits of flexibility and economy,that make them especially suitable for temporarily increasing traffic demand.Moreover,UAVs have a higher possibility of line-of-sight(LOS)links with users because of their flying characteristics,which provide better link quality for the communication.Existing solutions focus on the optimization of the UAV deployment problem for static user topology using the control information obtained from the Terrestrial Base Station(TBS),that makes it hard for the controller to make real-time decisions.Moreover,in previous works,they divided the cell into a set of small independent regular areas or allocate UAVs at the center of the users,which underutilize the UAV resources.To break this stalemate,we propose a SemI-DistributEd system,named SIDE,for the UAV self-deployment.In SIDE,we introduce a mechanical equilibrium-based approach,named EMech,via which the UAV positions are self-adapted according to users' attraction(e.g.,user distance and traffic demand)within their transmission range.To facilitate the EMech,we propose a fine-grained area splitting strategy,termed KDivision,that partitions the service area in accordance with the user density.Finally,an area merging technique,namely RMerge,is exploited to approximately optimize the positions of the UAVs assisted by a Utility Function that strikes a balance amid the network performance and economic cost.Finally,we conduct field experiments to validate the feasibility of EMech.Extensive simulation results show that the proposed SIDE finds the optimal number of assigned UAVs,which not only reduces the cost of the system significantly,but also improves the achievable rate up to 74.6% compared to the existing solutions while consuming almost the same energy level.