Modern wireless traffic demand pushes Internet Service Providers to develop effective strategies to improve user experience. Since deploying dense Base Stations (BSs) is not cost efficient, an alternative is to deploy autonomous drones to supplement existing BSs. A street graph is adopted to represent the area of interest. The outdoor User Equipments (UEs) to be served locate near streets and the 2D projections of drones are restricted to streets to avoid collision with buildings. We build up a UE density function based on a real dataset, reflecting the traffic in the area. We study four problems: where to deploy single drone to cover maximum UEs, where to deploy @math drones cover maximum UEs subject to an inner drone distance constraint, where to deploy @math drones cover maximum UEs subject to inner drone distance constraint and drones' battery constraints, and the minimum drones to cover a given percentage of UEs subject to inner drone distance constraint. We prove that the latter three problems are NP-hard and propose greedy algorithms with theoretical analysis. To our best knowledge, this is the first paper to consider the battery constraints for drone deployments. Extensive simulations have been conducted to verify the effectiveness of our approaches.
The use of autonomous drones in cellular networks has opened new paradigms in the area of networking. With the development of 5G network, drones will play a crucial role as a continuous network support @cite_8 . There is a growing number of publications on the topic of drones in cellular network. Some publications study the fundamental wireless communication between drones and UEs. In @cite_22 , air-to-ground pathloss is modelled. It shows that there are two main propagation situations: UEs have Line-of-Sight (LoS) with drones and UEs have non-Line-of-Sight (NLoS) with drones due to reflections and diffractions. A closed form expression for the probability of LoS between drones and UEs is developed in @cite_16 . The authors of @cite_4 investigate the maximum coverage by two drones in the presence and absence of interference between them.
@cite_16 @cite_4 @cite_22 @cite_8
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The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing in a wide range of wireless networking applications. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. On the other hand, UAVs can operate as flying mobile terminals within the cellular networks. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, air-to-ground channel modeling, and energy efficiency are explored along with representative results. Then, fundamental open problems and potential research directions pertaining to wireless communications and networking with UAVs are introduced. To cope with the open research problems, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems. With hard requirements of high performance for the next generation mobile communication systems, especially 5G networks, coverage has been the crucial problem which requires the deployment of more stations by the service providers. However, this deployment of new stations is not cost effective and requires network replanning. This issue can easily be overcome by the use of Unmanned Aerial Vehicles (UAVs) in the existing communication system. Thus, considering this as a problem, an intelligent solution is presented for the accurate and efficient placement of the UAVs with respect to the demand areas resulting in the increase in the capacity and coverage of the wireless networks. The proposed approach utilizes the priority-wise dominance and the entropy approaches for providing solutions to the two problems considered in this paper, namely, Macro Base Station (MBS) decision problem and the cooperative UAV allocation problem. Finally, network bargaining is defined over these solutions to accurately map the UAVs to the desired areas resulting in the significant improvement of the network parameters, namely, throughput, per User Equipment (UE) capacity, 5th percentile spectral efficiency, network delays and guaranteed signal to interference plus noise ratio by 6.3 , 16.6 , 55.9 , 48.2 , and 36.99 , respectively in comparison with the existing approaches. Using drone base stations (drone-BSs) in wireless networks has started attracting attention. Drone-BSs can assist the ground BSs in both capacity and coverage enhancement. One of the important problems about integrating drone-BSs to cellular networks is the management of their placement to satisfy the dynamic system requirements. In this paper, we propose a method to find the positions of drone-BSs in an area with different user densities using a heuristic algorithm. The goal is to find the minimum number of drone-BSs and their 3D placement so that all the users are served. Our simulation results show that the proposed approach can satisfy the quality-of-service requirements of the network. Modern day wireless networks have tremendously evolved driven by a sharp increase in user demands, continuously requesting more data and services. This puts significant strain on infrastructure-based macro cellular networks due to the inefficiency in handling these traffic demands, cost effectively. A viable solution is the use of unmanned aerial vehicles (UAVs) as intermediate aerial nodes between the macro and small cell tiers for improving coverage and boosting capacity. This letter investigates the problem of user-demand-based UAV assignment over geographical areas subject to high traffic demands. A neural-based cost function approach is formulated, in which UAVs are matched to a particular geographical area. It is shown that leveraging multiple UAVs not only provides long-range connectivity but also better load balancing and traffic offload. Simulation study demonstrates that the proposed approach yields significant improvements in terms of fifth percentile spectral efficiency up to 38 and reduced delays up to 37.5 compared with a ground-based network baseline without UAVs.