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On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks
Using drone base stations (drone-BSs) in wireless networks has started a...
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Reshaping Cellular Networks for the Sky: The Major Factors and Feasibility
In this paper, we investigate the feasibility of using the existing cell...
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Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects
Drones (or unmanned aerial vehicles [UAVs]) are expected to be an import...
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Ultra-Dense 5G Small Cell Deployment for Fiber and Wireless Backhaul-Aware Infrastructures
In this paper, we study the cell planning problem for a two-tier cellula...
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Cell Association via Boundary Detection: A Scalable Approach Based on Data-Driven Random Features
The problem of cell association is considered for cellular users present...
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Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones
This paper proposes an energy management framework for cellular heteroge...
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Spatial Configuration of Agile Wireless Networks with Drone-BSs and User-in-the-loop
Agile networking can reduce over-engineering, costs, and energy waste. T...
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Beyond 5G with UAVs: Foundations of a 3D Wireless Cellular Network
In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs as well as latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs' deployment based on the notion of truncated octahedron shapes is proposed that ensures full coverage for a given space with minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs' latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from optimal transport theory. Simulation results show that the proposed approach reduces the latency of drone-UEs compared to the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46 compared to the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.
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