Throughput maximization in poisson aerial base station-based networks with coverage probability and power density constraints

In this paper, we propose a new scheme called dynamic power-dynamic density at which our goal is to maximize the throughput of aerial base station-based networks with coverage probability and power density constraints. The corresponding optimization problem is nonconvex and intractable. To tackle this issue, we propose an iterative algorithm based on the well-known alternative method. In this method, we decompose the optimization problem into two nonconvex subproblems, namely, power allocation and density finding. Then, in order to provide a low complex and high speed algorithm, these subproblems are transformed into geometric programming forms, which can be solved by existing convex tools. Moreover, the convergence of the proposed iterative scheme is proved and it is studied from the computational complexity perspective. To investigate the performance of the proposed algorithm with existing schemes, we consider two other schemes, namely, dynamic power-fixed density and dynamic density-fixed power. Based on simulation results, the throughput of aerial base stations in our scheme is approximately 27% more than that of the two existing schemes. Furthermore, the trade-off between power and density for different network parameters is studied.