Concise and Informative Article Title Throughput Maximization through Joint User Association and Power Allocation for a UAV‐Integrated H‐CRAN

Abstract

The heterogeneous cloud radio access network (H-CRAN) is considered a promising solution to expand the coverage and capacity required by fifth-generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H-CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H-CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP-hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K-means algorithms.

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

“…The creation of channel models to represent such A2G links is challenging as many factors need to be considered such as a UAV's movement/vibration, changes in the UAVs altitude, type of UAV, type of propagation environment, antenna movements and shadowing caused by the UAVs own frame, to name a few [115]. While just free space path loss methods might suffice if A2G links were only composed of LoS components, to account for NLoS (Non-LoS) components more complex approaches are required [119]. Two popular approaches are the probabilistic path loss model and the stochastic Rician fading model [115] [118].…”

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