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.
Highlights
Mobile data traffic demand is growing exponentially each year due to the increased mobile users and advances in traffic-intensive applications [1]
We prove that the performance of the proposed particle swarm optimization algorithm is superior to that of the uniform distribution of UAV and base station and K-means algorithm
We considered the location optimization and resource allocation for a heterogeneous cloud radio access network (H-cloud radio access network (CRAN)) system integrated with UAVs
Summary
Mobile data traffic demand is growing exponentially each year due to the increased mobile users and advances in traffic-intensive applications [1]. Aerial deployment is considered as a promising approach for the universal access from air to ground user equipment (UE) in designated areas during temporary events (e.g., hot spots and events in large public spaces) [18,19,20,21,22,23,24] Such dynamic aerial BSs are able to dynamically adjust their positions and altitudes to provide air-to-ground (A2G) links, which are superior to ground-based static BSs. The application of aerial BSs is considered as beneficial to the existing cellular systems, which can enhance the wireless capacity and coverage on the ground. We propose an approach that optimizes 3D positions of multiple UAVs, user association, admission control, and power allocation in order to maximize system throughput and user numbers. The system model and MINLP problem formulation are introduced in
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