Resident Population Density-Inspired Deployment of K-tier Aerial Cellular Network
Name:
Resident_Population_Density-Inspired_Deployment_of_K-tier_Aerial_Cellular_Network.pdf
Size:
8.437Mb
Format:
PDF
Description:
Accepted Manuscript
Type
ArticleKAUST Department
CEMSE division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaElectrical and Computer Engineering Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Date
2023-03-20Permanent link to this record
http://hdl.handle.net/10754/686857
Metadata
Show full item recordAbstract
Using Unmanned Aerial Vehicles (UAVs) to enhance network coverage has proven a variety of benefits compared to terrestrial counterparts. One of the commonly used mathematical tools to model the locations of the UAVs is stochastic geometry (SG). However, in the existing studies, both users and UAVs are often modeled as homogeneous point processes. In this paper, we consider an inhomogeneous Poisson point process (PPP)-based model for the locations of the users that captures the degradation in the density of active users as we move away from the town center. In addition, we propose the deployment of aerial vehicles following the same inhomogeneity of the users to maximize the performance. In addition, a multi-tier network model is also considered to make better use of the rich space resources. Then, the analytical expressions of the coverage probability for a typical user and the total coverage probability are derived. Finally, we optimize the coverage probability with limitations of the total number of UAVs and the minimum local coverage probability. Finally we give the optimal UAV distribution parameters when the maximum overall coverage probability is reached.Citation
Wang, R., Kishk, M. A., & Alouini, M.-S. (2023). Resident Population Density-Inspired Deployment of K-tier Aerial Cellular Network. IEEE Transactions on Wireless Communications, 1–1. https://doi.org/10.1109/twc.2023.3257222arXiv
2301.00879Additional Links
https://ieeexplore.ieee.org/document/10077541/ae974a485f413a2113503eed53cd6c53
10.1109/twc.2023.3257222