3D Modeling of UAV Networks (Unmanned Aerial Vehicles – UAVs)

Athanasios Kanatas


UAV networks have been proposed as a component of the next-generation wireless communication networks (beyond 5G).

Researchers from the Department of Digital Systems propose for a first time a simple yet realistic mathematical framework for the three-dimensional (3D) modeling of a UAV network consisting of a finite number of UAVs. The framework builds upon stochastic geometry tools.


UAV networks have been proposed as a component of the next-generation wireless communication networks (beyond 5G) due to specific advantages they provide in emergency situations, in large-scale concentration situations, even in military applications [1]. Their spatial modeling, however, remains a complicated task. Recently, the adoption of stochastic geometry tools proved capable of modeling extremely complicated frameworks of ground-based two-dimensional (2D) cellular networks [2]. Given the need for characterizing the interference inside a cellular network, stochastic geometry provides the potential to simplify the performance evaluation of such a network and extract some insights into their design.

3D Binomial Point Process has been proposed for the spatial modeling of UAV networks.

Until now, 2D Poisson point process has been widely used for the spatial modeling of users in current cellular networks. However, two major problems appear in this approach. The first one is that 2D modeling is not adequate for realistic UAV networks. Recently [3], researchers from the Department of Digital Systems indicated for the first time the necessity of studying 3D UAV networks. Specifically, it was shown both analytically and through simulations that current 2D models provide a more optimistic performance in terms of coverage, in contrast to a realistic 3D model. The second problem is that an aerial network cannot precisely be modeled through PPP, as different realizations of PPP constitute of different number of nodes. In fact, in realistic 3D aerial networks, the number of aerial nodes is finite. In such cases, a simple yet proper choice for the stochastic spatial modeling of aerial nodes, is the 3D binomial point process. 

Within the context of the analysis performed, the 3D BPP was adopted for the spatial modeling and performance evaluation of a realistic UAV network, where a finite number of UAVs is deployed inside a sphere. A UAV – base station is deployed at the center of the sphere and assumed to communicate with the nearest UAV node. The link suffers from the presence of a dominant interferer. The model under investigation is depicted in the figure. The system was evaluated in terms of several performance metrics revealing useful insights about the design of UAV networks.

This paper conducted and published in the scientific journal IEEE Access by the Ph.D. candidate of the Department of Digital Systems Mr. Charalampos Armeniakos, under the supervision of Prof. Athanasios Kanatas, in collaboration with the Associate Professor of Univ. of Athens, Assistant Prof. Petros Bithas.


[1] M. Mozaffari, W. Saad, M. Bennis, Y. Nam and M. Debbah, “A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems,” IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2334-2360, 2019.

[2] M. Haenggi, Stochastic Geometry for Wireless Networks. Cambridge, U.K.: Cambridge Univ. Press, 2012.

[3] C. K. Armeniakos, P. S. Bithas and A. G. Kanatas, “SIR Analysis in 3D UAV Networks: A Stochastic Geometry Approach,” in IEEE Access, doi:10.1109/ACCESS.2020.3036983.