Abstract
In this work, a mathematical framework to evaluate the performance of a finite, three dimensional (3D) unmanned aerial vehicle (UAV) network in the presence of interference is developed. The framework builds upon stochastic geometry tools and specifically the binomial point process (BPP) for the spatial distribution of the UAVs. A UAV base station (UAV-BS) reference receiver is located at the center of a sphere and communicates with the nearest transmitting UAV node, whose distance from the UAV-BS reference receiver is either fixed or random. The reverse link suffers from the presence of a single dominant interferer, whose location is random within the sphere. Closed-form expressions are derived for statistical metrics of the signal-to-interference ratio (SIR) for two scenarios, namely for fixed or random location of the desired transmitting node. Then, the impact of the location of the transmitting node on the coverage probability (CP) is studied while the average error probability and the ergodic capacity have been also analytically investigated. Finally, the theoretical results are numerically evaluated and compared to simulation to reveal some useful insights.
Highlights
For the first time, to the best of the authors’ knowledge, a 3D binomial point process (BPP) is considered to model the spatial locations of a realistic unmanned aerial vehicle (UAV) network, where a finite number of UAVs is assumed to be deployed inside a sphere
The analysis focuses on studying the network performance in terms of coverage probability (CP), which is defined as the probability that the received signal-to-interference ratio (SIR) at the UAV base station (UAV-BS) exceeds a threshold value γth
The CP is numerically evaluated under different operational scenarios
Summary
Unmanned aerial vehicles (UAVs) have emerged as key enablers of seamless wireless connectivity in diverse scenarios such as large-scale temporary events, military operations, or emergency situations, and capacity enhancement in occasional demands [1], [2]. Armeniakos et al.: SIR Analysis in 3D UAV Networks: A Stochastic Geometry Approach region, e.g., in a post disaster scenario In such cases, the PPP is clearly not an adequate model for UAV networks since different realizations constitute of random number of points. For the first time, to the best of the authors’ knowledge, a 3D BPP is considered to model the spatial locations of a realistic UAV network, where a finite number of UAVs is assumed to be deployed inside a sphere. B. CONTRIBUTIONS In this paper, a 3D UAV network with a finite number of nodes is assumed and important statistical characteristics of the received signal-to-interference ratio (SIR) are analytically investigated by exploiting the tool of stochastic geometry.
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