On Secrecy Performance of a Dual-Hop UAV-Assisted Relaying Network with Randomly Distributed Non-Colluding Eavesdroppers: A Stochastic Geometry Approach

Abstract

Unmanned aerial vehicle (UAV)-based relaying has been considered to offer an excellent performance due to its flexible mobility, on-demand deployment, and cost effectiveness compared to conventional ground-relaying methods. This paper studies the secrecy performance of a dual-hop UAV-assisted relay network, where the base station communicates with the ground user via a low altitude UAV in the presence of randomly distributed eavesdroppers. A stochastic geometric approach is employed to model the spatial locations of the ground user and the eavesdroppers which follows a Homogeneous Poisson Point Process (HPPP). Based on this theory, cumulative distribution functions (CDF) of the ground user and the eavesdroppers are obtained. Considering the decode-and-forward (DF) relay protocol, the CDF equivalent end-to-end instantaneous signal-to-noise ratio (SNR) of the network is derived. To characterize the network secrecy performance, the exact analytical expressions for the network security outage probability (SOP), the strictly positive secrecy capacity (SPSC), and the average secrecy capacity (ASC) are derived. Moreover, a Monte-Carlo simulation is provided to show the accuracy of the derived analytical expressions. The results depict that both the network and channel parameters that include the fading parameter, the density of the eavesdroppers, the average SNR of the B-to-U link, the average SNR of the U-to-E link, the UAV altitude, and the coverage radius have a significant influence on the network secrecy performance.

Probabilistic Analysis of an RL Circuit Transient Response under Inductor Failure Conditions

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“…Probability theory has been effectively used for the analysis and provision of solutions to various problems in multiple disciplines in science and engineering [26][27][28][29][30][31][32]. For example, it is extensively used in communication systems and signal processing applications [33,34]. The work in [35] used saddle point approximation for the estimation of hazard rate function for a linear combination of Poisson random variables.…”