In this paper, we numerically study the secrecy performance of the free-space optical (FSO) system by considering different eavesdropping scenarios. More precisely, we considered three possible eavesdropping scenarios for Eve: 1) Eve is between Alice and Bob; 2) Eve and Bob are in the same receiving plane; 3) Eve is behind Bob. We adopt the Málaga ( M )-distribution channel to model atmospheric turbulence due to the presence of link blockage while considering the non-zero boresight pointing error and path loss. To do so, we obtain a novel probability density function (PDF) and cumulative distribution function (CDF) under different eavesdropping scenarios, based on which we derived the secrecy outage probability (SOP) analytical expressions as well as their asymptotic expressions at a high SNR regime. We verified the results using Monte Carlo simulations, which showed that the parameters related to atmospheric turbulence and pointing errors, as well as the location of the eavesdropper, have different effects on different eavesdropping scenarios.

Optical satellite networks (OSN) utilizing wavelength division multiplexing (WDM) inter-satellite links (ISLs) and wavelength routing is becoming a new trend in constructing high-speed, large-capacity, and low-latency global network systems. Since the number of wavelength channels for the WDM ISLs is limited, the traffic requests might incur queuing delays due to the wavelength channel congestion. To evaluate the delay-tolerance characterization of dual-layer wavelength routing OSN (DWROSN), the ISLs arrival coefficient (IAC) is proposed to construct the queuing model. Simulation results are compared with theoretical results in terms of queuing length and queuing delays. The results show that the traffic intensity, number of wavelength channels, and node degree of the satellite have an impact on the queuing characterization of the DWROSN. The queuing model with the IAC can efficiently evaluate the queuing characterization of the DWROSN.

In this paper, the secrecy outage probability of a partially coherent beam free-space optical communication system considering the combined effect of anisotropic non-Kolmogorov strong turbulent atmosphere and the eavesdropper’s position is investigated. The Fisher–Snedecor distribution is chosen to model the atmospheric turbulence because it is not only suitable for weak to strong turbulence conditions but is also mathematically simple. Based on the fading channel model and the generalized pointing error model, we derive analytical expressions for the lower bounded secrecy outage probability. Also, we obtain asymptotic closed-form expression at a high signal-to-noise regime. The obtained expressions are corroborated by Monte Carlo simulations. The calculation results based on the analytical expressions show that increasing the source coherence parameter makes the secrecy interruption probability performance first better and then worse for a given condition, and an optimal value is observed. The anisotropic coefficient and power law have a positive effect on the secrecy outage probability of the free-space optical communication system. Under the given conditions, the performance of the secrecy outage probability is significantly improved by the divergent beam, although the increased spot size at the receiver makes it easier for eavesdroppers to tap the link. The secrecy outage probability performance deteriorates as the vertical component of Bob, the jitter variance, and the refractive index structure constant increase.

Efficient autonomous exploration in unknown environments is a core challenge for Unmanned Aerial Vehicle (UAV) applications in unstructured settings. The primary challenges are exploration speed, coverage efficiency, and the autonomous, efficient, and obstacle-/threat-avoiding global guidance of UAV under local observational information. This paper proposes an autonomous exploration method driven by simultaneous incremental map prediction and the fusion of global frontier information to enhance the exploration efficiency of UAVs in unknown unstructured environments. Based on generative deep learning, we introduce an incremental map prediction method for 3D unstructured mountainous terrain, enabling the simultaneous acquisition of map predictions and their uncertainty estimates. Map prediction and trajectory planning are conducted concurrently: by utilizing the simultaneously predicted 3D map and its confidence (i.e., the uncertainty estimates), an overlap analysis is conducted between the flyable areas in the predicted map and the high-confidence regions. Dynamic guidance subspaces are generated by extracting global frontier points, within which shortest-time optimization is adopted for trajectory planning to maximize information gain and coverage per step. Experimental results demonstrate that compared to classical methods, our proposed approach achieves significant performance improvements in key metrics, including map coverage rate, total exploration time, and average path length.