The low-Earth-orbit (LEO) satellite network has emerged as a promising technology for providing global coverage with high data rates. However, analyzing the performance of the LEO satellite network is particularly challenging due to its dependence on several orbit geometry parameters, such as satellite altitude and inclination angle. This paper introduces a novel systematic analysis framework for the LEO satellite network, emphasizing the significance of orbit geometric parameters. We model the location of satellites in a circular orbit according to a one-dimensional homogeneous Poisson point process. Utilizing this model, we derive the distribution of the nearest distance between a satellite and a fixed user’s location on Earth as a function of orbit-geometry parameters. Leveraging this distribution, we characterize the coverage probability of a single-orbit LEO network, considering both small and large-scale fading effects. We then extend our coverage analysis to multi-orbit networks, demonstrating the advantages of utilizing multi-orbit satellite networks in terms of coverage probability. Finally, we present simulation results to validate the proposed model and its accuracy.

Background/Objectives: Hospital length of stay (LOS) is widely analyzed and serves as a benchmark for assessing changes during hospitalization. This study introduced a method to estimate patients’ LOS and highlighted the variations in LOS among individuals with or without multiple chronic conditions (MCCs) and across different levels of disease severity, using data from the 2016 National Inpatient Sample in the United States. Methods: To analyze the factors influencing LOS, a multinomial logistic regression model was employed, demonstrating its effectiveness in estimating and predicting expected LOS. Factors such as demographic characteristics, MCCs, and disease severity were strongly linked to LOS. Results: The overall prevalence of MCCs exceeded 66%, rising to over 90% among elderly patients and more than 88% among those with severe diseases. LOS distribution was primarily concentrated within the first month following admission: over 13% of patients were discharged within a day, over 85% within a week, and more than 99% within a month. Multinomial logistic regression analysis showed that LOS was significantly influenced by age, disease severity, and the presence of MCCs. Older patients, especially those with MCCs, had significantly longer LOSs compared to younger patients without MCCs. Conclusions: LOS tended to increase with age and higher disease severity, particularly in patients with MCCs. Multinomial logistic regression revealed that patients over 65 and those with high disease severity (severity score 4) had significantly longer LOS. Shorter LOS was more frequent among patients under 65 years old, those without MCC, and those with low disease severity, whereas longer LOS was commonly observed in patients with MCCs or high disease severity.

This paper uses the theory of point processes and stochastic geometry to quantify the sky visibility experienced by users located in an outdoor environment. The general idea is to represent the buildings of this environment as a stationary marked point process, where the points represent the building locations and the marks their heights. The point process framework is first used to characterize the distribution of the blockage angle, which limits the visibility of a typical user into the sky due to the obstruction by buildings. In the context of communications, this distribution is useful when users try to connect to the nodes of an aerial or non-terrestrial network in a Line-of-Sight way. Within this context, the point process framework can also be used to investigate the gain of connectivity obtained thanks to Reconfigurable Intelligent Surfaces. Assuming that such surfaces are installed on the top of buildings to extend the user’s sky visibility, this point process approach allows one to quantify the gain in visibility and hence the gain in connectivity obtained by the typical user. The distributional properties of visibility-related metrics are cross-validated by comparison to simulation results and 3GPP measurements.

In this paper, the problem of direction-of-arrival (DoA) estimation for a hybrid array of subarrays is considered in millimeter-wave communication systems. Unlike much prior work using the outputs from the analog subarrays with symbol-wise processing, the proposed framework jointly exploits multiple outputs in time domain for high-resolution DoA estimation. With block-wise processing, the proposed method designs an optimal hybrid combiner maximizing beamforming gain, while reducing a receiver complexity based on fast Fourier transform filtering for each blocked signal. The performance of the proposed method is analyzed, including estimation performance with a limited training signal, training overhead, and computational complexity. An extension of the proposed method to a uniform planar array case is also discussed. Numerical results show the effectiveness of the proposed algorithm.

Wireless-powered communications (WPCs) are increasingly crucial for extending the lifespan of low-power Internet of Things (IoT) devices. Furthermore, reconfigurable intelligent surfaces (RISs) can create favorable electromagnetic environments by providing alternative signal paths to counteract blockages. The strategic integration of WPC and RIS technologies can significantly enhance energy transfer and data transmission efficiency. However, passive RISs suffer from double-fading attenuation over RIS-aided cascaded links. In this article, we propose the application of an active RIS within WPC-enabled IoT networks. The enhanced flexibility of the active RIS in terms of energy transfer and information transmission is investigated using adjustable parameters. We derive novel closed-form expressions for the ergodic rate and outage probability by incorporating key parameters, including signal amplification, active noise, power consumption, and phase quantization errors. Additionally, we explore the optimization of WPC scenarios, focusing on the time-switching factor and power consumption of the active RIS. The results validate our analysis, demonstrating that an active RIS significantly enhances WPC performance compared to a passive RIS.

Accurate performance characterization of low Earth orbit (LEO) satellite networks is challenging due to terrestrial blockages and the resulting heterogeneous line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. This paper develops a tractable stochastic-geometry framework for analyzing the downlink coverage probability of LEO satellite networks under probabilistic shadowing. Unlike prior works based on homogeneous propagation and nearest-satellite association, we adopt a strongest-satellite association rule and explicitly account for heterogeneous pathloss and fading statistics. We derive an exact analytical expression for the coverage probability in the interference-limited regime, together with tight upper and lower bounds. For a simplified shadowing and beam-gain model, we further obtain a closed-form approximation that facilitates systematic investigation. Our analysis and numerical results show that the desirable satellite deployment density also depends on the terrestrial propagation environment, and that satellite density, altitude, and terrestrial environment are fundamentally coupled. In particular, denser terrestrial environments can support higher satellite densities due to effective interference attenuation, and lower altitudes generally provide higher coverage but exhibit increased sensitivity to density selection. The proposed framework offers practical deployment guidelines for LEO satellite networks.