Search Results (169)

This paper examines the secrecy performance of a decode-and-forward (DF) relay-assisted device-to-device (D2D) communication system operating over Terahertz (THz) channels in multi-cluster two-wave (MTW) fading environments. Eavesdroppers are located near the relay and the receiver, intercepting their respective signals. Co-channel interference (CCI) affecting the relay, receiver, and eavesdroppers is also considered. To counter fading, both the relay and the receiver employ Maximal Ratio Combining (MRC). The analysis uses a characteristic function (CF)-based approach to derive key secrecy metrics, such as secrecy outage probability, secrecy success probability, the probability of strictly positive secrecy capacity, and intercept probability. The derived expressions are dependent on the characteristics of the THz, MTW fading, and CCI parameters. Finally, the system’s performance is then evaluated numerically for a range of channel and interference parameters. Full article

This study examines the potential contribution of Solar Electric Vehicles (SEVs) and Vehicle-Integrated Photovoltaics (VIPV) to disaster-related energy resilience through a probabilistic modeling framework. While previous research has highlighted the technical feasibility of EV-based support for microgrids and emergency facilities, it has paid limited attention to the behavioral uncertainty surrounding voluntary energy sharing by EV owners. To address this gap, we develop a Monte Carlo simulation model that integrates technical constraints, solar-generation variability, and heterogeneous participation probabilities to evaluate whether SEVs can sustain essential loads during prolonged outages. The analysis focuses on a worst-case scenario in which external lifelines are disrupted for seven days. Results indicate that approximately 450–1000 SEVs within a 5 km radius are required to maintain a continuous power supply, with BEVs requiring roughly twice as many units due to the absence of onboard PV generation. The findings highlight the sensitivity of resilience outcomes to user behavior and spatial vehicle distribution, underscoring the need for incentive mechanisms to encourage participation. Key limitations include simplified behavioral assumptions, region-specific irradiance conditions, and the exclusion of mobility constraints. Overall, the study provides a quantitative foundation for integrating SEVs into resilience planning while emphasizing the importance of social dynamics in determining real-world feasibility. Full article

Ensuring fast, reliable communication for emergency vehicles is vital in a smart-city vehicular ad hoc network. However, conventional technologies such as dedicated short-range communications and radio links often fail to meet strict low-latency, high-reliability requirements in congested, resource-limited environments. We developed a priority-based power allocation scheme that reserves sufficient transmission power and bandwidth for emergency vehicles while maintaining acceptable service for regular vehicles. Simulation and performance analysis show that the proposed method achieves lower outage probability and higher sum rate than existing resource allocation strategies under various channel conditions and signal-to-noise ratios, providing an effective communication solution for urban emergency services. Full article

In this paper, the performance of dual-hop relay transmission in modern wireless communication systems is analyzed by considering two fundamental relaying techniques, namely, Amplify-and-Forward (AF) and Decode-and-Forward (DF). The propagation conditions on the source–relay (S-R) and relay–destination (R-D) links are modeled using the κ-μ statistical distribution, which effectively captures the fading characteristics in both line-of-sight (LoS) and non-line-of-sight (NLoS) environments. The analysis focuses on key performance metrics, including the outage probability (P_out) and average bit error probability (P_e), for Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) modulation schemes, assuming transmission via a single relay without a direct S–D link. Closed-form expressions for the considered metrics are derived based on the κ-μ model and verified by numerical evaluation. In addition to classical analytical modeling, a Generative Artificial Intelligence (GenAI)-enabled workflow is incorporated as a supportive tool in order to aid in automated analysis, the interpretation of the results in the context of network management under varying channel and system parameters based on the P_out and P_e calculations with the aim to tackle the underlying complexity and cognitive load of infrastructure adaptation and re-configuration operations. The combined analytical and GenAI-assisted approach provides valuable insights for the optimization, design, and continuous evolution of robust relay-based architectures in next-generation wireless networks. Full article

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over $(r_{\mathrm{PV}},P_{\mathrm{ext}})$ exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments. Full article

Free-space optical (FSO) communication systems offer fiber-like bandwidth, high security, and rapid deployment; however, their performance is highly susceptible to atmospheric impairments, such as dust storms, which can cause fading that degrades link reliability. In this study, we analyze the performance of FSO links under a dust-induced fading channel modeled as a Beta distribution channel. We derive an expression for the instantaneous signal-to-noise ratio (SNR) distribution. Using the SNR expression, we construct a general framework that yields closed-form formulas for fundamental performance measures such as outage probability, average bit-error rate (BER), and ergodic capacity. The analysis considers both intensity modulation/direct detection (IM/DD) and coherent detection techniques, encompassing typical modulation schemes including modulation formats such as on–off keying (OOK), M-ary phase-shift keying (M-PSK), and M-ary quadrature amplitude modulation (M-QAM). The results show that dust-induced fading penalizes all modulations, though coherent detection achieves better error performance than IM/DD at equivalent SNR. For example, a coherent receiver requires approximately 4.4 dB lower average SNR than an IM/DD system to achieve the same outage probability. Overall, the proposed unified framework shows that dust-induced fading can severely degrade the performance of FSO links, while also quantifying how network operators can trade off complexity and performance when choosing between coherent and IM/DD detection under realistic dust-storm conditions. Full article

Defending power systems against a high-altitude electromagnetic pulse (HEMP) requires accurately assessing its impact on critical equipment. This paper presents a method integrating theoretical analysis, deep neural networks (DNNs), critical thresholds for primary equipment, and the minimum path method to quantitatively assess the failure probabilities of critical equipment and their effects on a 10 kV distribution network. The analysis of HEMP impact on power distribution networks can be completed within several tens of seconds. Results indicate that the failure probabilities of unreinforced transformers and insulators can reach up to 96% and 12.7%, respectively. These failures can cause typical 10 kV overhead line distribution networks to experience power outages over distances exceeding a thousand kilometers. The maximum power interruption probability reaches 41.6%, with a maximum load loss ratio of 48.6%, even with the proportion of unreinforced transformers of 5%. The spatial distribution of power interruption probability and load loss ratio exhibits an “eye” shape. The results also identify insulator failure as the primary cause of system failures, and corresponding protective suggestions are provided. Full article

This paper investigates an integrated sensing and communication (ISAC) system operating in a rainfall scenario, where a base station (BS) simultaneously serves multiple communication users and performs rainfall detection. Specifically, considering the fading characteristics of the millimeter-wave (mmWave) channel and the impact of rainfall on the signal propagation link, we adopt the Weibull distribution as the channel model between the nodes. Based on the above, the received signal-to-noise ratio (SNR), channel capacity, bit error rate (BER), and outage probability of the users within the system are analyzed to characterize the communication performance. Furthermore, the sensing capability of the BS is demonstrated through the analysis of the probability of rainfall. Simulation results reveal that increasing the distance between the BS and users significantly degrades their communication performance. Furthermore, the performance is highly sensitive to the rainfall intensity. Specifically, compared to storm conditions, light rain yields an improvement of 16.9 dB in the average user SNR, a 7.2 bps/Hz increase in channel capacity, and a 40.2% reduction in the outage probability. Additionally, an increase in the complex dielectric constant of raindrops substantially reduces the backscattering coefficient at the ISAC BS. Full article

Cold-climate urban neighborhoods face mounting energy and thermal risks from extreme weather and power outages, creating trade-offs between different resilience capacities and objectives. This study develops a scalable, data-driven decision-making tool to support early-stage prioritization of resilience strategies at both the building component and neighborhood levels. A database of 48 active and passive strategies was systematically linked to 14 resilience objectives, reflecting energy- and thermally oriented capacities. Each strategy–objective pair was qualitatively assessed through a literature review and translated into probability distributions. Monte Carlo simulations (10,000 iterations) were performed to generate possible outcomes and several scores were calculated. Comparative scenario analysis—spanning holistic, short-term, long-term, energy-oriented, and thermally oriented perspectives—highlighted distinct adoption patterns. Active energy strategies, such as ESS, decentralized RES, microgrids, and CHP, consistently achieved the highest adoption (A) scores across levels and scenarios. Several passive measures, including green roofs, natural ventilation with passive heat recovery, and responsive glazing, also demonstrated strong multi-objective performance and outage resilience. A case study application integrated stakeholder-specific objective weightings, revealing convergent strategies suitable for immediate adoption and divergent ones requiring negotiation. This tool provides an adaptable probabilistic foundation for evaluating resilience strategies under uncertainty. Full article

In this study, data on the characteristics of overhead power lines of high voltage was used in a classification task to predict power supply outages by means of a supervised machine learning technique. In order to choose the most optimal features for power outage prediction, an Exploratory Data Analysis on power line parameters was carried out, including statistical and correlational methods. For the given task, five classifiers were considered as machine learning algorithms: Support Vector Machine, Logistic Regression, Random Forest, and two gradient-boosting algorithms over decisive trees LightGBM Classifier and CatBoost Classifier. To automate the process of data conversion and eliminate the possibility of data leakage, Pipeline and Column Transformers (builder of heterogeneous features) were applied; data for the models was prepared using One-Hot Encoding and standardization techniques. The data were divided into training and validation samples through cross-validation with stratified separation. The hyperparameters of the classifiers were adjusted using optimization methods: randomized and exhaustive search over specified parameter values. The results of the study demonstrated the potential for predicting power failures on 110 kV overhead power lines based on data on their parameters, as can be seen from the derived quality metrics of tuned classifiers. The best quality of outage prediction was achieved by the Logistic Regression model with quality metrics ROC AUC equal to 0.78 and AUC-PR equal to 0.68. In the final phase of the research, an analysis of the influence of power line parameters on the failure probability was made using the embedded method for determining the feature importance of various models, including estimating the vector of regression coefficients. It allowed for the evaluation of the numerical impact of power line parameters on power supply outages. Full article

This work quantifies downtime caused by cyberattacks for eight critical urban services in Milan by coupling sectoral Continuous-Time Markov Chains (CTMCs) with an approximately equal-area H3 hexagonal grid of the city. The pipeline ingests OpenStreetMap infrastructure, simulates coupled failure/repair dynamics across sectors (power, telecom, hospitals, ambulance stations, banks, ATMs, surveillance, and government offices), and reports availability, outage burden (area under the infected/down curve, or AUC), and multi-sector distress probabilities. Cross-sector dependencies (e.g., power→telecom) are modeled via a joint CTMC on sector up/down states; uncertainty is quantified with nested bootstraps (inner bands for stochastic variability, and outer bands for parameter uncertainty). Economic impacts use sector-specific cost priors with sensitivity analysis (PRCC). Spatial drivers are probed via hotspot mapping (Getis–Ord $G_i^{*}$, local Moran’s I) and spatial regression on interpretable covariates. In a baseline short decaying attack, healthcare remains the most available tier, while power and banks bear a higher burden; coupling increases P(≥$k\mathrm{sectors}\mathrm{down})$ and per-sector AUC relative to an independent counterfactual, with paired-bootstrap significance at $\alpha =0.05$ for ATMs, banks, hospitals, and ambulance stations. Government offices are borderline, and telecom shows the same direction of effect but is not significant at $\alpha =0.05$. Under a persistent/adaptive attacker, citywide downtime and P(≥2) rise substantially. Costs are dominated by telecom/bank/power under literature-informed penalties, and uncertainty in those unit costs explains most of the variance in total loss. Spatial analysis reveals statistically significant hotspots where exposure and dependency pressure are high, while a diversified local service mix appears protective. All code and plots are fully reproducible with open data. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article

This study investigates a dynamic switching method for signal relay protocols in Cooperative Pattern Division Multiple Access (Co-PDMA) networks. The proposed approach aims to fully utilize the advantages of signal relays in fading-prone environment while simultaneously reducing the network outage probability and improving the throughput and energy efficiency. To demonstrate the necessity of implementing the dynamic switching method for signal relay protocols, Co-PDMA networks with Decode-and-Forward (DF) or Amplify-and-Forward (AF) protocols are explored over Nakagami-m fading. Based on the analysis of these two scenarios, the overall outage probability, throughput, and energy efficiency of the Co-PDMA network with a dynamic DF/AF protocol are determined. The results demonstrate that the proposed method selects the optimal signal relay protocol for forwarding user data in a simple and efficient manner across varying transmit signal-to-noise ratios, quality of service, and signal relay locations. Compared with fixed signal relay protocols, the proposed method is more conducive to achieving green communication in Co-PDMA networks, as it enhances communication reliability and the total volume of data transmitted. Full article

A three-node cooperative communication system with one-way transmission and a decode-and-forward relaying scheme is considered. The outage probability of the system is analyzed under the assumption that both the source and relay are energy harvesting nodes, and signal space diversity is employed to enhance transmission reliability. Exact closed-form and asymptotic expressions for the outage probability are derived. An optimization problem is also formulated to minimize the outage probability with respect to the transmission powers of the source and relay. Our analysis shows that the outage probability is a convex function of the transmit powers, with a unique global minimum. Monte Carlo simulations are conducted to validate the exact closed-from expression of the outage probability and the optimal transmit powers. In contrast to traditional battery-powered systems, the results show that increasing the transmit power in energy harvesting cooperative communication systems may result in a higher outage probability. The results also show that, at high SNRs, the optimal transmit power of a node is the power that balances its energy harvesting and depletion rates, leading to the best energy utilization. Full article

The dynamic development of distributed sources (mainly RES) contributes to the emergence of, among others, balance and overload problems. For this reason, many RES do not receive conditions for connection to the power grid in Poland. Operators sometimes extend permits based on the possibility of periodic power reduction in RES in the event of the problems mentioned above. Before making a decision, investors, for economic reasons, need information on the probability of annual power reduction in their potential installation. Analyses that allow one to determine such a probability require knowledge of the reliability indicators of transmission lines and transformers, as well as failure removal times. The article analyses the available literature on the annual risk of outages of these elements and methods to determine the appropriate reliability indicators. Example calculations were performed for two networks (test and real). The values of indicators and times that can be used in practice were indicated. The unique contribution of this article lies not only in the comprehensive comparison of current, relevant transmission line and transformer reliability analysis methods but also in developing the first reliability indices for the Polish power system in more than 30 years. It is based on the relationships presented in the article and their comparison with results reported in the international literature. Full article