Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Stochastic Modeling of Dependability Attributes for a Multilayer Satellite‐HAP‐Terrestrial System. |
| Συγγραφείς: |
Raj, Raina, Dharmaraja, S., Vishnevsky, Vladimir, Kozyrev, Dmitry |
| Πηγή: |
International Journal of Communication Systems; 9/25/2025, Vol. 38 Issue 14, p1-19, 19p |
| Θεματικοί όροι: |
Stochastic models, Markov processes, Statistical reliability, Reliability in engineering, Multilayers, Telecommunication satellites |
| Περίληψη: |
High‐altitude platforms (HAPs) are an attractive solution for delivering long‐term and stable relay services in communication systems, thanks to their superior capabilities such as extensive coverage, high mobility, and high‐quality communication channels. The growing interest in deploying HAPs as relay nodes to bridge terrestrial and satellite networks has contributed to the advancement of integrated satellite‐aerial‐terrestrial (SAT) networks. To assess the dependability of such networks, a stochastic model is developed that incorporates the essential characteristics of satellite, HAP, and terrestrial components. A semi‐Markov process (SMP) is employed to capture the non‐Markovian behavior of the system states and to facilitate the evaluation of the model. The underlying study provides steady‐state and time‐dependent analysis of the system. The SMP framework enables the calculation of steady‐state and transient probabilities, which are then used to determine key performance metrics such as system uptime and downtime probabilities, as well as the mean time to failure (MTTF) and reliability of the system. The effects of various component failure parameters on overall system performance are illustrated using graphical analysis. Finally, the analytical results derived from the model are validated through discrete‐event simulation, ensuring the reliability of the proposed approach. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Βάση Δεδομένων: |
Complementary Index |