They Way of Utilising A Anatomical Based Drones in NGMN A Detailed And Intensiveperformance Analysis

The escalating demand for wireless data, primarily, underscores the demand for alternative network solutions beyond traditional terrestrial infrastructure. Particularly in scenarios like large gatherings or natural disasters, maintaining stable connectivity poses significant challenges for network operators. Unpredictable events such as earthquakes emphasize the critical importance of reliable communication Infrastructure for disaster relief efforts. However, conventional cellular systems often struggle to provide consistent connectivity in such situations. Empirical research illustrated and underscored the potential and capability of drone-based solutions with respect to enhancing user experience, additionally ensuring stable network access, especially when conventional terrestrial infrastructure is compromised. Given these advantages, integrating drones into 5G/6G wireless networks is actively being explored. However, the large-scale deployment of drones presents certain challenges. Stochastic theory, widely employed in analyzing wireless networks due to its ability to model connectivity and randomness across various scenarios, has been Instrumental in several studies. Theoretical findings are validated through Monte Carlo simulations, demonstrating distinct characteristics of drone mobile networks, such as utilizing many antennas and operating at low transmission rate levels. The research delves into a comprehensive evaluation of drone-based mobile networks at the system level, providing mathematical expressions for key metrics such as signal-to-interference-plusnoise ratio (SINR), spectral efficiency (SE), and outage probability. It incorporates LOS and NLOS propagation, as well as multi-slope path loss. The paper adopts a combination of three-dimensional Brownian (random) motion and deterministic motion models to establish performance limits in practical drone cellular areas, allowing exploration of various mobility patterns, Including random Brownian motion. It considers both LOS and NLOS propagation scenarios, with parameters such as path-loss exponents and Nakagami-m fading distributions. The paper provides a detailed description of the network topology and propagation models employed in the analysis.