Access to broadband communications in different parts of the world has become a priority for some governments and regulatory authorities around the world in recent years. Building new digital roads and pursuing a connected society includes looking for easier access to the internet. In general, not all areas where people congregate are fully covered, especially in rural zones, thus restricting access to data communications and inducing inequality. In the present review article, we have comprehensively surveyed the use of three platforms to deliver broadband services to such remote and low-income areas, and they are proposed as follows: unmanned aerial vehicles (UAV), altitude platforms (AP), and low-Earth orbit (LEO) satellites. These novel strategies support the connected and accessible world hypothesis. Hence, UAVs are considered a noteworthy solution since their efficient maneuverability can solve rural coverage issues or not-spots.

Access to broadband communications in different parts of the world has become a priority for some governments and regulatory authorities around the world in recent years. Building new digital roads and pursuing a connected society includes looking for easier access to the Internet. In general, not all the areas where people congregate are fully covered, especially in rural zones, thus restricting access to data communications and bringing inequality. In the present review article, we have comprehensively surveyed the use of three platforms to deliver broadband services to such remote and low-income areas are proposed: Unmanned Aerial Vehicles (UAV), Altitude Platforms (APS), and Low-Earth Orbit (LEO) satellites. These novel strategies support the connected and accessible world hypothesis. Hence UAVs are considered a noteworthy solution since their efficient maneuverability can aboard the rural coverage issues or not-spots.

Achieving the multi-layered spectral objectives of sixth-generation (6G) networks requires high-fidelity channel characterization, particularly for C-band deployments in complex vegetated environments supporting cellular vehicle-to-everything (C-V2X) and non-terrestrial networks (NTN). This study presents a physics-aware, heteroscedasticity-driven channel modeling framework that utilizes Variational Gaussian Processes (VGP) to quantify both aleatory and epistemic uncertainties, thereby characterizing the non-stationary dynamics of foliage-induced shadowing. By employing an Exponentiated Quadratic (EQ) kernel, the proposed stochastic architecture captures spatial correlation from real-world measurements at 4.9, 5.85, and 6.1 GHz. The results demonstrate a significant enhancement in predictive accuracy, achieving average Root Mean Square Error (RMSE) reductions of 73%, 76%, 82%, and 75% over traditional Floating-Intercept (FI), Close-In (CI), ABG, and CIF empirical benchmarks, respectively. These improvements are further validated through coverage probability and spread ratio metrics. Our findings establish variance-adaptive probabilistic regression as a robust alternative to the homoscedastic linear assumption, advancing 6G propagation modeling in high-entropy, vegetated scenarios.