# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 3 Results
### Propagation Models Results
A graph was generated to see the difference between ideal propagation conditions and small-scale fading, as shown in Figure 23. This graph represents the highest received power for each time step, highlighting the dynamic variations introduced by small-scale fading. The intentional disruption simulates real-world conditions. We measured 800 times each with a time step of 10 ms. Together, these figures highlight the critical role of environmental geometry and obstacle placement in shaping UAV communication reliability, particularly in complex or semi-reflective environments where multipath effects dominate. In our measurements, small-scale fading was assessed by capturing a dense time series of received-power samples at a fixed 5 m separation, with antennas aligned for clear line-of-sight (LOS). Because the LOS component dominates under these conditions, the received envelope follows a Rician distribution rather than a pure Rayleigh model. In other words, the Rician Probability Density Function (pdf) incorporates both the deterministic LOS contribution and the scattered multipath components. Mathematically, this pdf is given by [24]:  
Figure 18.0  
where:  
\(s\) is the non-centrality parameter (the magnitude of the LOS component),  
Figure 18.1 Received power, metal plate vs. LOS (**a**), Static vs. dynamic metal plates (**b**), LOS vs. Wall vs. obstacles in between (**c**), LOS vs. persons vs. obstacles (**d**). \(\sigma\) is the scale parameter (the standard deviation of the scattered components), and \(I_{0}\) is the modified Bessel function of the first kind and order zero. A convenient way to characterize any Rician distribution is through its shape parameter \(K\), often called the Rician K-factor, which quantifies the ratio of the power in the dominant, line-of-sight component to the power in the scattered components. It is defined as [25]:  
Figure 18.2  
which quantifies the ratio of direct-path power to scattered-path power. In our dataset, we first constructed a normalized histogram of the measured attenuation (in dB) at 5 m to visualize the empirical distribution. We then performed a maximum-likelihood fit of a Rician pdf to those samples, as shown in Figure 18.3  
The resulting Rician fit has a mean envelope of 53.96 dB and a standard deviation equivalent to \(\sigma_{\text{dB}}=5.57\)dB. From these parameters, we also computed a two-sigma confidence interval (approximately 95.5% coverage), which spans 42.82 dB to 65.11 dB of attenuation. The close agreement between the normalized histogram and the fitted Rician curve confirms that our small-scale fading channel at this distance is accurately described by a Rician model. The single outlier near 50 dB, visible in the histogram, is likely due to a transient multipath reflection during data collection but does not significantly alter the overall fit quality. By fitting the Rician distribution and quantifying its K-factor, mean, and variance, we have fully characterized the small-scale fading statistics for our carbon-fiber monopole in a controlled LOS scenario. These statistics can now be used to inform link-budget margins for UAV-to-UAV communications in similar environments.