## 4 Parameterization and Analysis of the Proposed TDL Model
### Fading Statistics and K-Factor Values of Individual Tap  
Based on the measurement result, different fading behavior is observed for different channel taps. It is important to evaluate the statistical characteristics of individual channel taps, where we consider five typical candidate distributions (Normal, Weibull, Rice, Rayleigh, and Nakagami distribution). To quantitatively analyze the accuracy of five distributions, we utilize the Cramer-von Mises (CVM) test as the goodness-of-fit index, which is more robust and accurate than popularly used Kolmogorov-Smirnov (KS) test. It is an enhanced method due to the integration of the power of the difference between the cumulative distribution functions (CDFs) of measurement data and of the theoretical distribution [39]. The goodness-of-fit index of CVM test can be calculated as [34]  
\[\rho_{c}=\int_{-\infty}^{+\infty}\lvert F_{x}(x)-F(x)\rvert^{2}dF(x)\,, \tag{13}\]  
where \(F_{x}(x)\) and \(F(x)\) are the empirical and theoretical CDF of the sample \(x\), respectively. We statistically analyze the amplitude distribution and calculate the Rice K-factor of per tap, the suitable distribution and K-factor values \(K_{m}\) can be shown in Table 3.  
In scenario FS1, where the UAV is vertically landing, the envelope of the first tap follows the Rice distribution according to the goodness-of-fit index of CVM test, with the maximum K-factor value of individual channel taps is 27.58 dB. This finding is also consistent with physical reality, where a strong LoS component is usually present within the first channel tap. In scenario FS1, other channel taps are found to follow the normal distribution, which usually implies a large value of the K-factor. This is attributed to the UAV descending smoothly at a speed of 1.2 m/s, in the open rural environment, resulting in less severe multipath effect. Therefore, for UAVs in low-altitude scenarios, high K-factor values are conducive to improving signal quality, simplifying equalization and  
Figure 5: (**a**) The measured PDF of normalized empirical APDP. (**b**) The normalized empirical APDP for FS2 model. detection algorithms in UAVs communication systems, and allowing the use of higher-order modulation schemes to support higher data transfer rates and bandwidth utilization. In the scenario FS2, the first two channel taps are found to follow the Weibull distribution, where the estimated scale and shape parameters are \(\beta_{w1}=18.72\), \(\Omega_{w1}=1.015\), and \(\beta_{w2}=3.813\), \(\Omega_{w2}=0.998\) for the first and the second taps, respectively. It can be seen that the parameter \(\beta_{m}\) of the first tap are much larger than that of the second tap, and the other taps all submit to normal distribution. In the scenario FS3, when the UAV is preparing for circling, the altitude of the UAV steady increases resulting in a significant change terrain environment. Objects on ground like houses and trees may cause MPCs, and can be observed clearly in the first four taps, which are found to follow Rice, Weibull, Weibull, and Nakagami distributions, respectively. In measurement scenarios from FS4 to FS6, we evaluate the impact of different UAV altitude on the proposed channel model. It is observed that the K-factor values of the first channel tap strongly depends on the UAV altitude, where the value increases from 15.96 dB at altitude 300 m to 17.23 dB at altitude 800 m. For channel taps with larger delay, the K-factor values only slightly depend on the altitude. Similar observations were reported in reference [40]. Therefore, the height of the UAV can be appropriately raised to increase the K-factor value, thereby improving the signal quality and stability of the UAV communication system, making the communication link more reliable. Additionally, it is noted that the Weibull distribution is found to be the best candidate among considered five theoretical distributions for the first tap at all three UAV altitudes.