# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## Appendix A Absolute Error Between theoretical and practical measurements
While advanced techniques such as those in [5; 6] offer high angular resolution and spatial sensing for signal directionality, they do not explicitly model the temporal dynamics of the received power. Thus, our model complements such methods by focusing on directional transitions during motion, which are critical to mobile communication planning and handover optimization. On the other hand, although the second theoretical model accurately predicted typical propagation patterns, it underrepresented extreme field conditions--particularly in suburban inter-handover zones--where sudden drops in the RSSI and increased standard deviations of the RSSNR suggest higher interference levels and less predictable power behavior. In this part of the analysis, the internal coverage areas of the cells are referred to as non-intra-handover and non-inter-handover cases. As expected, the graphs shown in Figures 15 and 16 indicate that power levels are higher within the intra-handover zones than in the inter-handover zones. As observed in both the experimental and theoretical cases, the mean values are very close, and the data distribution and dispersion exhibit similar patterns. This indicates that the applied method yielded results consistent with expectations. By comparing the mean values of the experimental and theoretical RSSI in the urban environment, the results obtained are \(-79.31\,\mathrm{dBm}\) and \(-79.39\,\mathrm{dBm}\), respectively, with a relative error of less than \(1\%\). This minimal discrepancy indicates a strong correlation between both sets of values and confirms the precision of the proposed vector-based model in estimating the received power under typical propagation conditions. Notably, the model achieved this level of accuracy without the need for site-specific calibration, training data, or computationally demanding simulations, distinguishing it from ray-tracing and machine learning-based models. While advanced DoA estimation techniques leveraging intelligent metasurfaces [5; 6] offer superior angular resolution, they are not optimized for continuous power prediction in dynamic mobile scenarios. Nonetheless, these technologies could  
Figure 15: Comparison of experimental RSSI and theoretical RSSI in intra-handover and non-intra-handover cases. complement our approach by supplying accurate spatial orientation data, enabling hybrid models that incorporate both directionality and temporal power variation. Furthermore, statistical validation through the analysis of variance (ANOVA) confirmed the significant impact of mobility events on signal strength. For the intra-handover process, the ANOVA test yielded a highly significant effect on RSSI values (\(p=1.85\times 10^{-11}\)). The [PERSON] post hoc analysis revealed a mean RSSI difference of \(-6.55\,\mathrm{dB}\) between the handover and non-handover conditions, with a 95% confidence interval of \([-8.45,-4.64]\), clearly excluding zero. Similarly, for the inter-handover scenario, the effect on the RSSI was even more pronounced (\(p<2\times 10^{-16}\)), with a mean difference of \(-10.94\,\mathrm{dB}\) and a 95% confidence interval of \([-12.08,-9.80]\). These results confirm that handover processes, especially inter-cell ones have a statistically significant and substantial impact on the received signal strength. Additionally, [PERSON]'s post hoc comparisons between the theoretical and experimental RSSI values indicated statistically significant differences under specific mobility conditions. In the inter-handover zone under the SI-SI scenario (i.e., both theoretical and experimental handover detections were positive), the average difference was \(3.21\,\mathrm{dB}\) (\(p=0.0021\)) with a confidence interval of \([1.17,5.25]\), indicating a moderate but statistically relevant discrepancy. In the NO-NO scenario (no handover detected in either case), the difference increased to \(3.47\,\mathrm{dB}\) (\(p<0.001\)) with a tighter confidence interval of \([3.05,3.87]\), suggesting a consistent and systematic divergence between both models outside of mobility zones.