# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 5 Conclusions  
The vector calculus-based analysis enabled the derivation of an estimation equation capable of predicting power levels at a given geographic coordinate with an accuracy of approximately 95%. This prediction, which relies on key parameters such as the mobile terminal's movement direction and network characteristics, significantly reduces the need for extensive field measurements in these environments. By applying vector calculus, it was possible to estimate the received power at a specific location based on the mobile terminal's movement direction, defined by the angle \(\theta\) and the traveled distance. This approach proved effective for analysis within the horizontal plane. The present study introduces a theoretical model grounded in vector calculus to estimate the received power at a point, given the known power at the preceding location and the direction of the mobile terminal's movement. Estimating the received power at subsequent locations based on terminal movement is valuable, as it enables the determination of the terminal's position within the coverage area and supports applications such as signal arrival direction assessment, identification of handover zones, and detection of reception issues. These capabilities can be leveraged to optimize network coverage through adaptive handover strategies and targeted enhancement interventions. The model's performance was evaluated in both urban and suburban environments, with estimation errors observed to be below 7.6%. Given the potential for improvement, future work will consider additional factors, including antenna radiation characteristics such as horizontal and vertical gains, as well as propagation conditions specific to the studied environments. For a more comprehensive analysis that incorporates vertical dimensions, future studies should also include parameters like the electrical and mechanical tilt of antennas. Furthermore, the integration of automation techniques and machine learning algorithms presents opportunities to enhance model accuracy and adaptability. By training models on extensive empirical datasets and incorporating real-time measurement feedback, it will be possible to develop more precise and dynamic power estimation frameworks. Conceptualization, P.L.-M.; Methodology, P.L.-M.; Validation, P.L.-M., R.A. and R.L. ; Formal analysis, K.V.M., P.L.-M. and R.C. ; Data curation, K.V.M. and R.C. ; Writing--review & editing, G.P. All authors have read and agreed to the published version of the manuscript. * (19) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Survey of 5G Channel Measurements and Models. _IEEE [PERSON]. Surv. Tutor._**2021**, _24_, 3142-3168[PERSON] [CrossRef]
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