abstract
- AuthorHow do you know you select enough tuning dataset from measurements to guarantee model prediction accuracy Tuning datasets are often selected based on simple random sampling with predefined rates. Usually, these rates are determined as a/b, where a% of the data goes to training, and the remaining b% goes to testing. But it is not clear to what extent tuning dataset in order to minimize the estimation path loss errors. It is thus required to analyze the performance of channel modeling by selectingamong all measurement samplesappropriate tuning dataset. Using radio measurements and deterministic Ray Launching techniques to collect enough reliable samples, this study analyzes the impact of tuning dataset selectionexpressed in terms of the mean absolute error and coston a novel Kriging- aided in-building measurement-based path loss prediction model.