A UHF Band, Multiple Environment Path Loss Dataset

 Accurate path loss models are fundamental to good wireless communication network design. Machine Learning models have demostrated better prediction accuracies than the Empirical models and Deterministic models. However, Empirical and Deterministic models have specified range of parameters in which they are valid. This feature makes Empirical models to be used in Radio Frequency Planning softwares due to flexibility and less computational complexity when compared with Deterministic models.  Machine Learning models are most of the  time developed taking into consideration fixed parameter values interms of frequencies and antenna heights, which limits their incoorporation in Radio Frequency Planning softwares due to low user flexibiliity even though they have the best accuracy. This dataset is aimed for developing a multiple parameter path loss model. The parameters considered are frequency, antenna height, and environment, so as to improve flexibility.