Experimental validation for spectrum cartography using adaptive multi-kernels

This paper validates the functionality of an algorithm for spectrum cartography, generating a radio environment map (REM) using adaptive radial basis functions (RBF) based on a limited number of measurements. The power at all locations is estimated as a linear combination of different RBFs without assuming any prior information about either power spectral densities (PSD) of the transmitters or their locations. The RBFs are represented as centroids at optimized locations, using machine learning to jointly optimize their positions, weights and Gaussian decaying parameters. Optimization is performed using expectation maximization with a least squares loss function and a quadratic regularizer. Measurements from 14 receivers, randomly divided into 2 sets, are used for training and validating the algorithm. Estimations are compared to the validation set by means of normalized mean square error (NMSE), and the obtained results verify the functionality of the algorithm.