Spectrum cartography using adaptive radial basis functions: Experimental validation

Abstract

In this paper, we experimentally validate the functionality of a developed algorithm for spectrum cartography using adaptive Gaussian radial basis functions (RBF). The RBF are strategically centered around representative centroid locations in a machine learning context. We assume no prior knowledge about neither the power spectral densities (PSD) of the transmitters nor their locations. Instead, the received signal power at each location is estimated as a linear combination of different RBFs. The weights of the RBFs, their Gaussian decaying parameters and locations are jointly optimized using expectation maximization with a least squares loss function and a quadratic regularizer. The performance of adaptive RBFs based spectrum cartography is shown through measurements using a universal software radio peripheral, a customized node and LabView framework. The obtained results verify the ability of adaptive RBF to construct spectrum maps with an acceptable performance measured by normalized mean square error (NMSE).

Spectrum cartography based on dynamic compressed sensing by using multiple domains information

“…S PECTRUM cartography [1], [2] has been introduced to construct radio maps [3], [4] of a certain channel metric [5], such as power spectral density (PSD), received signal power (RSS) or channel gain over a geographical region of interest based on measurements collected by the radio frequency (RF) sensors deployed within the region [1] for years. Since radio maps comprise the critical information [6], [7] of RF environment across multiple domains [8], e.g., space, frequency, and time, they have been widely utilized in applications of wireless communications including network planning, interference coordination, dynamic spectrum access, and spectrum surveillance.…”

Compressive Multispectral Spectrum Sensing for Spectrum Cartography

“…In [ 5 , 6 , 7 , 8 ], the authors introduced compressive sensing (CS) techniques for the building of IM. In their works, a central manager constructs the maps by CS techniques rather than interpolation techniques, taking advantage of the sparsity in the model of location of the sensors.…”

“…This amount of data requires the development of reduction data strategies that facilitates all the computational processes involved in the construction of these maps. In this sense, several works have used CS techniques as reduction data strategy [ 5 , 6 , 7 , 8 ], in the context of cartography. These works have focused only on the spatial sparsity.…”

Experimental validation for spectrum cartography using adaptive multi-kernels

“…An initial validation is performed in [15] using sequential measurements, where a transmitter is in a fixed position and a receiver is moved to 100 different positions. At each position an average of the received signal strength over a short time period is stored along with the coordinates.…”