Prediction of Radio Frequency Spectrum Occupancy

As more users access the radio frequency (RF) spectrum for wireless communications, spectrum availability is becoming an increasingly scarce resource. Hence, the ability to detect or predict when a spectrum channel is available for use is of great importance. To support autonomous access to the spectrum band, we research two feature extraction techniques: (i) based on the standard energy calculation and (ii) based on cumulant calculations. We compare the performance of a baseline reactive predictor which projects the current time-step values to the next time-step, against linear support vector regression (SVR) based approaches using the aforementioned feature extraction techniques. We evaluate the occupancy state prediction in a spectrum band for different values of signal-to-noise ratio (SNR) and spectrum RF activity, using simulated RF signal data. Our experiments indicate that using first order cumulant based approach with SVR improves prediction accuracy.