Prediction of Radio Frequency Spectrum Occupancy
| Autor: | Marvin A. Conn, Manohar Mareboyana, Darsana P. Josyula, Hubert Kyeremateng-Boateng |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science 010401 analytical chemistry Feature extraction 020206 networking & telecommunications Higher-order statistics 02 engineering and technology 01 natural sciences Radio spectrum 0104 chemical sciences Support vector machine 0202 electrical engineering electronic engineering information engineering Wireless Radio frequency business Algorithm Energy (signal processing) Communication channel |
| Zdroj: | TrustCom |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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