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Proceedings Paper

Comparison of RF spectrum prediction methods for dynamic spectrum access
Author(s): Jacob A. Kovarskiy; Anthony F. Martone; Kyle A. Gallagher; Kelly D. Sherbondy; Ram M. Narayanan
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Paper Abstract

Dynamic spectrum access (DSA) refers to the adaptive utilization of today’s busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

Paper Details

Date Published: 1 May 2017
PDF: 11 pages
Proc. SPIE 10188, Radar Sensor Technology XXI, 1018819 (1 May 2017); doi: 10.1117/12.2262306
Show Author Affiliations
Jacob A. Kovarskiy, The Pennsylvania State Univ. (United States)
Anthony F. Martone, U.S. Army Research Lab. (United States)
Kyle A. Gallagher, U.S. Army Research Lab. (United States)
Kelly D. Sherbondy, U.S. Army Research Lab. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 10188:
Radar Sensor Technology XXI
Kenneth I. Ranney; Armin Doerry, Editor(s)

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