Comparison of RF spectrum prediction methods for dynamic spectrum access

Jacob A. Kovarskiy, Anthony F. Martone, Kyle A. Gallagher, Kelly D. Sherbondy, Ram Mohan Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXI
EditorsArmin Doerry, Kenneth I. Ranney
PublisherSPIE
Volume10188
ISBN (Electronic)9781510608771
DOIs
StatePublished - Jan 1 2017
EventRadar Sensor Technology XXI 2017 - Anaheim, United States
Duration: Apr 10 2017Apr 12 2017

Other

OtherRadar Sensor Technology XXI 2017
CountryUnited States
CityAnaheim
Period4/10/174/12/17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Kovarskiy, J. A., Martone, A. F., Gallagher, K. A., Sherbondy, K. D., & Narayanan, R. M. (2017). Comparison of RF spectrum prediction methods for dynamic spectrum access. In A. Doerry, & K. I. Ranney (Eds.), Radar Sensor Technology XXI (Vol. 10188). [1018819] SPIE. https://doi.org/10.1117/12.2262306