Predictive energy detection for inferring radio frequency activity

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

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

3 Scopus citations

Abstract

Next generation cognitive radar/radio systems rely on dynamic spectrum access (DSA) to adaptively and ef-ficiently utilize the radio frequency (RF) spectrum. Such technology must detect, predict, and avoid channels occupied by RF interference. Conventional spectrum sensing methods may fail to determine signal occupancy states during transition periods. Predicting RF activity reduces the probability of interference during such transition periods and improves the overall efficiency of DSA schemes. This work employs a one-step ahead prediction approach to determine future busy or idle states through linear support vector regression (SVR). Supervised learning forecasts future signal energy which then acts as a decision statistic to determine occupancy in a sub-band of interest. The scheme's prediction accuracy is evaluated with respect to input signal-to-noise ratio (SNR) and RF activity as a function of mean busy/idle time. Generalizing RF activity as an alternating renewal process allows exponential random variables to generate simulated data for SVR training and testing. The results show that this approach predicts RF activity with high accuracy over various signal traffic statistics and SNRs. Prediction accuracy is also evaluated with respect to the expected busy/idle transitions given activity statistics.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXII
EditorsArmin Doerry, Kenneth I. Ranney
PublisherSPIE
ISBN (Electronic)9781510617773
DOIs
StatePublished - Jan 1 2018
EventRadar Sensor Technology XXII 2018 - Orlando, United States
Duration: Apr 16 2018Apr 18 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10633
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherRadar Sensor Technology XXII 2018
CountryUnited States
CityOrlando
Period4/16/184/18/18

    Fingerprint

All Science Journal Classification (ASJC) codes

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

Cite this

Kovarskiy, J. A., Martone, A. F., Gallagher, K. A., Sherbondy, K. D., & Narayanan, R. M. (2018). Predictive energy detection for inferring radio frequency activity. In A. Doerry, & K. I. Ranney (Eds.), Radar Sensor Technology XXII [1063318] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10633). SPIE. https://doi.org/10.1117/12.2305857