George Mason University
George Mason University Mason
George Mason University

Spectrum sensing using a hidden bivariate Markov model

by Brian Mark / Yariv Ephraim

Publication Details MORE LESS

  • Published Date: September 1, 2013
  • Volume/Issue: 12/9
  • Publisher: IEEE


A new statistical model, in the form of a hidden bivariate Markov chain observed through a Gaussian channel, is developed and applied to spectrum sensing for cognitive radio. We focus on temporal spectrum sensing in a single narrowband channel in which a primary transmitter is either in an idle or an active state. The main advantage of the proposed model, compared to a standard hidden Markov model (HMM), is that it allows a phase-type dwell time distribution for the process in each state. This distribution significantly generalizes the geometric dwell time distribution of a standard HMM. Measurements taken from real data confirm that the geometric dwell time distribution characteristic of the HMM is not adequate for this application. The Baum algorithm is used to estimate the parameter of the proposed model and a forward recursion is applied to online estimation and prediction of the state of the cognitive radio channel. The performance of the proposed model and spectrum sensing approach are demonstrated using numerical results derived from real spectrum measurement data.

Other Contributors

Thao Nguyen