Abstract
This paper explores the learning capability of hidden Markov model (HMM) in capturing the temporal correlation and predicting primary user (PU) activity pattern of real spectrum data of GSM-900 band through an USRP-LabVIEW platform for cognitive radio (CR) systems. The inability of the widely used stationary Markov model in estimating the occupancy pattern of primary channels for a long duration of time has been verified. We proposed an alternative duty cycle (DC)–based two-state discrete-time Markov chain (DTMC-DC) model. Analysis of empirical data indicates that DC required for a non-stationary DTMC-DC model can be well approximated by a trapezoidal shape and the PU spectrum usage pattern estimated using DTMC-DC is capable of learning the statistical behavior (length of idle and busy interval periods) of a real channel accurately with a reduced complexity.
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