Three-State Non-Stationary Hidden Markov Model for an Improved Spectrum Inference in Cognitive Radio Networks

Abstract

Spectrum manufacturers, operators and regulators are faced with the challenge of meeting the astronomical increase in demand by spectrum users due to the limited available radio spectrum already fixed for licensed or Primary Users (PUs). The emergence of Cognitive Radio Network (CRN) allows unlicensed or Secondary Users (SUs) to opportunistically access spectrum holes left unused by the PUs through spectrum sensing, management, sharing and mobility functionalities with the aid of algorithms and protocols. However, CRN suffers prolonged delay with negative impact on spectral efficiency. In order to improve the spectral efficiency, spectrum inference was introduced. Yet, inaccurate spectrum inference by existing mechanisms could not solve spectrum underutilization effectively due to persistent false alarm, interference and missed detection of PUs. Two-state Non-Stationary Hidden Markov Model (NSHMM) focused only on idle and busy states of PUs while previous work on three-state Stationary Hidden Markov Model (SHMM) did not consider the time-varying property of channel states obtainable in real scenarios where the state transition probability of a PU is time-varying. This work has proposed three-state NSHMM for spectrum inference in CRNs by formulating its parameters and modelling PU's dwell time distributions to realize the time-varying property of the stochastic PU behavior apart from the fuzzy state that takes care of noisy effects and undetermined or incomplete observations in the existing mechanisms where only idle and busy states were mostly recognized. The performance of the proposed mechanism was evaluated using Probability of Detection (PD), Prediction Accuracy (PA) and Spectrum Utilization Efficiency (SUE). The results were compared to the performance metrics obtained from spectrum inference of existing 2-state NSHMM and 3-state SHMM. The simulation results obtained revealed that the proposed three-state NSHMM spectrum inference mechanism gave the best performance with the highest PD, PA and SUE which curtailed PU collision because of its least possible chances of incorrect detection of primary users and least false alarm. The outstanding performance of the proposed NSHMM was due to its non-stationarity as well as the fuzzy state incorporated in the development of the mechanism. Therefore, the proposed three-state NSHMM for an improved spectrum inference in CRNs has grossly abated PU collision, false alarm and spectrum underutilization.