Performance analysis of HMM- and ANN-based spectrum vacancy predictor behaviour for cognitive radios

Cognitive Radios (CRs) are devices, which should be cognizant about the Spectrum Holes upon which the idea of a CR resides, which relates to the sensing and channel management function of the device. CR, therefore, must employ channel prediction techniques so as to decide the usage of channel and also prevents interference with the primary users (or incumbent users). In this paper, we use HMM to predict the channel usage patterns and to determine the channel occupancy states. We make use of BWA to train the parameters of the HMM model, Viterbi algorithm to estimate the hidden state of the channel and BWFA to predict the next state of the sequence. In addition to this, we compare the performance of the HMM-based model with that of a neural network-based predictor, which employs a time-delay feed forward network and which uses back propagation algorithm for training.

[1]  Yuanan Liu,et al.  Optimisation of cooperative spectrum sensing in cognitive radio network , 2009, IET Commun..

[2]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[3]  Dusit Niyato,et al.  A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio , 2010, 2010 IEEE International Conference on Communications.

[4]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[5]  J. Nicholas Laneman,et al.  Sequence Detection Algorithms for Dynamic Spectrum Access Networks , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[6]  Ersoy Öz.  Algorithms of Hidden Markov Model and a Prediction Method on Product Preferences , 2010 .

[7]  Zhen Hu,et al.  Quickest spectrum detection using hidden Markov Model for cognitive radio , 2009, MILCOM 2009 - 2009 IEEE Military Communications Conference.

[8]  Dharma P. Agrawal,et al.  Markov chain existence and Hidden Markov models in spectrum sensing , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[9]  T. Smithers,et al.  Directed By: , 2005 .

[10]  Ghanshyam Singh,et al.  Opportunistic Spectrum Sensing by Employing Matched Filter in Cognitive Radio Network , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[11]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[12]  Hüseyin Arslan,et al.  Binary Time Series Approach to Spectrum Prediction for Cognitive Radio , 2007, 2007 IEEE 66th Vehicular Technology Conference.

[13]  S. N. Sivanandam,et al.  Principles of soft computing , 2011 .

[14]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[15]  Xin Liu,et al.  Optimal Bandwidth Selection in Multi-Channel Cognitive Radio Networks: How Much is Too Much? , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  Dan Guo,et al.  A sensing policy based on the statistical property of licensed channel in cognitive network , 2010, Int. J. Internet Protoc. Technol..

[18]  Dharma P. Agrawal,et al.  A framework for statistical wireless spectrum occupancy modeling , 2010, IEEE Transactions on Wireless Communications.

[19]  W.H. Tranter,et al.  Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case , 2007, Proceedings 2007 IEEE SoutheastCon.