Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Markov chain existence and Hidden Markov models in spectrum sensing
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
A framework for statistical wireless spectrum occupancy modeling
IEEE Transactions on Wireless Communications
Quickest spectrum detection using hidden Markov model for cognitive radio
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
A sensing policy based on the statistical property of licensed channel in cognitive network
International Journal of Internet Protocol Technology
Opportunistic Spectrum Sensing by Employing Matched Filter in Cognitive Radio Network
CSNT '11 Proceedings of the 2011 International Conference on Communication Systems and Network Technologies
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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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.