Performance analysis of HMM– and ANN–based spectrum vacancy predictor behaviour for cognitive radios. (27th November 2012)

• Record Type: Journal Article
• Authors: Karia, Deepak C.; Lande, B.K.; Daruwala, R.D.
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 11:Number 4(2012)
• Page Start: 206
• Page End: 213
• Publication Date: 2012-11-27
• Subjects: cognitive radios -- channel prediction -- HMM -- hidden Markov models -- Baum–Welch algorithm -- Viterbi algorithm -- real–time data -- distributed data -- artificial neural networks -- ANNs -- backpropagation algorithms -- performance evaluation -- spectrum vacancy predictor -- channel usage patterns -- channel occupancy states; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

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.