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

Authors: Deepak C. Karia; B.K. Lande; R.D. Daruwala

Addresses: Department of Electronics and Telecommunication, S.P.I.T., University of Mumbai, Mumbai 400058, India ' Department of Electronics, S.A.K.E.C., University of Mumbai, Mumbai 400088, India ' Department of Electronics, V.J.T.I., University of Mumbai, Mumbai 400019, India

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.

Keywords: 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.

DOI: 10.1504/IJAHUC.2012.050439

International Journal of Ad Hoc and Ubiquitous Computing, 2012 Vol.11 No.4, pp.206 - 213

Received: 25 Jul 2011
Accepted: 16 Nov 2011
Published online: 22 Nov 2012 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article