TELKOMNIKA Telecommunication, Computing, Electronics and Control
Hidden Markov model technique for dynamic spectrum access
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Hidden Markov model technique for dynamic spectrum access
Hidden Markov model technique for dynamic spectrum access
Subject
Baum-Welch algorithm, Cognitive radio network, Dynamic spectrum access, Hidden Markov process, Markov chain
Description
Dynamic spectrum access is a paradigm used to access the spectrum
dynamically. A hidden Markov model (HMM) is one in which you
observe a sequence of emissions, but do not know the sequence of states, the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods.
dynamically. A hidden Markov model (HMM) is one in which you
observe a sequence of emissions, but do not know the sequence of states, the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods.
Creator
Jayant P. Pawar, Prashant V. Ingole
Source
DOI: 10.12928/TELKOMNIKA.v18i5.14470
Publisher
Universitas Ahmad Dahlan
Date
October 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Language
English
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Jayant P. Pawar, Prashant V. Ingole, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Hidden Markov model technique for dynamic spectrum access,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4071.
Hidden Markov model technique for dynamic spectrum access,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4071.