Hidden Markov model technique for dynamic spectrum access
Keywords: Baum-Welch algorithm Cognitive radio network Dynamic spectrum access Hidden Markov process Markov chain ABSTRACT Dynamic spectrum access is a paradigm used to access the spectrum dynamically. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transi...
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| Veröffentlicht in: | Telkomnika 2020-10, Vol.18 (5), p.2780-2786 |
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| description | Keywords: Baum-Welch algorithm Cognitive radio network Dynamic spectrum access Hidden Markov process Markov chain ABSTRACT Dynamic spectrum access is a paradigm used to access the spectrum dynamically. 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. 1.INTRODUCTION Spectrum sensing is usually done by measuring the power spectral density of the channel of interest in cognitive radio [1] based system to perform the dynamic spectrum access [2] operation. Estimation using maximum likelihood algorithm We also obtained the estimated transition probability and emission probability matrix by using the maximum likelihood (ML) estimate. Table 5 shows the average of estimated transition matrices for both the HMM i.e. HMM1 and HMM2 using maximum likelihood method along with its stationary distribution. |
| doi_str_mv | 10.12928/telkomnika.v18i5.14470 |
| format | Article |
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| subjects | Algorithms Cognitive radio Eigenvalues Emission Markov analysis Markov chains Maximum likelihood estimates Maximum likelihood estimation Maximum likelihood method Parameter estimation Power spectral density Probability Random variables Transition probabilities |
| title | Hidden Markov model technique for dynamic spectrum access |
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