Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks
The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum...
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| Format: | Article |
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College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2024-10-01
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| Series: | ABUAD Journal of Engineering Research and Development |
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| Online Access: | https://journals.abuad.edu.ng/index.php/ajerd/article/view/763 |
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| author | Emmanuel Oluwatosin Rabiu Damilare Oluwole Akande Zachaeus Kayode Adeyemo Isaac Akinwale Akanbi Oluwole Oladele Obanisola |
| author_facet | Emmanuel Oluwatosin Rabiu Damilare Oluwole Akande Zachaeus Kayode Adeyemo Isaac Akinwale Akanbi Oluwole Oladele Obanisola |
| author_sort | Emmanuel Oluwatosin Rabiu |
| collection | DOAJ |
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The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.
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| format | Article |
| id | doaj-art-963f3ebd20b6430eaaa60646fb68419d |
| institution | OA Journals |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-963f3ebd20b6430eaaa60646fb68419d2025-08-20T02:28:00ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852024-10-017210.53982/ajerd.2024.0702.40-j638Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio NetworksEmmanuel Oluwatosin Rabiu0Damilare Oluwole Akande1Zachaeus Kayode Adeyemo2Isaac Akinwale Akanbi3Oluwole Oladele Obanisola4Electronic and Electrical Engineering Department, Ladoke Akintola University of Technology, Ogbomosho, NigeriaElectronic and Electrical Engineering Department, Ladoke Akintola University of Technology, Ogbomosho, NigeriaElectronic and Electrical Engineering Department, Ladoke Akintola University of Technology, Ogbomosho, NigeriaNigerian Communications Commission, Abuja, NigeriaDepartment of Electrical and Electronic Engineering, Ajayi Crowther University, Oyo, Nigeria The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy. https://journals.abuad.edu.ng/index.php/ajerd/article/view/763Cognitive Radio Network3-state HMMSpectrum PredictionPrediction AccuracyProbability of Detection |
| spellingShingle | Emmanuel Oluwatosin Rabiu Damilare Oluwole Akande Zachaeus Kayode Adeyemo Isaac Akinwale Akanbi Oluwole Oladele Obanisola Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks ABUAD Journal of Engineering Research and Development Cognitive Radio Network 3-state HMM Spectrum Prediction Prediction Accuracy Probability of Detection |
| title | Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks |
| title_full | Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks |
| title_fullStr | Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks |
| title_full_unstemmed | Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks |
| title_short | Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks |
| title_sort | three state hidden markov model for spectrum prediction in cognitive radio networks |
| topic | Cognitive Radio Network 3-state HMM Spectrum Prediction Prediction Accuracy Probability of Detection |
| url | https://journals.abuad.edu.ng/index.php/ajerd/article/view/763 |
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