| Title: |
Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks |
| Authors: |
Emmanuel Oluwatosin Rabiu; Damilare Oluwole Akande; Zachaeus Kayode Adeyemo; Isaac Akinwale Akanbi; Oluwole Oladele Obanisola |
| Source: |
ABUAD Journal of Engineering Research and Development, Vol 7, Iss 2 (2024) |
| Publisher Information: |
College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| Publication Year: |
2024 |
| Collection: |
Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: |
Cognitive Radio Network; 3-state HMM; Spectrum Prediction; Prediction Accuracy; Probability of Detection; Engineering (General). Civil engineering (General); TA1-2040 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://journals.abuad.edu.ng/index.php/ajerd/article/view/763; https://doaj.org/toc/2756-6811; https://doaj.org/toc/2645-2685; https://doaj.org/article/963f3ebd20b6430eaaa60646fb68419d |
| DOI: |
10.53982/ajerd.2024.0702.40-j |
| Availability: |
https://doi.org/10.53982/ajerd.2024.0702.40-j; https://doaj.org/article/963f3ebd20b6430eaaa60646fb68419d |
| Accession Number: |
edsbas.BFC2AC59 |
| Database: |
BASE |