A novel scheme for spectrum prediction in cognitive radio networks / Mehdi Askari and Rezvan Dastanian
An efficient spectrum prediction model is presented to improve the spectrum utilization in cognitive radio network. In this model, a novel improved version of Teaching-Learning-Based-Optimization algorithm, also referred to iTLBO algorithm, is proposed to train a feed forward artificial neural netwo...
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| 主要な著者: | , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Universiti Teknologi MARA
2021
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| 主題: | |
| オンライン･アクセス: | https://ir.uitm.edu.my/id/eprint/52056/1/52056.pdf https://ir.uitm.edu.my/id/eprint/52056/ https://jeesr.uitm.edu.my/ |
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| 要約: | An efficient spectrum prediction model is presented to improve the spectrum utilization in cognitive radio network. In this model, a novel improved version of Teaching-Learning-Based-Optimization algorithm, also referred to iTLBO algorithm, is proposed to train a feed forward artificial neural network (ANN). The performance of the proposed iTLBO-ANN model is compared with some hybrid prediction models, including the genetic algorithm with ANN (GA-ANN), the firefly algorithm with ANN (FF-ANN), and the conventional TLBO algorithm with ANN (TLBO- ANN). Performance evaluation via a real-word spectrum data set (GSM-900) confirms that iTLBO-ANN outperforms other spectrum prediction schemes in terms of prediction error and prediction efficiency. |
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