Published July 14, 2022 | Version v1
Journal article Open

A Study on the Performance of GA-Holt-Winters Model in 900 MHz Spectrum Prediction

Creators

  • 1. Department of Electrical/Electronic Engineering, The Federal Polytechnic Offa, Offa, Kwara State, Nigeria
  • 2. Jamola Technologies, Offa, Kwara State, Nigeria

Description

Abstract: Continuous spectrum measurement is expensive and time consuming. This has necessitated the concept of spectrum prediction. Spectrum prediction uses historically observed data from spectrum sensing to forecast future channel states. In this research the suitability of the genetic algorithm modified Holt-Winters exponential model in the prediction of spectrum occupancy data was investigated. Minute spectrum duty cycle of selected locations in Ilorin, Nigeria was used in the evaluation of the forecast behaviour of the methods. It was observed that GA-Holt-Winter technique gave lower forecast values as evaluated from the “Mean Square Error (MSE)” serving “as objective function” in comparison with Holt-Winters method. The Holt-Winters method and GA-Holt-Winters technique were observed to be of good forecast behaviour with GSM 900 RL for both location. There was about 16% decrease in the MSE of GA-Holt-Winters technique compared to Holt-Winters in the GSM 900 RL for both locations. Finally, there was about 28% and 8% decrease in the MSE of GA-Holt-Winters technique compared to Holt-Winters in the GSM 900 RL for locations 1 and 2 respectively.

Keywords: Cognitive radio network, genetic algorithm, Holts-Winters exponential smoothing, spectrum occupancy, spectrum measurement, spectrum prediction.

Title: A Study on the Performance of GA-Holt-Winters Model in 900 MHz Spectrum Prediction

Author: Frederick Ojiemhende Ehiagwina, Lateef Olashile Afolabi, Khadijat Mustapha, Kabirat Raheem, Anifowose Jamiu Jibola, Wasiu O. Salaudeen

International Journal of Novel Research in Electrical and Mechanical Engineering

ISSN 2394-9678

Vol. 9, Issue 1, September 2021 - August 2022

Page No: 23-31

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 14-July-2022

DOI: https://doi.org/10.5281/zenodo.6832380

Paper Download Link (Source)

https://www.noveltyjournals.com/upload/paper/A%20Study%20on%20the%20Performance-14072022-3.pdf

Notes

International Journal of Novel Research in Electrical and Mechanical Engineering, ISSN 2394-9678, Novelty Journals, Website: www.noveltyjournals.com

Files

A Study on the Performance-14072022-3.pdf

Files (1.6 MB)

Name Size Download all
md5:50d154b7124eaa25559cfd187672f8dc
1.6 MB Preview Download

Additional details

Related works

Is derived from
Journal article: 2394-9678 (ISSN)
Is published in
Journal article: https://www.noveltyjournals.com/upload/paper/A%20Study%20on%20the%20Performance-14072022-3.pdf (URL)

References

  • [1] Amzi,N.L.B.M. (2013) "Parameters estimation of Holt-Winter smoothing method using genetic algorithm". Mater of Science Dissertation, "Faculty of Science, Universiti Teknologi Malaysia".
  • [2] Bütün, İ., Talay, A. Ç., Altilar, D. T., Khalid, M., & Sankar, R. (2010). Impact of mobility prediction on the performance of cognitive radio networks. In Wireless Telecommunications Symposium (WTS) (pp. 1-5)". IEEE.
  • [3] Chiroma, H., Abdul-kareem, S., Noor, A. S. M., Abubakar, A. I., Safa, N. S., Shuib, L., Hamza, M. F., Gital, A. Y. & Herawan, T". (2016). "A review on artificial intelligence methodologies for the forecasting of crude oil price". Intelligent Automation & Soft Computing, 1-14.
  • [4] ITU-R SM.2256-1 (2016). Spectrum occupancy measurements and evaluation. Spectrum Management Series (pp. 1–53). International Telecommunication Union
  • [5] Jacob, J., Jose, B. R., & Mathew, J. (2014, September). "Spectrum prediction in cognitive radio networks: A bayesian approach. In 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies (pp. 203-208). IEEE".
  • [6] Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based based model for optimizating bank lending decisions. Expert Systems with Applications, 80, 75-82.
  • [7] Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2008). Introduction to time series analysis and forecasting. In Wiley Series in Probability and Statistics. Hoboken. New Jersey. John Wiley & Sons Publication.
  • [8] Peng, S., Qin, S., Li, G. (2019). Predicting expressway subsidence based on niching genetic algorithm and Holt–Winters model. Arabian Journal of Geosciences, 12(354), 1-10.
  • [9] Safari, N., Chung, C. Y., & Price, G. C. D. (2017). Novel multi-step short-term wind power prediction framework based on chaotic time series analysis and singular spectrum analysis. IEEE Transactions on Power Systems, 33(1), 590-601.
  • [10] Yang, J., & Zhao, H. (2015). Enhanced throughput of cognitive radio networks by imperfect spectrum prediction. IEEE Communications Letters, 19(10), 1738-1741.
  • [11] Zhang, T., Wang, J., Liu, Q., Zhou, J., Dai, J., Han, X., ... & Xu, K. (2019). Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks. Photonics Research, 7(3), 368-380.
  • [12] Zhao, Y., Hong, Z., Wang, G., & Huang, J. (2016, August). High-order hidden bivariate Markov model: A novel approach on spectrum prediction. In 2016 25th International Conference on Computer Communication and Networks (ICCCN) (pp. 1-7). IEEE.

Subjects

Paper Download Link (Source)
https://www.noveltyjournals.com/upload/paper/A%20Study%20on%20the%20Performance-14072022-3.pdf