IRO Journal on Sustainable Wireless Systems
IRO Journals
Volume 2 — Issue 2 — June 2020

Machine Learning Implementation in Cognitive Radio Networks with Game-Theory Technique

https://doi.org/10.36548/jsws.2020.2.002
Jennifer S. Raj
Department of ECE, Gnanamani College of Technology
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How to Cite

Raj, Jennifer S. 2020. “Machine Learning Implementation in Cognitive Radio Networks With Game-Theory Technique”. IRO Journal on Sustainable Wireless Systems 2 (2): 68-75. https://doi.org/10.36548/jsws.2020.2.002.
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Keywords

— Regression
— Game Theory
— Queuing Theory
— Spectrum Allocation
— Cognitive Networks
Published: 18-05-2020

Abstract

Significant enhancement of spectrum utilization can be performed by means of Cognitive Radio technology. A game theory based Cognitive Radio Network with Dynamic Spectrum Allocation model is proposed in this paper. M|M|1 queuing model is implemented along with Preemptive Resume Priority for accommodation of all the cases. An Incremental Weights-Decremental Ratios (IW-DR) algorithm based on priority-based scheduling is used for supplementing this theory. Regression models are used for restructuring and improving the efficiency of the system.

References

  1. Haoxiang, W. (2019). MULTI-OBJECTIVE OPTIMIZATION ALGORITHM FOR POWER MANAGEMENT IN COGNITIVE RADIO NETWORKS. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 1(02), 97-109.
  2. Darney, P. E., & Jacob, I. J. (2019). PERFORMANCE ENHANCEMENTS OF COGNITIVE RADIO NETWORKS USING THE IMPROVED FUZZY LOGIC. Journal of Soft Computing Paradigm (JSCP), 1(02), 57-68.
  3. Valanarasu, M. R., & Christy, A. (2019). COMPREHENSIVE SURVEY OF WIRELESS COGNITIVE AND 5G NETWORKS. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 23-32.
  4. Pandian, M. D. (2019). ENHANCED NETWORK PERFORMANCE AND MOBILITY MANAGEMENT OF IOT MULTI NETWORKS. Journal of trends in Computer Science and Smart technology (TCSST), 1(02), 95-105.
  5. Raj, J. S., & Smys, S. (2019). VIRTUAL STRUCTURE FOR SUSTAINABLE WIRELESS NETWORKS IN CLOUD SERVICES AND ENTERPRISE INFORMATION SYSTEM. Journal of ISMAC, 1(03), 188-205.
  6. Isaac, M. (2017). Machine learning prediction algorithm to determine best performing routes in cognitive radio networks (Doctoral dissertation, Uganda Technology And Management University).
  7. Mangairkarasi, S., Sarankapani, R., & Arivudainambi, D. (2020). A Game-Theoretic Approach for Cognitive Radio Networks using Machine Learning Techniques (No. 2313). EasyChair.
  8. Bharathi, S., Kumar, D., & Ram, D. (2018). Defence against responsive and non-responsive jamming attack in cognitive radio networks: an evolutionary game theoretical approach. The Journal of Engineering, 2018(2), 68-75.
  9. Qadir, J. (2016). Artificial intelligence based cognitive routing for cognitive radio networks. Artificial Intelligence Review, 45(1), 25-96.
  10. Jaishanthi, B., Ganesh, E. N., & Sheela, D. (2019). Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network. Automatika, 60(5), 564-569.
  11. Kwasinski, A., Wang, W., & Mohammadi, F. S. (2020). Reinforcement Learning for Resource Allocation in Cognitive Radio Networks. Machine Learning for Future Wireless Communications, 27-44.
  12. Gupta, R., & Gupta, P. C. (2017). COGNITIVE RADIO NETWORKS IMPLEMENTATION FOR SPECTRUM UTILIZATION IN HADOTI (RAJASTHAN) REGION. VINDHYA BHARTI.
  13. Umbert, A., Sallent, O., Pérez-Romero, J., Sánchez-González, J., Collins, D., & Kist, M. (2018, May). An experimental assessment of channel selection in cognitive radio networks. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 78-88). Springer, Cham.
  14. Tuberquia, M., & Hernandez, C. (2018). New Approaches in cognitive radios using evolutionary algorithms. International Journal of Electrical and Computer Engineering, 8(3), 1636.
  15. Roumeliotis, A. J., Poulakis, M. I., Vassaki, S., & Panagopoulos, A. D. (2017). Radio Resources Management Optimization in Cognitive Radio Networks. New Directions in Wireless Communications Systems: From Mobile to 5G, 433.