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Interference-Aware QoS Guarantees in OFDM-Based Cognitive Radio Networks Based on Branch and Bound

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Abstract

In the past decade, the OFDM access method has been widely used in different types of networks. Indeed; OFDM is the technology of choice for all major wireless systems, including WIFI, WiMAX, 3G, 4G and 5G. In this paper, we are interested in its application within a cognitive radio networks. The main objective is to provide an acceptable quality of service for the secondary user while minimizing interference with the primary user. This problem has been formulated in the literature in the form of a multi-objective function with three modes of communication (multimedia, reliable and low battery). In this paper, we exploit the efficiency of the bounding operators of the branch and bound method in order to solve this problem. The simulation results showed the effectiveness of our proposal by comparing it with the cuckoo search algorithm which has already been validated in the literature for this type of problem. Our proposal surpasses the cuckoo search algorithm for two modes of communication in terms of fitness and execution time.

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References

  1. Hosseinzadeh Aghdam, M., & Sharifi, A. A. (2021). A novel ant colony optimization algorithm for PAPR reduction of OFDM signals. International Journal of Communication Systems, 34(1), e4648.

    Google Scholar 

  2. Patel, V., & Warhade, K. (2021). An Improved Carrier Frequency Offset Estimation Under Narrowband Interference in OFDM Cognitive Radio. Advances in Signal and Data Processing (pp. 531–544).

  3. El Bahi, F. Z., Ghennioui, H., & Zouak, M. (2019). Spectrum sensing technique of OFDM signal under noise uncertainty based on Mean Ambiguity Function for Cognitive Radio. Physical Communication, 33, 142–150.

    Article  Google Scholar 

  4. Kumar, M., & Majhi, S. (2019). Joint signal detection and synchronization for OFDM based cognitive radio networks and its implementation. Wireless Networks, 25(2), 699–712.

    Article  Google Scholar 

  5. Meylani, L., Kurniawan, A., & Arifianto, M. S. (2019). Radio resource allocation with the fairness metric for low density signature OFDM in underlay cognitive radio networks. Sensors, 19(8), 1921.

    Article  Google Scholar 

  6. Thangaraj, C. A., & Aruna, T. (2019). Energy-efficient power allocation with guaranteed QoS under imperfect sensing for OFDM-based heterogeneous cognitive radio networks. Wireless Personal Communications, 109(3), 1845–1862.

    Article  Google Scholar 

  7. Shah, H. A., & Koo, I. (2018). A novel physical layer security scheme in OFDM-based cognitive radio networks. IEEE Access, 6, 29486–29498.

    Article  Google Scholar 

  8. Varade, P., Wabale, A., Yerram, R., & Jaiswal, R. (2018). Throughput Maximization of Cognitive Radio Multi Relay Network with Interference Management (2088–8708). International Journal of Electrical & Computer Engineering, 8(4), 2230–2238.

    Google Scholar 

  9. Saoucha, N. A., & Benmammar, B. (2019). Bio-inspired approaches for OFDM-based cognitive radio. International Journal of Internet Protocol Technology, 12(2), 61–75.

    Article  Google Scholar 

  10. Tuan, P. V., Viet, P., & Koo, I. (2016). Throughput maximisation by optimising detection thresholds in full-duplex cognitive radio networks. IET Communications, 10(11), 1355–1364.

    Article  Google Scholar 

  11. Benmammar, B., Benmouna, Y., & Krief, F. (2019). A Pareto optimal multi-objective optimisation for parallel dynamic programming algorithm applied in cognitive radio ad hoc networks. International Journal of Computer Applications in Technology, 59(2), 152–164.

    Article  Google Scholar 

  12. Benmammar, B., et al. (2017). A parallel implementation on a multi-core architecture of a dynamic programming algorithm applied in cognitive radio ad hoc networks. International Journal of Communication Networks and Information Security, 9(2), 196.

    Google Scholar 

  13. Benmouna, Y., Benazzouz, M., Chikh, M. A., & Mahmoudi, S. (2019). New Method for Bayesian Network Learning. International Journal of Pattern Recognition and Artificial Intelligence, 33(2), 1959005.

  14. Benmouna, Y., Mezmaz, M. S., Mahmoudi, S., & Chikh, M. A. (2020). Parallel cycle-based branch-and-bound method for Bayesian network learning. Pattern Analysis and Applications, 23, 897–911.

  15. Saoucha, N. A., & Benmammar, B. (2017). Adapting radio resources in multicarrier cognitive radio using discrete firefly approach. International Journal of Wireless and Mobile Computing, 13(1), 39–44.

    Article  Google Scholar 

  16. Newman, T. R., Barker, B. A., Wyglinski, A. M., Agah, A., Evans, J. B., & Minden, G. J. (2007). Cognitive engine implementation for wireless multicarrier transceivers. Wireless Communications and Mobile Computing, 7(9), 1129–42.

    Article  Google Scholar 

  17. Yang, X. S., & Deb, S. (2017). Cuckoo search: state-of-the-art and opportunities. In 2017 IEEE 4th international conference on soft computing & machine intelligence (ISCMI) (pp. 55–59). IEEE.

  18. Darwin, C. (1859). On the origin of species by means of natural selection, or, the preservation of favoured races in the struggle for life. J. Murray.

  19. Kennedy, J., & Eberhart, R. C. Particle Swarm Optimization . In Proceedings of the IEEE international conference on neural networks IV (pp. 1942–1948), Perth, Australia, November (1995).

  20. Tilahun, S. L., & Ngnotchouye, J. M. T. (2017). Firefly algorithm for discrete optimization problems: A survey. KSCE Journal of Civil Engineering, 21(2), 535–545.

    Article  Google Scholar 

  21. Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Springer, Berlin, Heidelberg.

  22. Kingsbury, N. (2020) Approximation Formulae for the Gaussian Error Integral, Q(x). http://cnx.org/content/m11067/latest/. Accessed January (2020).

  23. Newman, T. R. (2008). Multiple objective fitness functions for cognitive radio adaptation (Doctoral dissertation, University of Kansas).

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Correspondence to Youcef Benmouna.

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Benmouna, Y., Benmammar, B. Interference-Aware QoS Guarantees in OFDM-Based Cognitive Radio Networks Based on Branch and Bound. Wireless Pers Commun 120, 169–183 (2021). https://doi.org/10.1007/s11277-021-08440-0

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