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An optimized spectrum sharing scheme for cognitive radio

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Abstract

In this paper, the spectrum sharing problem in cognitive radio is studied as a maximization optimization problem. First, two different optimization models are represented to maximize utilization and fairness in spectrum sharing. Then, an improved Teaching–Learning-Based-Optimization (iTLBO) algorithm is proposed and applied to solve the spectrum sharing optimization problem. Evaluated by 10 benchmark functions, the proposed iTLBO algorithm shows best optimization performance than the other optimization algorithms on most benchmark functions. Finally, the simulation results validate that the proposed spectrum sharing scheme improves both the spectrum utilization and fairness compared to other existing methods.

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References

  1. Panda, T., Patra, K. C., Alzubi, J. A., Barpanda, N. K., & Palai, G. (2019). Analytical studies on FBG based SOI structure for realization of optical interconnect. Optik - International Journal of Light and Electron Optics., 193, 162979.

    Article  Google Scholar 

  2. Amiri, I. S., Palai, G., Alzubi, J. A., & Nayak, S. R. (2020). Chip to chip communication through the photonic integrated circuit: A new paradigm to optical VLSI. Optik - International Journal of Light and Electron Optics., 202, 163588.

    Article  Google Scholar 

  3. Jin, Z., Yao, K., Lee, B., Cho, J., & Zhang, L. (2019). Channel status learning for cooperative spectrum sensing in energy-restricted cognitive radio networks. IEEE Access, 7, 64946–64954.

    Article  Google Scholar 

  4. Mitola, J., & Maguire, J. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.

    Article  Google Scholar 

  5. Zhao, Z., Peng, Z., Zheng, S., & Shang, J. (2009). Cognitive radio spectrum allocation using evolutionary algorithms. IEEE Transactions on Wireless Communications, 8, 4421–4425.

    Article  Google Scholar 

  6. Zheng H, Peng C (2005), Collaboration and fairness in opportunistic spectrum access. In Proc. 40th annual IEEE International Conference on Communications (ICC), pp 3132–3136

  7. Tegou, T. L., et al. (2018). Spectrum allocation in cognitive radio networks using chaotic biogeography-based optimization. IET Networks, 7, 328–335.

    Article  Google Scholar 

  8. Peng, C., Zheng, H., & Zhao, B. Y. (2006). Utilization and fairness in spectrum assignment for opportunistic spectrum access. Mobile Netw. Appl., 11, 555–576.

    Article  Google Scholar 

  9. Rao, V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303–315.

    Article  Google Scholar 

  10. Tizhoosh, H.R (2005) Opposition-Based Learning: A New Scheme for Machine Intelligence. In Proc. Int. Conf. on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, pp 695–701

  11. Xiaoyuan, J., Hu, Y., Jianxin, Z., Yajun, Y., & Xu, S. (2017). An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry. Applied Soft Computing, 57, 504–516.

    Article  Google Scholar 

  12. Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10, 281–295.

    Article  Google Scholar 

  13. Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10, 646–657.

    Article  Google Scholar 

  14. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization, 39, 459–471.

    Article  MathSciNet  MATH  Google Scholar 

  15. Zou, F., Wang, L., Hei, X., & Chen, D. (2015). Teaching-learning-based optimization with learning experience of other learners and its application. Applied Soft Computing, 37, 725–736.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

    Mehdi Askari & Rezvan Dastanian

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  1. Mehdi Askari
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  2. Rezvan Dastanian
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Correspondence to Mehdi Askari.

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Askari, M., Dastanian, R. An optimized spectrum sharing scheme for cognitive radio. Wireless Netw 29, 1621–1627 (2023). https://doi.org/10.1007/s11276-022-03193-5

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  • DOI: https://doi.org/10.1007/s11276-022-03193-5

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