Enhancing Spectrum Efficiency and Energy Harvesting Selection for Cognitive Using a Hybrid Technique

  • M. BalasubramanianEmail author
  • V. Rajamani
  • S. Puspha
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)


Cognitive Radio Networks (CRN) is one of the evolving technologies for detecting available channels automatically in a wireless spectrum in which several researchers have developing new concepts and algorithms to improve its efficiency. Two main issues that are considered to be important in wireless communications are energy effectiveness and spectrum efficiency in which the performance is increased by using CRN as it is compact with both primary user (PU) and secondary user (SU). Both PU and SU are recognized by the licensed and unlicensed bands. In this paper, the hybrid combination of dynamic Genetic Algorithm (GA) with Token Passing Algorithm (TPA) is proposed to enhance the efficiency for accessing the given spectrum for PU and SU. In this proposed work, maximum throughput is achieved by increasing the efficiency of spectrum and improving energy harvesting in channel selection. The collision constraints, energy causality constraints, efficient resource allocation. And sensing occupied channels are considered for average channel capacity for improving throughput performance. The main motivation of this paper is to avoid adjacent channel interference and co-channel interference using the proposed method by achieving energy harvesting and maximum spectrum efficiency for CRN.


Cognitive Radio Networks (CRN) Genetic Algorithm (GA) Token Passing Algorithm (TPA) Energy harvesting Spectrum efficiency 


  1. 1.
    Maleki, S., Pandharipande, A., Leus, G.: Energy-efficient distributed spectrum sensing for cognitive sensor networks. IEEE Sens. J. 11(3), 565–573 (2010)CrossRefGoogle Scholar
  2. 2.
    Kokkinen, K., Turunen, V., Kosunen, M., Chaudhari, S., Koivunen, V., Ryynänen, J.: On the implementation of autocorrelation-based feature detector. In: 2010 4th International Symposium on Communications, Control and Signal Processing, ISCCSP 2010, Limassol, Cyprus, 3–5 March 2010, pp. 1–4. IEEE (2010). (978-1-4244-6287)Google Scholar
  3. 3.
    Do, T., Mark, B.L.: Improving spectrum sensing performance by exploiting multiuser diversity. InTech (2012). (978-953-51-0268-7)Google Scholar
  4. 4.
    Alghamdi, O.A., Ahmed, M.Z.: New optimization method for cooperative spectrum sensing in cognitive radio networks. In: IEEE Conference (2011)Google Scholar
  5. 5.
    Peh, E.C., Liang, Y.C., Guan, Y.L., Zeng, Y.: Cooperative spectrum sensing in cognitive radio networks with weighted decision fusion schemes. IEEE Trans. Wirel. Commun. 9(12), 3838–3847 (2010)CrossRefGoogle Scholar
  6. 6.
    Lee, S., Zhang, R.: Cognitive wireless powered network: spectrum sharing models and throughput maximization. IEEE Trans. Cogn. Commun. Network. 1(3), 335–346 (2015)CrossRefGoogle Scholar
  7. 7.
    Yang, C., Li, J., Guizani, M., Anpalagan, A., Elkashlan, M.: Advanced spectrum sharing in 5G cognitive heterogeneous networks. IEEE Wirel. Commun. 23(2), 94–101 (2016)CrossRefGoogle Scholar
  8. 8.
    Lu, X., Wang, P., Niyato, D., Hossain, E.: Dynamic spectrum access in cognitive radio networks with RF energy harvesting. IEEE Wirel. Commun. 21(3), 102–110 (2014)CrossRefGoogle Scholar
  9. 9.
    Lee, S., Zhang, R., Huang, K.: Opportunistic wireless energy harvesting in cognitive radio networks. IEEE Trans. Wirel. Commun. 12(9), 4788–4799 (2013)CrossRefGoogle Scholar
  10. 10.
    Yuan, F., Zhang, Q.T., Jin, S., Zhu, H.: Optimal harvest-use-store strategy for energy harvesting wireless systems. IEEE Trans. Wirel. Commun. 14(2), 698–710 (2014)CrossRefGoogle Scholar
  11. 11.
    Ku, M.L., Li, W., Chen, Y., Liu, K.R.: Advances in energy harvesting communications: Past, present, and future challenges. IEEE Commun. Surv. Tutor. 18(2), 1384–1412 (2015)CrossRefGoogle Scholar
  12. 12.
    Pappas, N., Jeon, J., Ephremides, A., Traganitis, A.: Optimal utilization of a cognitive shared channel with a rechargeable primary source node. J. Commun. Netw. 14(2), 162–168 (2012)CrossRefGoogle Scholar
  13. 13.
    Sultan, A.: Sensing and transmit energy optimization for an energy harvesting cognitive radio. IEEE Wirel. Commun. Lett. 1(5), 500–503 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Park, S., Kim, H., Hong, D.: Cognitive radio networks with energy harvesting. IEEE Trans. Wirel. Commun. 12(3), 1386–1397 (2013)CrossRefGoogle Scholar
  15. 15.
    Park, S., Hong, D.: Optimal spectrum access for energy harvesting cognitive radio networks. IEEE Trans. Wirel. Commun. 12(12), 6166–6179 (2013)CrossRefGoogle Scholar
  16. 16.
    Chung, W., Park, S., Lim, S., Hong, D.: Spectrum sensing optimization for energy-harvesting cognitive radio systems. IEEE Trans. Wirel. Commun. 13(5), 2601–2613 (2014)CrossRefGoogle Scholar
  17. 17.
    Park, S., Hong, D.: Achievable throughput of energy harvesting cognitive radio networks. IEEE Trans. Wirel. Commun. 13(2), 1010–1022 (2014)CrossRefGoogle Scholar
  18. 18.
    Dhillon, H.S., Li, Y., Nuggehalli, P., Pi, Z., Andrews, J.G.: Fundamentals of heterogeneous cellular networks with energy harvesting. IEEE Trans. Wirel. Commun. 13(5), 2782–2797 (2014)CrossRefGoogle Scholar
  19. 19.
    Niyato, D., Wang, P., Kim, D.I.: Channel selection in cognitive radio networks with opportunistic RF energy harvesting. In: Proceedings IEEE International Conference Communication (ICC), Sydney, NSW, Australia, pp. 1555–1560 (2014)Google Scholar
  20. 20.
    Yin, S., Zhang, E., Yin, L., Li, S.: Optimal saving-sensing-transmitting structure in self-powered cognitive radio systems with wireless energy harvesting. In: Proceedings of IEEE International Conference on Communication (ICC), Budapest, Hungary, pp. 2807–2811 (2011)Google Scholar
  21. 21.
    Gao, X., Xu, W., Li, S., Lin, J.: An online energy allocation strategy for energy harvesting cognitive radio systems. In: International Conference Wireless Communication, Signal Processing (WCSP), Hangzhou, China, pp. 1–5 (2013)Google Scholar
  22. 22.
    Liang, Y.C., Zeng, Y., Peh, E.C., Hoang, A.T.: Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wirel. Commun. 7(4), 1326–1337 (2008)CrossRefGoogle Scholar
  23. 23.
    Cui, S., Goldsmith, A.J., Bahai, A.: Energy-constrained modulation optimization. IEEE Trans. Wirel. Commun. 4(5), 2349–2360 (2005)CrossRefGoogle Scholar
  24. 24.
    Liu, C., Mou, Y., Pan, C.: Optimization of power allocation in wireless cooperative communication system. In: AIP Conference Proceedings, vol. 1839, no. 1, p. 020185, May 2017Google Scholar
  25. 25.
    Suliman, R.A.H., Bilal, K.H., Elemam, I.: Review paper on cognitive radio networks. J. Electr. Electron. Syst. Open Access J. 7(1), 1000252 (2018)Google Scholar
  26. 26.
    Elhachmi, J., Guennoun, Z.: Cognitive radio spectrum allocation using genetic algorithm. EURASIP J. Wirel. Commun. Netw. 2016(1), 133 (2016)CrossRefGoogle Scholar
  27. 27.
    Rathee, M., Kumar, S.: Quantum-inspired ant-based energy balanced routing in wireless sensor networks. Recent Pat. Comput. Sci. 10(1) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of CSESPIHER AvadiChennaiIndia
  2. 2.Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering CollegeChennaiIndia
  3. 3.SPIHER AvadiChennaiIndia

Personalised recommendations