Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Design of Minimizing Interference and Maximizing Throughput in Cognitive Radio Network by Joint Optimization of the Channel Allocation and Power Control

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

A Correction to this article was published on 06 June 2023

This article has been updated

Abstract

Most of the existing technologies for cognitive radio network (CRN) is essential for providing an effective solution for spectrum utilization problem in the wireless medium. Power allocation plays a dual and complex role in the multi-hop network, and these dual roles are known to be minimizing the total transmission power and also minimize the outage probability. Cognitive Radio technology enables a feasible way of using white spaces by incorporating diverse spectrum-sharing approaches. Interference makes efficient communication while sharing the channels among unlicensed and licensed users. In addition, Signal to Interference Noise Ratio also enhances the channel capacity. To investigate a joint channel and power allocation for CRNs, this paper aims to optimize the channel allocation and power control of secondary users to minimize interference between primary and secondary users and maximize throughput in CRN. This joint optimization is carried out with the combination of two renowned heuristic strategies that is termed Adaptive Luciferin Enhancement-based Team Work-Glowworm Optimization, which is carried out by deriving the multi-objective function regarding functions like Throughput and Interference. The analysis has demonstrated that the developed CRN framework has maximized the throughput of the CRN and also minimized the interference among the primary users over the existing power allocation strategies. Further, this model has enhanced the network lifetime and analyzed the convergence and complexity of the algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

No new data were generated or analyzed in support of this research.

Change history

References

  1. H. Lei, H. Zhang, I. S. Ansari, Z. Ren, G. Pan, K. A. Qaraqe and A. M. S. Alouini, On secrecy outage of relay selection in underlay cognitive radio networks over Nakagami- $m$ fading channels, IEEE Transactions on Cognitive Communications and Networking, Vol. 3, No. 4, pp. 614–627, 2017.

    Article  Google Scholar 

  2. P. D. Thanh, T. N. K. Hoan and I. Koo, Joint resource allocation and transmission mode selection using a POMDP-based hybrid half-duplex/full-duplex scheme for secrecy rate maximization in multi-channel cognitive radio networks, IEEE Sensors Journal, Vol. 20, No. 7, pp. 3930–3945, 2020.

    Article  Google Scholar 

  3. Z. Wei and B. Hu, A fair multi-channel assignment algorithm with practical implementation in distributed cognitive radio networks, IEEE Access, Vol. 6, pp. 14255–14267, 2018.

    Article  Google Scholar 

  4. F. Sheikholeslami, M. Nasiri-Kenari and F. Ashtiani, Optimal probabilistic initial and target channel selection for spectrum handoff in cognitive radio networks, IEEE Transactions on Wireless Communications, Vol. 14, No. 1, pp. 570–584, 2015.

    Article  Google Scholar 

  5. C. Huang and L. Wang, Dynamic Sampling Rate Adjustment for Compressive Spectrum Sensing over Cognitive Radio Network, IEEE Wireless Commun. Lett., Vol. 1, No. 2, pp. 57–60, 2012.

    Article  Google Scholar 

  6. X. Wang, Joint sensing-channel selection and power control for cognitive radios,", IEEE Transactions on Wireless Communications, Vol. 10, No. 3, pp. 958–967, 2011.

    Article  Google Scholar 

  7. H. T. Thien, V. H. Vu and I. Koo, A transfer games actor-critic learning framework for anti-jamming in multi-channel cognitive radio networks, IEEE Access, Vol. 9, pp. 47887–47900, 2021.

    Article  Google Scholar 

  8. H. Jiang, L. Lai, R. Fan and H. V. Poor, Optimal selection of channel sensing order in cognitive radio, IEEE Transactions on Wireless Communications, Vol. 8, No. 1, pp. 297–307, 2009.

    Article  Google Scholar 

  9. J. Jeya, S. S. Kalamkar and A. Banerjee, Energy harvesting cognitive radio with channel-aware sensing strategy, IEEE Communications Letters., Vol. 18, No. 7, pp. 1171–1174, 2014.

    Article  Google Scholar 

  10. F. Zhou, N. C. Beaulieu, Z. Li, J. Si and P. Qi, Energy-efficient optimal power allocation for fading cognitive radio channels: ergodic capacity, outage capacity, and minimum-rate capacity, IEEE Transactions on Wireless Communications, Vol. 15, No. 4, pp. 2741–2755, 2016.

    Article  Google Scholar 

  11. D. Xu and Q. Li, Joint power control and time allocation for wireless powered underlay cognitive radio networks, IEEE Wireless Communications Letters, Vol. 6, No. 3, pp. 294–297, 2017.

    Article  Google Scholar 

  12. Z. Chen, F. Gao, X. Zhang, J. C. F. Li and M. Lei, Sensing and power allocation for cognitive radio with multiple primary transmit powers, IEEE Wireless Communications Letters, Vol. 2, No. 3, pp. 319–322, 2013.

    Article  Google Scholar 

  13. S. Li, S. Xiao, M. Zhang and X. Zhang, Power saving and improving the throughput of spectrum sharing in wideband cognitive radio networks, Journal of Communications and Networks, Vol. 17, No. 4, pp. 394–405, 2015.

    Article  Google Scholar 

  14. S. Huang, H. Chen and Y. Zhang, Optimal power allocation for spectrum sensing and data transmission in cognitive relay networks, IEEE Wireless Communications Letters, Vol. 1, No. 1, pp. 26–29, 2012.

    Article  Google Scholar 

  15. K. Illanko, M. Naeem, A. Anpalagan and D. Androutsos, Energy-efficient frequency and power allocation for cognitive radios in television systems, IEEE Systems Journal, Vol. 10, No. 1, pp. 313–324, 2016.

    Article  Google Scholar 

  16. S. Wang, F. Huang and Z. Zhou, Fast power allocation algorithm for cognitive radio networks, IEEE Communications Letters, Vol. 15, No. 8, pp. 845–847, 2011.

    Article  Google Scholar 

  17. S. Parsaeefard and A. R. Sharafat, Robust distributed power control in cognitive radio networks, IEEE Transactions on Mobile Computing, Vol. 12, No. 4, pp. 609–620, 2013.

    Article  Google Scholar 

  18. A. Paul and S. P. Maity, On outage minimization in cognitive radio networks through routing and power control, Wireless Personal Communications, Vol. 98, pp. 251–269, 2018.

    Article  Google Scholar 

  19. K. K. Anumandla, S. L. Sabat, R. Peesapati, A. V. Prabu, J. R. K. K. Dabbakuti and R. Rout, Optimal spectrum and power allocation using evolutionary algorithms for cognitive radio networks, Internet Technology Letters, 2020. https://doi.org/10.1002/itl2.207.

    Article  Google Scholar 

  20. X. He, H. Jiang, Y. Song, Y. Luo and Q. Y. Zhang, Joint optimization of channel allocation and power control for cognitive radio networks with multiple constraints, Wireless Networks, Vol. 26, No. 1, pp. 101, 2020.

    Article  Google Scholar 

  21. X. Kang, Optimal power allocation for bi-directional cognitive radio networks with fading channels, IEEE Wireless Communications Letters, Vol. 2, No. 5, pp. 567–570, 2013.

    Article  Google Scholar 

  22. C. L. Chuang, W. Y. Chiu and Y. C. Chuang, Dynamic multiobjective approach for power and spectrum allocation in cognitive radio networks, IEEE Systems Journal, Vol. 15, No. 4, pp. 5417–5428, 2021.

    Article  Google Scholar 

  23. X. Kang, R. Zhang, Y. Liang and H. K. Garg, Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint, IEEE Journal on Selected Areas in Communications, Vol. 29, No. 2, pp. 374–383, 2011.

    Article  Google Scholar 

  24. G. Peter, J. Livin and A. Sherine, Hybrid optimization algorithm based optimal resource allocation for cooperative cognitive radio network, Array, 2021. https://doi.org/10.1016/j.array.2021.100093.

    Article  Google Scholar 

  25. H. W. Lee, W. Chang and B. C. Jung, Optimal power allocation and allowable interference shaping in cognitive radio networks, Computers & Electrical Engineering, Vol. 71, pp. 265–272, 2018.

    Article  Google Scholar 

  26. Y. Zhou, G. Zhou, Y. Wang and G. Zhao, A glowworm swarm optimization algorithm based tribes, Applied Mathematics & Information Sciences, Vol. 7, No. 2L, pp. 537–541, 2013.

    Article  Google Scholar 

  27. M. Dehghani and P. Trojovský, Teamwork optimization algorithm: a new optimization approach for function minimization/maximization, Sensors, Vol. 21, No. 4567, pp. 1–10, 2021.

    Google Scholar 

  28. W. F. Fihri, H. E. Ghazi and B. A. E. Majd, A multi-objective particle swarm optimization based algorithm for primary user emulation attack detection, Wireless Personal Communications, Vol. 117, pp. 867–886, 2021.

    Article  Google Scholar 

  29. G. Eappen and T. Shankar, Multi-objective modified grey wolf optimization algorithm for efficient spectrum sensing in the cognitive radio network, Arabian Journal for Science and Engineering, Vol. 46, pp. 3115–3145, 2021.

    Article  Google Scholar 

  30. J. L. Tabjula, S. Kanakambaran, S. Kalyani, P. Rajagopal and B. Srinivasan, Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring, Structural Control and Health Monitoring, 2021. https://doi.org/10.1002/stc.2690.

    Article  Google Scholar 

  31. J. Tabjula, S. Kalyani, P. Rajagopal and B. Srinivasan, Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves, Structural Health Monitoring, Vol. 21, pp. 2719, 2021.

    Google Scholar 

  32. D. Roy and M. Dutta, A systematic review and research perspective on recommender systems, Journal of Big Data, Vol. 9, pp. 59, 2022.

    Article  Google Scholar 

  33. S. Rajkumar and C. Rebeiro, Implementation of cryptographic primitives, International Journal of Engineering Trends and Technology, Vol. 49, No. 4, pp. 264, 2017.

    Article  Google Scholar 

  34. S. Rajkumar, Implementing software defined load balancer and firewall, International Journal of Scientific Research and Engineering Development, Vol. 5, No. 5, pp. 300–304, 2022.

    Google Scholar 

  35. L. S. Ambati, K. Narukonda, G. R. Bojja and D. Bishop, Factors influencing the adoption of artificial intelligence in organizations-from an employee’s perspective, Dakota State University, 2020.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE, KL Deemed to be University, Guntur, India

    T. Sarath Babu & Penke Satyanarayana

  2. ECE, G. Pulla Reddy Engineering College, Kurnool, India

    S. Nagaraja Rao

Authors
  1. T. Sarath Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Nagaraja Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Penke Satyanarayana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Sarath Babu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: the co-authors are linked with their correct affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Babu, T.S., Rao, S.N. & Satyanarayana, P. A Design of Minimizing Interference and Maximizing Throughput in Cognitive Radio Network by Joint Optimization of the Channel Allocation and Power Control. Int J Wireless Inf Networks 30, 211–225 (2023). https://doi.org/10.1007/s10776-023-00592-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-023-00592-z

Keywords

Search

Navigation