Skip to main content

Advertisement

Log in

Resource allocation scheme for CCRN using hybrid Giza Pyramids construction-based complex-valued satin bowerbird optimization

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In cooperative cognitive radio networks (CCRN), Primary Users (PUs) share their unused spectrum resources with Secondary Users in order to facilitate cooperative communication in cognitive radio networks (SUs). The novel hybrid Giza Pyramids construction-based Complex valued Satin Bower Optimization (GPC-CSBO) algorithm is proposed in this paper to tackle the multiobjective resource allocation problem in CCRN. To operate the cooperative intermediate nodes and increase their throughput, a variety of constraints are taken into consideration, including the amount of power transmitted, QoS constraints, channel capacity, energy consumption, the priority of resource requests from secondary users (SUs), throughput, fairness evaluation constraints, etc. By constructing a cluster of SUs with the help of the SU base station, the GPC algorithm is used for effective load balancing. By providing 11 different constraints as an input to the hybrid GPC-CSBO algorithm, the SUs message request is prioritized and queued. The hybrid GPC-CSBO algorithm selects an optimal path between the SU to the PU base station based on fairness. The efficiency of the proposed algorithm is evaluated in an IEEE 802.11 WLAN area by comparing the proposed methodology with existing techniques. Throughput, energy efficiency, fairness index, delay, packet delivery, and network lifetime are taken into consideration when determining how effective the proposed approach is.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and material

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Code availability

Not applicable.

References

  1. Das GC, Saha S, Bhowmick A, Maity SP (2020) Throughput analysis of a energy harvesting cooperative cognitive radio network. In: 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science (pp. 1–4). IEEE

  2. Ostovar A, Keshavarz H, Quan Z (2021) Cognitive radio networks for green wireless communications: an overview. Telecommun Syst 76(1):129–138

    Article  Google Scholar 

  3. Maharaj BT, Awoyemi BS (2022) Introduction to cognitive radio networks. In: Maharaj BT, Awoyemi BS (eds) Developments in cognitive radio networks. Springer, Cham

    Chapter  Google Scholar 

  4. Rajpoot V, Tripathi V (2022) Cross-layer design based hybrid MAC protocol for cognitive radio network. Phys Commun 50:101524

    Article  Google Scholar 

  5. Zhou X, Li Y, Kwon YH, Soong AC (2008) Detection timing and channel selection for periodic spectrum sensing in cognitive radio. In: IEEE GLOBECOM 2008–2008 IEEE Global Telecommunications Conference (pp. 1–5). IEEE

  6. GHOSH S, Maity SP, Acharya T (2021) On outage analysis in overlay CCRN with RF energy harvesting and co-channel interference

  7. Naeem A, Rehmani M, Saleem Y, Rashid I, Crespi N (2017) Network coding in cognitive radio networks: a comprehensive survey. IEEE Commun Surv Tutor 19(3):1945–1973

    Article  Google Scholar 

  8. Liang YC, Zeng Y, Peh EC, Hoang AT (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wireless Commun 7(4):1326–1337

    Article  Google Scholar 

  9. Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126

    Google Scholar 

  10. Sundararaj V (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Commun 104(1):173–197

    Article  Google Scholar 

  11. Justus JJ, Anuradha M (2022) A golden eagle optimized hybrid multilayer perceptron convolutional neural network architecture‐based three-stage mechanism for multiuser cognitive radio network. Int J Commun Syst 35(4):e5054

    Article  Google Scholar 

  12. Zheng G, Ho Z, Jorswieck EA, Ottersten B (2014) Information and energy cooperation in cognitive radio networks. IEEE Trans Signal Process 62(9):2290–2303

    Article  MathSciNet  MATH  Google Scholar 

  13. Gwon Y, Dastangoo S, Kung HT (2013) Optimizing media access strategy for competing cognitive radio networks. In: 2013 IEEE Global Communications Conference (GLOBECOM) (pp. 1215–1220). IEEE

  14. Preetham CS, Prasad MSG (2016) Hybrid overlay/underlay transmission scheme with optimal resource allocation for primary user throughput maximization in cooperative cognitive radio networks. Wireless Pers Commun 91(3):1123–1136

    Article  Google Scholar 

  15. Zhang G, Yang K, Song J, Li Y (2013) Fair and efficient spectrum splitting for unlicensed secondary users in cooperative cognitive radio networks. Wireless Personal Commun 71(1):299–316

    Article  Google Scholar 

  16. Kaur A, Kumar K (2020) Energy-efficient resource allocation in cognitive radio networks under cooperative multi-agent model-free reinforcement learning schemes. IEEE Trans Netw Serv Manag 17(3):1337–1348

    Article  Google Scholar 

  17. Liao X, Si J, Shi J, Li Z, Ding H (2020) Generative adversarial network assisted power allocation for cooperative cognitive covert communication system. IEEE Commun Lett 24(7):1463–1467

    Article  Google Scholar 

  18. Peter G, Livin J, Sherine A (2021) Hybrid optimization algorithm based optimal resource allocation for cooperative cognitive radio network. Array 12:100093

    Article  Google Scholar 

  19. Song Z, Wang X, LiuZhang YZ (2019) Joint spectrum resource allocation in NOMA-based cognitive radio network with SWIPT. IEEE Access 7:89594–89603

    Article  Google Scholar 

  20. Liu Z, Zhao S, Yuan Y, Yang Y, Guan X (2021) Game-based approach of fair resource allocation in wireless powered cooperative cognitive radio networks. AEU-Int J Electron Commun 134:153699

    Article  Google Scholar 

  21. Harifi S, Mohammadzadeh J, Khalilian M, Ebrahimnejad S (2020) Giza pyramids construction: an ancient-inspired metaheuristic algorithm for optimization. Evolut Intell 14:1743

    Article  Google Scholar 

  22. Zhang S, Zhou Y, Luo Q (2021) A complex-valued encoding satin bowerbird optimization algorithm for global optimization. Evol Syst 12(1):191–205

    Article  Google Scholar 

  23. Zhang Y, Jin Z, Chen Y (2020) Hybrid teaching–learning-based optimization and neural network algorithm for engineering design optimization problems. Knowl-Based Syst 187:104836

    Article  Google Scholar 

  24. Wang P, Zhou Y, Luo Q, Han C, Niu Y, Lei M (2020) Complex-valued encoding metaheuristic optimization algorithm: a comprehensive survey. Neurocomputing 407:313–342

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Jean Justus.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor 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

Justus, J.J., Anuradha, M. Resource allocation scheme for CCRN using hybrid Giza Pyramids construction-based complex-valued satin bowerbird optimization. J Supercomput 79, 4687–4712 (2023). https://doi.org/10.1007/s11227-022-04761-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04761-4

Keywords

Navigation