Skip to main content
SpringerLink
Log in

QUATRE Algorithm for 5G Heterogeneous Network Downlink Power Allocation Problem

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 268))

  • 544 Accesses

Abstract

As a new evolutionary algorithm, QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm has excellent performance and scalability, and many different specific high-performance variants have been developed, but there are few applications in the actual field. The 5G heterogeneous network can effectively improve the existing communication network, provide the possibility for higher throughput and stronger coverage, and at the same time have stronger flexibility, but a more complex network structure will bring cross-layer interference and resource allocation problem. This article mainly tests the effects of the QUATRE algorithm and its two variants on the energy efficiency optimization problem of the downlink of 5G heterogeneous networks. The final results show that the QUATRE and its two variants can achieve good results in this practical problem.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23(3), 715–734 (2019). https://doi.org/10.1007/s00500-018-3102-4

    Article  Google Scholar 

  2. BoussaïD, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)

    Article  MathSciNet  Google Scholar 

  3. Chai, Q.W., Chu, S.C., Pan, J.S., Zheng, W.M.: Applying adaptive and self assessment fish migration optimization on localization of wireless sensor network on 3-D terrain. J. Inf. Hiding Multimedia Signal Process. 11(2), 90–102 (2020)

    Google Scholar 

  4. Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2010)

    Article  Google Scholar 

  5. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst., Man, Cybern., Part B (Cybern.) 26(1), 29–41 (1996)

    Google Scholar 

  6. Formato, R.A.: Central force optimization. Prog. Electromagn. Res. 77, 425–491 (2007)

    Article  Google Scholar 

  7. Ge, X., Yang, J., Gharavi, H., Sun, Y.: Energy efficiency challenges of 5G small cell networks. IEEE Commun. Mag. 55(5), 184–191 (2017)

    Article  Google Scholar 

  8. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  9. Hu, P., Pan, J.S., Chu, S.C.: Improved binary grey wolf optimizer and its application for feature selection. Knowl.-Based Syst. 195, 105,746 (2020). https://doi.org/10.1016/j.knosys.2020.105746

  10. Kaimaletu, S., Krishnan, R., Kalyani, S., Akhtar, N., Ramamurthi, B.: Cognitive interference management in heterogeneous femto-macro cell networks. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–6 (2011)

    Google Scholar 

  11. Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mech. 213(3–4), 267–289 (2010)

    Article  Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  13. Liu, N., Pan, J.S., Xue, J.Y.: An orthogonal quasi-affine transformation evolution (O-QUATRE). In: Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT, July 18–20, Jilin, China, Vols 2, 157, pp. 57–66. Springer (2019)

    Google Scholar 

  14. López-Pérez, D., Chu, X., Vasilakos, A.V., Claussen, H.: On distributed and coordinated resource allocation for interference mitigation in self-organizing lte networks. IEEE/ACM Trans. Netw. 21(4), 1145–1158 (2013)

    Article  Google Scholar 

  15. Meng, Z., Pan, J.S.: A competitive quasi-affine transformation evolutionary (C-QUATRE) algorithm for global optimization. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 001,644–001,649. IEEE (2016)

    Google Scholar 

  16. Meng, Z., Pan, J.S.: Quasi-affine transformation evolution with external archive (QUATRE-EAR): an enhanced structure for differential evolution. Knowl.-Based Syst. 155, 35–53 (2018)

    Article  Google Scholar 

  17. Meng, Z., Pan, J.S., Kong, L.: Parameters with adaptive learning mechanism (PALM) for the enhancement of differential evolution. Knowl.-Based Syst. 141, 92–112 (2018)

    Article  Google Scholar 

  18. Meng, Z., Pan, J.S., Xu, H.: Quasi-affine transformation evolutionary (QUATRE) algorithm: a cooperative swarm based algorithm for global optimization. Knowl.-Based Syst. 109, 104–121 (2016)

    Article  Google Scholar 

  19. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  20. Pan, J.S., Meng, Z., Chu, S.C., Roddick, J.F.: QUATRE algorithm with sort strategy for global optimization in comparison with DE and PSO variants. In: The Euro-China Conference on Intelligent Data Analysis and Applications, pp. 314–323. Springer (2017)

    Google Scholar 

  21. Pan, J.S., Meng, Z., Chu, S.C., Xu, H.R.: Monkey king evolution: an enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment. Telecommun. Syst. 65(3), 351–364 (2017)

    Article  Google Scholar 

  22. Pan, J.S., Meng, Z., Xu, H., Li, X.: A matrix-based implementation of de algorithm: the compensation and deficiency. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 72–81. Springer (2017)

    Google Scholar 

  23. Rao, S.S.: Engineering Optimization: Theory and Practice. Wiley, New York (2019)

    Google Scholar 

  24. Wang, E.K., Liu, X., Chen, C.M., Kumari, S., Shojafar, M., Hossain, M.S.: Voice-transfer attacking on industrial voice control systems in 5G-aided IIoT domain. IEEE Trans. Ind. Inf. 1 (2020). https://doi.org/10.1109/TII.2020.3023677

  25. Wang, P., Chen, C.M., Kumari, S., Shojafar, M., Tafazolli, R., Liu, Y.N.: HDMA: hybrid D2D message authentication scheme for 5G-enabled VANETs. IEEE Trans. Intell. Transp. Syst. 1–10 (2020). https://doi.org/10.1109/TITS.2020.3013928

  26. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  27. Wu, T.Y., Lee, Z., Obaidat, M.S., Kumari, S., Kumar, S., Chen, C.M.: An authenticated key exchange protocol for multi-server architecture in 5G networks. IEEE Access 8, 28096–28108 (2020)

    Article  Google Scholar 

  28. Xue, X., Lu, J.: A compact brain storm algorithm for matching ontologies. IEEE Access 8, 43898–43907 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Pei-Cheng Song, Shu-Chuan Chu & Jeng-Shyang Pan

  2. College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Anhui Liang

Authors
  1. Pei-Cheng Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shu-Chuan Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anhui Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeng-Shyang Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeng-Shyang Pan .

Editor information

Editors and Affiliations

  1. Institute of Intelligent Media Technology, Communication, University of Zhejiang, Hangzhou City, China

    Jie-Fang Zhang

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, China

    Chien-Ming Chen

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Shu-Chuan Chu

  4. Department of Radio Communications and Video Technologies, Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Song, PC., Chu, SC., Liang, A., Pan, JS. (2022). QUATRE Algorithm for 5G Heterogeneous Network Downlink Power Allocation Problem. In: Zhang, JF., Chen, CM., Chu, SC., Kountchev, R. (eds) Advances in Intelligent Systems and Computing. Smart Innovation, Systems and Technologies, vol 268. Springer, Singapore. https://doi.org/10.1007/978-981-16-8048-9_25

Download citation

Publish with us

Policies and ethics

Search

Navigation