Skip to main content

Advertisement

SpringerLink
Log in

Multi-Objective Evolutionary Optimization for Spectrum Allocation and Power Control in Vehicular Communications

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper investigates the spectrum allocation and power control problems of Device-to-device enabled vehicular communications in a rapidly changing wireless channel environment. We model the above-mentioned issue as a multi-objective optimization problem to realize spectrum allocation and power control. To solve the problem, we proposed a multi-objective evolutionary algorithm. Firstly, we take the network capacity of vehicle-to-infrastructure links as the optimization objective to improve the network transmission rate while ensuring the reliability of all vehicle-to-vehicle links. Power consumption efficiency is then considered to control interference in the network and provide better energy efficiency. Computer numerical results show that our algorithm can obtain desirable solutions efficiently for various scenarios of spectrum allocation and power control in vehicular communications.

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

Access this article

Log in via an institution

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. MacHardy, Z., Khan, A., Obana, K., & Iwashina, S. (2018). V2X access technologies: Regulation, research, and remaining challenges. IEEE Communications Surveys & Tutorials, 20(3), 1858–1877.

    Article  Google Scholar 

  2. Benbraika, M. K., & Bitam, S. (2021). Spectrum allocation and power control for D2D communication underlay 5G cellular networks. International Journal of Communication Networks and Distributed Systems, 27(3), 299–322.

    Article  Google Scholar 

  3. Ren, J., Chai, Z., & Chen, Z. (2022). Joint spectrum allocation and power control in vehicular communications based on dueling double DQN. Vehicular Communications, 38, 100543.

    Article  Google Scholar 

  4. Scenarios, Requirements KPIs for 5G Mobile and Wireless System, Standard ICT-317669-METIS/D1.1, METIS D1.1, Apr. 2013. [Online].Available: https://www.metis2020.com/documents/deliver-ables/.

  5. Liang, L., Li, G. Y., & Xu, W. (2017). Resource allocation for D2D-enabled vehicular communications. IEEE Transactions on Communi-cations, 65(7), 3186–3197.

    Article  Google Scholar 

  6. Zhang, Z., Long, K., Wang, J., & Dressler, F. (2014). On swarm intelligence inspired self-organized networking: Its bionic mechanisms, designing principles and optimization approaches. Communications Surveys & Tutorials, 16(1), 513–537.

    Article  Google Scholar 

  7. Tan, K. C., Lee, T. H., & Khor, E. F. (2002). Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artificial Intelligence Review, 17, 251–290.

    Article  MATH  Google Scholar 

  8. Sierra, M. R., & Coello, C. A. C. (2005). Improving PSO-based multi-objective optimization using crowding, mutation and∈-dominance. Springer.

    Book  MATH  Google Scholar 

  9. Zitzler, E., Laumanns, M., Thiele, L. (2001). SPEA2: Improving the strength pareto evolutionary algorithm, In Proceedings of evolutionary methods for design, optimization and control with applications to industrial problems. Athens, Greece, 2001.

  10. Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., & Hanzo, L. (2017). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems. IEEE Communications Surveys & Tutorials, 19(1), 550–586.

    Article  Google Scholar 

  11. Xie, Y., Liu, Z., Ma, K., and Yuan, Y. (2019). "Robust Power Control in D2D-Enabled Vehicular Communication Network," 2019 3rd international symposium on autonomous systems (ISAS), 2019, pp. 108–113.

  12. Bi, Z., and Zhou, W. (2020). "Deep Reinforcement Learning Based Power Allocation for D2D Network," 2020 IEEE 91st vehicular technology conference (VTC2020-Spring), 2020, pp. 1–5.

  13. Li, R., & Zhu, P. (2020). Spectrum allocation strategies based on QoS in cognitive vehicle networks. IEEE Access, 8, 99922–99933.

    Article  Google Scholar 

  14. Li, Z., Guo, C., & Xuan, Y. (2019). A multi-agent deep reinforcement learning based spectrum allocation framework for D2D communications. IEEE Global Communications Conference (GLOBECOM), 2019, 1–6.

    Google Scholar 

  15. Ren, Y., Liu, F., Liu, Z., Wang, C., & Ji, Y. (2015). Power control in D2D-based vehicular communication networks. IEEE Transactions on Vehicular Technology, 64(12), 5547–5562.

    Article  Google Scholar 

  16. Guo, C., Liang, L., & Li, G. Y. (2019). Resource allocation for low-latency vehicular communications: An effective capacity perspective. IEEE Journal on Selected Areas in Communications, 37(4), 905–917.

    Article  Google Scholar 

  17. Wang, H., & Chu, X. (2012). Distance-constrained resource-sharing criteria for Device-to-Device communications underlaying cellular networks. Electronics Letters, 48(9), 528–530.

    Article  Google Scholar 

  18. Ron, D., & Lee, J.-R. (2022). DNN-based dynamic transmit power control for V2V communication underlaid cellular uplink. IEEE Transactions on Vehicular Technology, 71(11), 12413–12418.

    Article  Google Scholar 

  19. Feng, D., Lu, L., Yuan-Wu, Y., Li, G. Y., Feng, G., & Li, S. (2013). Device-to-device communications underlaying cellular networks. IEEE Transactions on Communications, 61(8), 3541–3551.

    Article  Google Scholar 

  20. Wang, K., Feng, Y., Liang, L., and Jin, S. (2022). "Joint Spectrum Allocation and Power Control in Vehicular Networks Based on Reinforcement Learning," 2022 international symposium on wireless communication systems (ISWCS), Hangzhou, China, 2022, pp. 1–6.

  21. Dinh-Van, S., Shin, Y., & Shin, O.-S. (2017). Resource allocation and power control based on user grouping for underlay device-to-device communications in cellular networks. Transactions on Emerging Telecommunications Technologies, 28(1), e2920.

    Article  Google Scholar 

  22. Wang, X., Qian, Z., Wang, X., & Huang, L. (2019). Resource allocation scheme based on rate-requirement for device-to-device downlink communications. International Journal of Pattern Recognition and Artificial Intelligence, 33(13), 1959040.

    Article  Google Scholar 

  23. Zhao, Z., Cheng, X., Wen, M., Jiao, B., & Wang, C. (2013). Channel estimation schemes for IEEE 802.11p standard. IEEE Intelligent Transportation Systems Magazine, 5(4), 38–49.

    Article  Google Scholar 

  24. Guo, C., Liang, L., & Li, G. Y. (2019). Resource allocation for vehicular communications with low latency and high reliability. IEEE Transactions on Wireless Communications, 18(8), 3887–3902.

    Article  Google Scholar 

  25. Zhu, Y., Bao, Y., & Li, B. (2012). On Maximizing delay-constrained coverage of urban vehicular networks. IEEE Journal on Selected Areas in Communications, 30(4), 804–817.

    Article  Google Scholar 

  26. Botsov, M., Klügel, M., Kellerer, W., & Fertl, P. (2014). Location dependent resource allocation for mobile device-to-device communica-tions. IEEE Wireless Communications and Networking Conference (WCNC), 2014, 1679–1684.

    Article  Google Scholar 

  27. Mankge, S. L., & Ghayoor, F. (2019). Power allocation for D2D-enabled vehicular communications to support driver assistance systems. IEEE AFRICON, 2019, 1–5.

    Google Scholar 

  28. Sun, W., Ström, E. G., Brännström, F., Sou, K. C., & Sui, Y. (2016). Radio resource management for D2D-based V2V communication. IEEE Transactions on Vehicular Technology, 65(8), 6636–6650.

    Article  Google Scholar 

  29. Tse, D., & Viswanath, P. (2005). Fundamentals of wireless communication. Cambridge Univ. Press.

    Book  MATH  Google Scholar 

  30. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  31. WF on SLS Evaluation Assumptions for eV2X, document R1–165704, 3GPP TSG RAN WG1 Meeting #85, May 2016.

  32. 3rd Generation Partnership Project: Technical Specification Group Radio Access Network: Study LTE-Based V2X Services: (Release 14), Standard 3GPP TR 36.885 V2.0.0, Jun. 2016.

  33. Jiang, H., Chen, J., & Liu, T. (2014). Multi-objective design of an FBG sensor network using an improved Strength Pareto Evolutionary Algorithm. Sensors & Actuators A Physical, 220(12), 230–236.

    Article  Google Scholar 

  34. Hajjej, F., Ejbali, R., and Zaied, M. (2015). Quality of Services based routing using evolutionary algorithms for Wireless Sensor Network, 2015 15th international conference on intelligent systems design and applications (ISDA), pp. 237–242.

  35. WINNER II Channel Models, Standard IST-4–027756 WINNER II D1.1.2 V1.2, Sep. 2007. [Online]. Available: http://projects.celtic-initiative.org/winner+/WINNER2-Deliverables/D1.1.2v1.1.pdf.

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant nos. 61972456, 62172298); Natural Science Foundation of Tianjin (No.20JCYBJC00140); Tianjin Research Innovation Project for Postgraduate Students (No.2021YJSS059); Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education, P.R.China (KFKT-2020101).

Author information

Author notes

  1. Zhengyi Chai, Jie Ren have contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, Tianjin, China

    Zhengyi Chai & Jie Ren

Authors
  1. Zhengyi Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Investigation: [ZC], Conceptualization: [ZC], Supervision: [ZC], Funding acquisition: [ZC]. Methodology: [JR], Validation: [JR], Data curation: [JR], Writing—Original draft preparation: [JR], Software: [JR].

Corresponding author

Correspondence to Jie Ren.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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 (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

Chai, Z., Ren, J. Multi-Objective Evolutionary Optimization for Spectrum Allocation and Power Control in Vehicular Communications. Wireless Pers Commun 129, 2767–2789 (2023). https://doi.org/10.1007/s11277-023-10257-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10257-y

Keywords

Search

Navigation