Wireless Networks

, Volume 25, Issue 1, pp 1–11 | Cite as

Joint optimization of spectrum access and power allocation in uplink OFDMA CR-VANETs

  • Zhufang KuangEmail author
  • Zhigang Chen
  • Jianping Pan
  • Dawood Sajjadi


Cognitive radio (CR) is a state-of-the-art technology to solve the spectrum shortage problem for emerging wireless services, which include the CR-enabled vehicular ad hoc networks (CR-VANETs) for vehicle-to-road side unit (RSU) communications. With the increasing demands for high data rate and more reliable mobile services, orthogonal frequency division multiple access (OFDMA) has been often used in such systems. Energy efficiency is an important issue in OFDMA CR-VANETs due to the concern of green communications to transmit the required data in the shortest time, without affecting primary users. In this paper, we proposed an adaptive solution to minimize the overall energy consumption of CR-VANETs as well as maintaining the service quality of Vehicle-to-RSU uplink communications. This goal has been achieved by the means of dynamically selecting different spectrum access schemes for CR-enabled vehicles with relays. Considering the inter-vehicle distances and location information, we formulated a mixed-integer nonlinear constrained optimization problem. A heuristic algorithm based on the greedy strategy and bisection method is then used to solve the formulated problem and it has been evaluated through extensive simulations for different upload data sizes and available communication durations. The acquired results substantiate the efficiency of the proposed solution in terms of energy consumption.


CR-VANETs Spectrum access Energy efficiency Orthogonal frequency division multiple access (OFDMA) 



This work is supported by the National Natural Science Foundation of China under Grants Nos. 61309027, 61379110, and 61073104, the Scientific Research Fund of Hunan Provincial Education Department under No. 13B148, the Key Research and Development Project of Hunan Science and Technology Plan under No. 2016SK2028, the China Postdoctoral Science Foundation under No. 2013M542136, the Postdoctoral Foundation of Central South University under No. 126224, and in part by NSERC, CFI and BCKDF.


  1. 1.
    Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., et al. (2011). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Communications Surveys & Tutorials, 13(4), 584–616.CrossRefGoogle Scholar
  2. 2.
    Mitola, J, I. I. I., & Maguire, G. Q, Jr. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRefGoogle Scholar
  3. 3.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  4. 4.
    Ahmed, E., Gani, A., Abolfazli, S., Yao, L., & Khan, S. (2016). Channel assignment algorithms in cognitive radio networks: Taxonomy, open issues, and challenges. IEEE Communications Surveys & Tutorials, 18(1), 795–823.CrossRefGoogle Scholar
  5. 5.
    Felice, M. D., Doost-Mohammady, R., Chowdhury, K. R., & Bononi, L. (2012). Smart radios for smart vehicles: Cognitive vehicular networks. IEEE Vehicular Technology Magazine, 7(2), 26–33.CrossRefGoogle Scholar
  6. 6.
    Singh, K. D., Rawat, P., & Bonnin, J.-M. (2014). Cognitive radio for vehicular ad hoc networks (CR-VANETs): Approaches and challenges. EURASIP Journal on Wireless Communications and Networking, 2014(1), 1–22.CrossRefGoogle Scholar
  7. 7.
    Tragos, E. Z., Zeadally, S., Fragkiadakis, A. G., Siris, V., et al. (2013). Spectrum assignment in cognitive radio networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 15(3), 1108–1135.CrossRefGoogle Scholar
  8. 8.
    Han, Y., Ekici, E., Kremo, H., & Altintas, O. (2017). Throughput-efficient channel allocation algorithms in multi-channel cognitive vehicular networks. IEEE Transactions on Wireless Communications, 16(2), 757–770.CrossRefGoogle Scholar
  9. 9.
    Cheng, N., Zhang, N., Lu, N., Shen, X., Mark, J. W., & Liu, F. (2014). Opportunistic spectrum access for CR-VANETs: A game-theoretic approach. IEEE Transactions on Vehicular Technology, 63(1), 237–251.CrossRefGoogle Scholar
  10. 10.
    Rawat, D. B., Reddy, S., Sharma, N., Bista, B. B., & Shetty, S. (2015). Cloud-assisted GPS-driven dynamic spectrum access in cognitive radio vehicular networks for transportation cyber physical systems. In Proceedings of Wireless Communications and Networking Conference (WCNC), IEEE (pp. 1942–1947).Google Scholar
  11. 11.
    Agarwal, S., & De, S. (2016). eDSA: Energy-efficient dynamic spectrum access protocols for cognitive radio networks. IEEE Transactions on Mobile Computing, 15(12), 3057–3071.CrossRefGoogle Scholar
  12. 12.
    Nguyen, T.-D., Berder, O., & Sentieys, O. (2011). Energy-efficient cooperative techniques for infrastructure-to-vehicle communications. IEEE Transactions on Intelligent Transportation Systems, 12(3), 659–668.CrossRefGoogle Scholar
  13. 13.
    Yang, C., Fu, Y., Zhang, Y., Xie, S., & Yu, R. (2013). Energy-efficient hybrid spectrum access scheme in cognitive vehicular ad hoc networks. IEEE Communications Letters, 17(2), 329–332.CrossRefGoogle Scholar
  14. 14.
    Das, D., & Das, S. (2017). Adaptive resource allocation scheme for cognitive radio vehicular ad-hoc network in the presence of primary user emulation attack. IET Networks, 6(1), 5–13.CrossRefGoogle Scholar
  15. 15.
    Han, Y., Ekici, E., Kremo, H., & Altintas, O. (2017). Resource allocation algorithms supporting coexistence of cognitive vehicular and IEEE 802.22 networks. IEEE Transactions on Wireless Communications, 16(2), 1066–1079.CrossRefGoogle Scholar
  16. 16.
    Zhang, H., He, X., Luo, T., & Shi, W. (2016). Transmission opportunity of spectrum sharing with cellular uplink spectrum in cognitive VANET. In Proceedings of IEEE Vehicular Technology Conference, IEEE (pp. 1–5).Google Scholar
  17. 17.
    Xu, W., Yuan, W., Shi, Q., & Wang, X. (2017). Distributed energy efficient cross-layer optimization for multihop MIMO cognitive radio networks with primary user rate protection. IEEE Transactions on Vehicular Technology, 66(1), 785–797.Google Scholar
  18. 18.
    Zhou, M., & Zhao, X. (2016). Energy-efficient power allocation algorithm in cognitive radio networks. IET Communications, 10(17), 2445–2451.CrossRefGoogle Scholar
  19. 19.
    Huang, X., Han, T., & Ansari, N. (2015). On green-energy-powered cognitive radio networks. IEEE Communications Surveys & Tutorials, 17(2), 827–842.CrossRefGoogle Scholar
  20. 20.
    Wang, S., Shi, W., & Wang, C. (2015). Energy-efficient resource management in OFDM-based cognitive radio networks under channel uncertainty. IEEE Transactions on Communications, 63(9), 3092–3102.CrossRefGoogle Scholar
  21. 21.
    Xiong, C., Lu, L., & Li, G. Y. (2014). Energy-efficient spectrum access in cognitive radios. IEEE Journal on Selected Areas in Communications, 32(3), 550–562.CrossRefGoogle Scholar
  22. 22.
    Yaacoub, E., & Dawy, Z. (2012). A survey on uplink resource allocation in OFDMA wireless networks. IEEE Communications Surveys & Tutorials, 14(2), 322–337.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Zhufang Kuang
    • 1
    • 2
    • 3
    Email author
  • Zhigang Chen
    • 2
  • Jianping Pan
    • 3
  • Dawood Sajjadi
    • 3
  1. 1.School of Computer and Information EngineeringCentral South University of Forestry and TechnologyChangshaChina
  2. 2.School of SoftwareCentral South UniversityChangshaChina
  3. 3.Department of Computer ScienceUniversity of VictoriaVictoriaCanada

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