Advertisement

Delay-Constrained Data Transmission with Minimal Energy Consumption in Cognitive Radio/WiFi Vehicular Networks

  • Show-Shiow TzengEmail author
  • Ying-Jen Lin
Article
  • 8 Downloads

Abstract

Cognitive radio benefits vehicular users to access massive and broadband services in a radio environment of limited spectrum. Reducing energy consumption is critical for energy-limited users and is also helpful to reduce greenhouse gas emission. This paper first proposes an energy-efficient transmission scheme in cognitive radio/WiFi vehicular networks, in which traffic is optimally distributed to cognitive radio and WiFi interfaces such that traffic is transmitted in an energy-efficient and timely manner. The traffic distribution problem is formulated as an optimization problem in which the amount of the energy of spectrum sensing and data transmission is minimized such that (1) data transmission is completed within a delay constraint and (2) the interference constraint of primary users and the power limitation of transmitters are satisfied. We prove that the optimization problem is a convex problem and does not always have a general solution. Due to the computation cost of the optimization problem, we propose a heuristic algorithm, namely, relay-weighted transmission, to solve the traffic distribution problem. Extensive numerical results show that the optimal transmission and the relay-weighted transmission schemes appropriately distribute data into the two radio interfaces such that the power consumption is significantly reduced.

Keywords

Cognitive radio networks Energy-efficient transmission Delay-constrained 

Notes

Acknowledgements

This research was supported by the Ministry of Science and Technology, Taiwan, under Grants MOST 103-2221-E-017-004- and MOST 104-2221-E-017-003-.

References

  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.
    Zheng, K., Zheng, Q., Chatzimisios, P., Xiang, W., & Zhou, Y. (2015). Heterogeneous vehicular networking: A survey on architecture, challenges, and solutions. IEEE Communication Surveys & Tutorials, 17(4), 2377–2396.CrossRefGoogle Scholar
  3. 3.
    Cordeschi, N., Amendola, D., & Baccarelli, E. (2015). Reliable adaptive resource management for cognitive cloud vehicular networks. IEEE Transactions on Vehicular Technology, 64(6), 2528–2537.CrossRefGoogle Scholar
  4. 4.
    Felice, M. D., Mohammady, R. D., Chowdhury, K. R., & Bononi, L. (2012). Smart radios for smart vehicles cognitive vehicular networks. IEEE Vehicular Technology Magazine, 7(2), 26–33.CrossRefGoogle Scholar
  5. 5.
    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, 49.CrossRefGoogle Scholar
  6. 6.
    Gerasimenko, M., Moltchanov, D., Andreev, S., Koucheryavy, Y., Himayat, N., Yeh, S.-P., et al. (2017). Adaptive resource management strategy in practical multi-radio heterogeneous networks. IEEE Access, 5, 219–235.CrossRefGoogle Scholar
  7. 7.
    Cheng, X., et al. (2014). Electrified vehicles and the smart grid: The ITS perspective. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1388–1404.CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Tian, D., Zhou, J., Sheng, Z., & Leung, V. C. M. (2016). Robust energy-efficient MIMO transmission for cognitive vehicular networks. IEEE Transactions on Vehicular Technology, 65(6), 3845–3859.CrossRefGoogle Scholar
  10. 10.
    Tsiropoulos, G., Dobre, O., Ahmed, M., & Baddour, K. (2016). Radio resource allocation techniques for efficient spectrum access in cognitive radio networks. IEEE Communications Surveys & Tutorials, 18(1), 817–840.CrossRefGoogle Scholar
  11. 11.
    Wang, X. Y., & Ho, P. H. (2010). A novel sensing coordination framework for CR-VANETs. IEEE Transactions on Vehicular Technology, 59(4), 1936–1948.CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Xie, S., Zhang, Y., Yu, R., & Leung, V. (2012). Energy-efficient spectrum discovery for cognitive radio green networks. ACM/Springer Mobile Networks and Applications, 17(1), 64–74.CrossRefGoogle Scholar
  13. 13.
    Godbole, A. S. (2002). Data Communications and Networks. New York City: Tata McGraw-Hill Education.Google Scholar
  14. 14.
    Goldsmith, A. J., & Chua, S.-G. (1997). Variable-rate variable-power MQAM for fading channels. IEEE Transactions on Communications, 45(10), 1218–1230.CrossRefGoogle Scholar
  15. 15.
    Stewart, J. (2016). Calculus: Early transcendentals. International metric edition (8th ed.). Boston: CENGAGE Learning Custom Publishing.Google Scholar
  16. 16.
    Zhuang, Y., Pan, J., Luo, Y., & Cai, L. (2011). Time and location-critical emergency message dissemination for vehicular ad-hoc networks. IEEE Journal on Selected Areas in Communications, 29(1), 187–196.CrossRefGoogle Scholar
  17. 17.
    ETSI EN 302 663 V1.2.0. (2012-11). Intelligent Transport Systems (ITS); Access layer specification for Intelligent Transport Systems operating in the 5 GHz frequency band.Google Scholar
  18. 18.
    Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  19. 19.
    Beck, A. (2014). Introduction to nonlinear optimization: Theory, algorithms, and applications with MATLAB. Phidelphia: SIAM.CrossRefzbMATHGoogle Scholar
  20. 20.
    Hasan, Z., Boostanimehr, H., & Bhargava, V. K. (2011). Green cellular networks: A survey, some research issues and challenges. IEEE Communications Surveys & Tutorials, 13(4), 524–540.CrossRefGoogle Scholar
  21. 21.
    Balasubramanian, N., Balasubramanian, A., & Venkataramani, A. (2009). Energy consumption in mobile phones: A measurement study and implications for network applications. In Proceedings of the 9th ACM SIGCOMM conference on internet measurement conference, Chicago, Illinois, USA (pp 280–293).Google Scholar
  22. 22.
    Hameed Mir, Z., & Filali, F. (2014). LTE and IEEE 802.11p for vehicular networking: A performance evaluation. EURASIP Journal on Wireless Communications and Networking, 2014, 89.CrossRefGoogle Scholar
  23. 23.
    Zheng, K., Zheng, Q., Charzimisios, P., Xiang, W., & Zhou, Y. (2015). Heterogeneous vehicular networking: A survey on architecture, challenges and solutions. IEEE Communication Surveys & Tutorials, 17(4), 2377–2396.  https://doi.org/10.1109/COMST.2015.2440103.CrossRefGoogle Scholar
  24. 24.
    Zoladek, H. (2000). The topological proof of Abel-Ruffini theorem. Topological Methods in Nonlinear Analysis Journal of the Juliusz Schauder Center, 16, 253–265.MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Pesic, P. (2004). “Abel’s proof: An essay on the sources and meaning of mathematical unsolvability,” Appendix B. Cambridge: The MIT Press.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Optoelectronics and Communication EngineeringNational Kaohsiung Normal UniversityKaohsiungTaiwan
  2. 2.Department of MathematicsNational Kaohsiung Normal UniversityKaohsiungTaiwan

Personalised recommendations