Advertisement

Deep Learning-Based V2V Channel Estimations Using VNETs

  • Qi SongEmail author
  • Tian Lan
  • Xuanxuan Tian
  • Tingting Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

The development of cooperative intelligent transportation systems brings new challenges to wireless communication technologies, where the channel estimation becomes more and more important. In this paper, a novel data-driven channel estimation method based on deep learning framework is adopted. Based on the feedforward neural network, the VNET neural network based on the convolutional neural network is proposed. The simulations and practical measurements are also provided to verify the performance advantages. The results show the achieved performance advantages of the proposed VNET-based method, which is shown to be an effective solution.

Keywords

Channel estimation Neural network OFDM Deep learning CNN 

References

  1. 1.
    Bimeyer, N., St ubing, H., Schoch, E., Gotz, S., Stolz, J.P., Lonc, B.: A generic public key infrastructure for securing Carto-X communication. In: 18th World Congress on Intelligent Transport Systems (2011)Google Scholar
  2. 2.
    David, C.M., Caroline, C.L., Benjamin, D., Douglas, Y.: Driver distraction and advanced vehicle assistive systems (ADAS): investigating effects on driver behavior. Adv. Human Aspects Transp. 484, 1015–1022 (2016)Google Scholar
  3. 3.
    Erica, D., Omar, A.S., Azhar, S., Haider, K., Carpenter, D.O.: Road traffic injury as a major public health issue in the Kingdom of Saudi Arabia: a review. Front Public Health 4, 215 (2016).  https://doi.org/10.3389/fpubh.2016.00215
  4. 4.
    X. Wang, L. Gao, S. Mao and S. Pandey, 2017. CSI-based fingerprinting for indoor localization: A deep learning approach, IEEE Trans. Veh. Technol., vol. 66, no. 1, pp. 763–776, Jan. 2017Google Scholar
  5. 5.
    OShea, T.J., Hoydis, J.: An introduction to machine learning communications systems. CoRR (2017). arXiv:1702.00832
  6. 6.
    Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., Hanzo, L.: Machine learning paradigms for next-generation wireless networks. IEEE Wirel. Commun. 24(2), 98–105 (2017)CrossRefGoogle Scholar
  7. 7.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT Press (2016). http://www.deeplearningbook.org
  8. 8.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2(4), 303–314 (1989)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  10. 10.
    Ye, H., Li, G.Y.: Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel. Commun. Lett. 7(1) (2018)CrossRefGoogle Scholar
  11. 11.
    Papa, G., Clemencon, S., Bellet, A.: SGD algorithms based on incomplete U-statistics: large-scale minimization of empirical risk. Neural Inf. Process. Syst. 1027–1035 (2015)Google Scholar
  12. 12.
    Vincent, P., et al.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 3371–3408 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Qi Song
    • 1
    Email author
  • Tian Lan
    • 1
  • Xuanxuan Tian
    • 1
  • Tingting Zhang
    • 1
  1. 1.Shenzhen Graduate SchoolCommunication Engineering Research Center, Harbin Institute of TechnologyShenzhenChina

Personalised recommendations