Based on Deep Learning CSI Recovery for Uplink Massive Device Dynamic Internet of Thing

  • Yue XiuEmail author
  • Wenyuan Wang
  • Yongliang Shen
  • Zhongpei Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)


This paper investigates uplink massive MIMO communication scenarios in dynamic Internet of things (IoT) networks. In this paper, dynamic IoT mainly consists of the Internet of vehicles (IoV) and the original IoT network. Because the speed of vehicle is very fast, the number of users is constantly changing in the IoT network, which leads the structure of the IoT network to change. We mainly consider how to obtain the channel state information (CSI) of active users. Due to active users and inactive users, the system model is considered a sparse structure. This structure inspired us to give an algorithm suitable for the sparse structure and obtain more accurate channel state information of dynamic IoT networks, though these numerical results, under the premise of guaranteeing performance, can greatly reduce the complexity of the algorithm.


Deep learning (DL) Dynamic Internet of things (DIoT) Adaptive compressive sensing Massive MIMO Sparse structure 


  1. 1.
    Boccardi, F., Heath, R.W., Lozano, A., et al.: Five disruptive technology directions for 5G. IEEE Commun. Mag. 52(2), 74–80 (2014)CrossRefGoogle Scholar
  2. 2.
    Wang, P., Li, Y., Song, L., et al.: Multi-gigabit millimeter wave wireless communications for 5G: from fixed access to cellular networks. IEEE Commun. Mag. 53(1), 168–178 (2015)CrossRefGoogle Scholar
  3. 3.
    Niu, Y., Li, Y., Jin, D., et al.: A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges. Wireless Netw. 21(8), 2657–2676 (2015)CrossRefGoogle Scholar
  4. 4.
    Rusek, F., Hu, S.: Sequential channel estimation in the presence of random phase noise in NB-IoT systems. arXiv:1706.04350 (2017)
  5. 5.
    Hu, S., Berg, A., Li, X., et al.: Improving the performance of OTDOA based positioning in NB-IoT system. arXiv:1704.05350 (2017)
  6. 6.
    Zhang, Z., Wang, X., Zhang, Y., et al.: Grant-free rateless multiple access: a novel massive access scheme for internet of things. IEEE Commun. Lett. 20(10), 2019–2022 (2016)CrossRefGoogle Scholar
  7. 7.
    Alkhateeb, A., El Ayach, O., Leus, G., et al.: Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE J. Sel. Top. Signal Process. 8(5), 831–846 (2014)CrossRefGoogle Scholar
  8. 8.
    Liu, C., Li, M., Collings, I.B., et al.: Design and analysis of transmit beamforming for millimeter wave base station discovery. IEEE Trans. Wireless Commun. 16(2), 797–811 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yue Xiu
    • 1
    Email author
  • Wenyuan Wang
    • 1
  • Yongliang Shen
    • 1
  • Zhongpei Zhang
    • 1
  1. 1.National Key Laboratory of Science and Technology on CommunicationsUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations