RSRP-Based Doppler Shift Estimator Using Machine Learning in High-Speed Train Systems

Taehyung Kim, Kyeongjun Ko, Incheol Hwang, Daesik Hong, Sooyong Choi, Hanho Wang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In the fifth-generation (5G) high-speed train (HST) system operating in the millimeter-wave (mmWave) band, a much higher Doppler shift occurs. Doppler shift severely degrades reception performance in orthogonal frequency division multiplexing (OFDM)-based wireless communication systems. The performance of the Doppler shift estimator is directly related to safety in the HST because the 5G HST system is used for train control. Therefore, it is necessary to develop a fast and accurate Doppler shift estimator (DSE) with low complexity. In this paper, we propose a new machine learning-based DSE (MLDSE). Taking note of the fact that an HST travels the same path repeatedly, the MLDSE estimates the Doppler shift by using the reference signal received power (RSRP) values measured by the mobile receiver at all times. However, since there is a one-to-many mapping problem when the RSRP values reflecting the 5G beam sweeping and selection correspond to Doppler shifts, machine learning cannot be performed. To solve this problem, we design an RSRP ambiguity reducer (AR) for the machine learning input so that the pattern of RSRP values can be mapped and learned into corresponding Doppler shifts. As a result, MLDSE can estimate Doppler shift more accurately than any HST DSEs known to the authors. In addition, an MLDSE consisting of only three layers is superior to the conventional techniques in terms of computational complexity as well as estimation accuracy.

Original languageEnglish
Article number9292473
Pages (from-to)371-380
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Issue number1
Publication statusPublished - 2021 Jan

Bibliographical note

Funding Information:
Manuscript received April 5, 2020; revised August 3, 2020 and October 5, 2020; accepted December 6, 2020. Date of publication December 11, 2020; date of current version February 12, 2021. This work was supported in part by R & D Program of Korea Railroad Research Institute, and in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2018R1D1A1B07050143. The review of this article was coordinated by Prof. T. Kurner. (Corresponding author: Hanho Wang.) Taehyung Kim, Incheol Hwang, and Daesik Hong are with the Information Telecommunication Lab., School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:;;

Publisher Copyright:
© 1967-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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