OFDM Multiple Antennas Cognitive Radio Relay CDMA Synchronization Channel Estimation Spectrum Sharing Interference Cancellation Resource Allocation Spectrum Sensing Neural Networks Full duplex Stochastic Geometry Equalizer Bi-Directional Feedback Energy Harvesting Heterogeneous Networks Femtocell Device-to-Device (D2D) Idle cells Cross-link interference FBMC Spectral efficiency Cell Search SINR mismatch problem NOMA Ultra-dense small cell networks HetNet interference management Dynamic TDD outage probability selection diversity achievable sum rate bursty traffic model Cognitive relay networks mode selection multi-spectral 5G Complexity Singular Vale Decomposition OQAM tabu-search Filtered OFDM TDD configuration flexible duplex Handoff GFDM Heterogeneous channel estimation capability self-interference cancellation in-band full-duplex system Channel estimation error coexistence CP-OFDM MU-MIMO automatic repeat request (ARQ) Two-way communications UWB full-duplex relay full-duplex cellular Simultaneous Sensing and Transmission Correlated MIMO transmission capacity (TC) sensing duration Bi-directional full-duplex Vehicle-to-Vehicle prototype filter pilot signal Coexistence scenarios resource size control Vehicle-to-vehicle communication link reliability interference to noise ratio eigen decomposition TS-W-OFDM Resource management Cooperative systems LTE-based V2V Aggregate interference time-frequency efficiency mixed numerology Windowing Reliability C-V2V Asynchronous Transmission Full-duplex Computation offloading Grant-free Transmission Preamble full-spreading NOMA massive connectivity Edge computing Multiple access MLP Deep learning Railway Mobility interference mitigation HST non-orthogonal multiple access
Status : Published 
Date : 2020-12 
Title : RSRP-based Doppler Shift Estimator using Machine Learning in High-Speed Train Systems 
Authors : Taehyung Kim, Kyeongjun Ko, Incheol Hwang, Daesik Hong, Sooyong Choi,and Hanho Wang 
Journal : IEEE Transactions on Vehicular Technology 
Abstract : 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 oneto-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. 
URL : https://ieeexplore.ieee.org/document/9292473 

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» [IEEE Trans. Veh. Technol.] Taehyung Kim, Kyeongjun Ko, Incheol Hwang, Daesik Hong, Sooyong Choi and Hanho Wang, "RSRP-Based Doppler Shift Estimator Using Machine Learning in High-Speed Train Systems," in IEEE Transactions on Vehicular Technology, Jan. 2021 Published  2020-12