OFDM Multiple Antennas Cognitive Radio Relay Synchronization CDMA Channel Estimation Spectrum Sharing Interference Cancellation Full duplex Spectrum Sensing Neural Networks Resource Allocation Stochastic Geometry Equalizer Bi-Directional Feedback Femtocell Heterogeneous Networks Energy Harvesting Device-to-Device (D2D) Cell Search Spectral efficiency NOMA FBMC interference management Dynamic TDD Cross-link interference SINR mismatch problem HetNet Idle cells Ultra-dense small cell networks achievable sum rate outage probability multi-spectral full-duplex relay selection diversity bursty traffic model mode selection Handoff CP-OFDM non-orthogonal multiple access self-interference cancellation Singular Vale Decomposition interference mitigation full-duplex cellular interference coordination Cognitive relay networks Time spreading beamforming Link adaptation Heterogeneous channel estimation capability coexistence MU-MIMO full-spreading NOMA GFDM Bi-directional full-duplex OQAM sensing duration Correlated MIMO Simultaneous Sensing and Transmission transmission capacity (TC) Two-way communications in-band full-duplex system automatic repeat request (ARQ) UWB flexible duplex TS-W-OFDM Windowing time-frequency efficiency eigen decomposition pilot signal 5G networks prototype filter Aggregate interference Long Term Evolution-Advanced interference to noise ratio resource size control link reliability mixed numerology Vehicle-to-vehicle communication LTE-based V2V Coexistence scenarios Resource management Filtered OFDM Cooperative systems Complexity tabu-search Deep learning Reliability Vehicle-to-Vehicle C-V2V HST Mobility Preamble 5G Grant-free Transmission Asynchronous Transmission Railway MLP massive connectivity
Status : Accepted 
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 update Accepted  2020-12