Spectrum Sensing 6G Full duplex Cognitive Radio OFDM Idle cells FBMC Energy Harvesting Ultra-dense small cell networks Bi-Directional stochastic geometry interference management Asynchronous Transmission SINR mismatch problem HetNet Dynamic TDD NOMA Cross-link interference Spectral efficiency bursty traffic model mode selection Device-to-Device (D2D) OQAM Channel estimation error multi-spectral Ultra-dense small cell Heterogeneous channel estimation capability synchronization Bi-directional full-duplex Preamble Intentional frequency offset (IFO) Asynchronism Shortened TTI Deep learning MLP Vehicle-to-vehicle communication Low Earth orbits (LEO) satellite Railway Simultaneous Sensing and Transmission full-duplex relay Cellular networks Latency selection diversity Cognitive relay networks MIMO K-S statistics Long Term Evolution-Advanced 5G networks interference coordination full-duplex cellular interference mitigation sensing duration Correlated MIMO outage probability transmission capacity (TC) Two-way communications self-interference cancellation in-band full-duplex system automatic repeat request (ARQ) achievable sum rate Coexisting network interference to noise ratio link reliability tabu-search massive connectivity resource size control Coexistence scenarios Resource management spectrum sharing mixed numerology Multiple input multiple output (MIMO) New radio non-terrestrial network (NR-NTN) full-spreading NOMA Intentional time offset LTE-based V2V CP-OFDM Singular Vale Decomposition non-orthogonal multiple access Non-orthogonal multiple access (NOMA) Complexity Cooperative systems Reliability B5G Multiple access Cell-free HST Mobility flexible duplex 5G Asynchronous non-orthogonal multiple access (NOMA) Edge computing Full-duplex Filtered OFDM Vehicle-to-Vehicle C-V2V Grant-free Transmission Sub-band filtering Computation offloading TDD configuration Degree of freedom (DoF) Satellite communication
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