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
-
2024.10 Sunday Monday Tuesday Wednesday Thursday Friday Saturday 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
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
[IEEE Trans. Veh. Technol.]
조회 35745
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 |
.
-
2024.10 Sunday Monday Tuesday Wednesday Thursday Friday Saturday 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
-
2024.10 Sunday Monday Tuesday Wednesday Thursday Friday Saturday 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
카테고리
- 전체(6)
- [IEEE J. Sel. Areas Commun.] (0)
- [IEEE Commun. Mag.] (1)
- [IEEE Trans. Commun.] (0)
- [IEEE Commun. Lett.] (3)
- [IEEE Trans. Wireless Commun.] (15)
- [IEEE Wireless Commun. Mag.] (0)
- [IEEE Wireless Commun. Lett.] (3)
- [IEEE Trans. Veh. Technol.] (6)
- [IEEE Trans. Signal Process.] (0)
- [IEEE Trans. Broadcast.] (0)
- [IEEE Trans. Magn.] (0)
- [IEEE Trans. Neural Netw.] (0)
- [IEEE Trans. Ind. Electron.] (0)
- [IEEE Trans. Consum. Electron.] (0)
- [IEEE Signal Process. Lett.] (0)
- [Electron. Lett.] (0)
- [IEICE Trans. Commun.] (0)
- [IEICE Trans. Fund.] (0)
- [Neuro Computing] (0)
- [Optical Engineering] (0)
- [IEEE Comm. Surveys & Turotials] (3)
- [IEEE Access] (5)
- [Other Journals] (1)