Measurement-Based LoS/NLoS Channel Modeling for Hot-Spot Urban Scenarios in UMTS Networks

Chen, Jiajing; Yin, Xuefeng; Tian, Li; Zhang, Nan; He, Yongyu; Cheng, Xiang; Duan, Weiming; Boqué, Sílvia Ruiz

doi:10.1155/2014/454976

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…the outof-channel interference [28], [29]. Nowadays, by utilizing the signals of 3.84 MHz-bandwidth from Universal Mobile Terrestrial System (UMTS) or the signals of 20 MHz bandwidth from the Long-Term-Evolution (LTE)-Advanced systems deployed along the HST railway, refined wideband characteristics of the channels may be explored with the elaborated design [30].…”

mentioning

confidence: 99%

Empirical Geometry-Based Random-Cluster Model for High-Speed-Train Channels in UMTS Networks

Yin

Cai

Cheng

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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In this paper, a recently conducted measurement campaign for high-speed-train (HST) channels is introduced, where the downlink signals of an in-service Universal Mobile Terrestrial System (UMTS) deployed along an HST railway between Beijing and Shanghai were acquired. The channel impulse responses (CIRs) are extracted from the data received in the common pilot channels (CPICHs). Within 1318 km, 144 base stations (BSs) were detected. Multipath components (MPCs) estimated from the CIRs are clustered and associated across the time slots. The results show that, limited by the sounding bandwidth of 3.84 MHz, most of the channels contain a single line-of-sight (LoS) cluster, and the rest consists of several LoS clusters due to distributed antennas, leaking cable, or neighboring BSs sharing the same CPICH. A new geometry-based random-cluster model is established for the clusters' behavior in delay and Doppler domains. Different from conventional models, the time-evolving behaviors of clusters are characterized by random geometrical parameters, i.e., the relative position of BS to railway, and the train speed. The distributions of these parameters, and the per-cluster path loss, shadowing, delay, and Doppler spreads, are extracted from the measurement data. Index Terms-Geometry-based stochastic model, high speed train, random cluster model, time-variant channel, Universal Mobile Terrestrial System.

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confidence: 99%

Empirical Geometry-Based Random-Cluster Model for High-Speed-Train Channels in UMTS Networks

Yin

Cai

Cheng

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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“…The EPA model has the lowest NLOS probability, and the ETU model has the highest NLOS probability. It has to be pointed out that the specific NLOS probability might vary with different LOS/NLOS identification approaches and threshold settings (Benedetto et al., 2007; Chen et al., 2014; Del Peral‐Rosado et al., 2016).…”

Section: Lte Frame Structure and Multipath Propagation Channelsmentioning

confidence: 99%

Performance comparison of time‐of‐arrival estimation techniques for LTE signals in realistic multipath propagation channels

Wang

Morton

2020

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Long term evolution (LTE) signals have the potential for use in positioning, especially in challenging environments. The time-of-arrival (TOA)-based technique supported by LTE is attractive due to its high positioning accuracy. However, it is vulnerable to multipath propagation effects in typical LTE channels. This paper will summarize several existing advanced TOA estimators for LTE signals, i.e., first peak detection, information theoretic criteria, super-resolution algorithm, and delay-lock loop (DLL). Later, the paper will evaluate the TOA estimation performances of these techniques with multipath propagation effects and varying signal conditions using simulations. For the DLL, the multipath error envelope metric is assessed for different signal bandwidths. The root mean square errors of the TOA estimations are compared to evaluate suitable TOA estimators under various conditions. Finally, some other performance characteristics of these techniques are also discussed. K E Y W O R D S long term evolution, multipath propagation channel, time-of-arrival estimation This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In this scenario, UEs are located below rooftops, typically at 1.5-15 m depending on the heights of surrounding buildings [14]. UEs may be located on different floors of the buildings.…”

Section: Urban Micro-cell O2imentioning

confidence: 99%

Device‐to‐device channel measurements and models: a survey

Cheng

et al. 2015

IET Communications

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Measurement-Based LoS/NLoS Channel Modeling for Hot-Spot Urban Scenarios in UMTS Networks

Cited by 6 publications

References 34 publications

Empirical Geometry-Based Random-Cluster Model for High-Speed-Train Channels in UMTS Networks

Empirical Geometry-Based Random-Cluster Model for High-Speed-Train Channels in UMTS Networks

Performance comparison of time‐of‐arrival estimation techniques for LTE signals in realistic multipath propagation channels

Device‐to‐device channel measurements and models: a survey

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