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REVIEW 5 major objections 6 minor 37 references

Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A five-tap tapped-delay-line model, with each tap switched on and off by a first-order two-state Markov chain, reproduces the non-stationary fading of a 5G-railway channel at 2.16 GHz, and is validated against independent measurements.

desk verdict A real 5G-R measurement campaign, but the validation is circular and the parameter table has inconsistencies that make the current model unusable as published. read the letter →

arxiv 2501.15729 v1 pith:NIC5QCN5 submitted 2025-01-27 cs.IT math.IT

Xuejian Zhang , Ruisi He , Mi Yang , Jianwen Ding , Ruifeng Chen , Shuaiqi Gao , Ziyi Qi , Zhengyu Zhang
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Bo Ai Zhangdui Zhong
This is my paper · ORCID
classification cs.ITmath.IT
keywords 5G-Railwaystappeddelaylinemodelnon-stationarychannelMarkovchainbirth-deathprocessmeasurementRMSspreadlink-levelsimulation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a five-tap tapped-delay-line (TDL) model, whose taps are switched on and off by a first-order two-state Markov chain, captures the non-stationary behavior of a 5G-railway (5G-R) downlink channel measured at 2.16 GHz with 10 MHz bandwidth on a railway test track. From the measured data it extracts the tap delays, powers, amplitude, phase and Doppler distributions, the Markov state-transition probabilities, and correlations between tap amplitudes. It then validates the model on an independent set of measurements by comparing the probability density of root-mean-square delay spread against the measured data and against the standardized TDL model, finding a much closer match than the standard model. If the claim holds, link-level simulations of 5G-R systems at this band can use the provided parameters instead of assuming stationary fading. The model is intended for the 5G-R dedicated frequency band at 2.16 GHz.

What carries the argument

The central object is the per-tap switching function $z_l(t)\in\{0,1\}$ in the TDL impulse response $h(\tau,t)=\sum_l z_l(t)\alpha_l(t)e^{j[\phi_l(t)+2\pi f_{D,l}(t)]}\delta(\tau-\tau_l(t))$. It is modeled as a first-order two-state Markov chain with transition matrix $T=[[p_{00},p_{01}],[p_{10},p_{11}]]$, where state 1 means the tap is present and state 0 means it is dead; the state is updated every channel coherence time. This mechanism converts a standard WSSUS TDL model into a non-stationary one by letting multipath components appear and disappear. The tap-existence states are extracted from measured power delay profiles using a 6 dB-above-noise-floor threshold, and the transition probabilities are estimated from those states.

What would settle it

Estimate the coherence time directly from the measured channel (for example from the Doppler spread or the autocorrelation of the frequency transfer function) and re-derive the Markov transition probabilities using that interval. If the resulting $p_{00}$, $p_{11}$, and $p_1$ values differ appreciably from Table I, or if the simulated RMS delay spread PDF no longer matches the independent measurement set, the paper's state-interval assumption would be falsified. A simpler check is to repeat the measurement at a different train speed and see whether the transition probabilities change as predicted.

Watch

Extended reading notes

Core claim

The central discovery is that the non-stationarity of the railway channel, understood as the 'birth and death' of resolvable multipath components, can be represented by a per-tap switching function $z_l(t)$ driven by a two-state Markov chain, and that all required parameters can be obtained from a measurement campaign. The resulting model has five taps at relative delays 0, 0.1, 0.2, 0.3, and 0.4 microseconds with average powers 0, -3.14, -17.02, -26.31, and -39.35 dB; the first tap always exists, while later taps have steady-state existence probabilities of 0.92, 0.77, 0.57, and 0.46. Each tap's amplitude follows a tabulated lognormal distribution, its phase is uniformly distributed on $[0,\pi]$, and its Doppler shift is uniformly distributed on $[-160,160]$ Hz for the 80 km/h train speed. The paper also reports the tap-amplitude correlation coefficients, which are symmetric and reach 0.77 between taps 1 and 3. Validation compares the PDF of RMS delay spread from the simulated model, the measured data, and the standard TDL model; the proposed model's distribution is claimed to be much closer to the measurements.

Load-bearing premise

The model's behavior is set by the Markov state decision interval, which the paper identifies with the channel coherence time; however, the coherence time is never estimated from the data, and the text instead uses the 100 ns delay resolution of the sounder as this interval, so the transition probabilities in Table I are only calibrated to that particular snapshot cadence and train speed.

Editorial extensions

If this is right

  • Link-level simulations for 5G-R at 2.16 GHz can directly use the tabulated five-tap parameters, Markov transition matrix, amplitude/phase/Doppler distributions, and tap correlation matrix to generate non-stationary channels.
  • Simulated RMS delay spread from the proposed model matches the measured PDF much better than the standardized TDL model, supporting its use for performance evaluation of 5G-R air interfaces.
  • Because the first tap always exists and the steady-state existence probability decreases with tap index, the model quantifies how often only two or three taps are resolvable in the measured rural scenario.
  • The dominance of $p_{00}$ and $p_{11}$ implies that multipath components persist over successive coherence intervals, so the channel's non-stationarity evolves on a time scale slower than small-scale fading but faster than large-scale effects.
  • The tap-amplitude correlation coefficients, not present in standard WSSUS TDL models, enable simulation of correlated fading across taps for receiver algorithms that exploit delay-domain structure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • We infer that the Markov transition probabilities are tied to the 100 ns state-update interval used in the paper; at a different train speed or snapshot rate the transition matrix would need re-estimation, since the coherence-time basis of the state interval is not independently measured.
  • A testable extension would be to estimate the channel coherence time from the Doppler spectrum or frequency-correlation function and re-derive the transition probabilities, checking whether the model's RMS delay spread fit is preserved.
  • We infer that the reported tap-amplitude correlations, especially the 0.77 value between taps 1 and 3, could be exploited in joint channel estimation for MIMO or multi-band 5G-R links, although the paper itself only tabulates them.
  • If the birth-death process is confirmed to track physical scatterer visibility, the model could be extended to predict handover or beam-management triggers in railway networks, but that connection is not made in the paper.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper reports a channel measurement campaign for a 5G railway private network at 2.16 GHz with 10 MHz bandwidth and uses the data to construct a 5-tap tapped-delay-line (TDL) channel model. The model augments each tap with a binary switching function driven by a first-order two-state Markov chain to represent multipath component birth and death. Parameters are reported for tap delays, powers, lognormal amplitude distributions, phase and Doppler distributions, transition probabilities, and a tap amplitude correlation matrix. The model is compared with measured RMS delay-spread distributions and with the 3GPP 38.901 RMa model, with the claimed result that the proposed model better captures the non-stationarity of the 5G-R channel.

Significance. If the parameter set were internally consistent and independently validated, the model would fill a real gap: a measurement-based non-stationary TDL model for the 5G-R band, useful for link-level simulation. The campaign at the National Railway Track Test Center with a real 5G-R dedicated base station and a train at 80 km/h is valuable, and the reporting of a tap correlation matrix and transition probabilities goes beyond standard WSSUS TDL models. However, as presented, the model cannot be used or reproduced because of fundamental parameter inconsistencies: the tap-count formula conflates delay resolution with coherence time, the amplitude distribution is given two different values, and the validation data are the same data used for fitting. These problems are load-bearing for the central claim.

major comments (5)
  1. [Section III-B, Eq. (3)] In Eq. (3), Tc is called the coherence time, but in the implementation it is set to 100 ns, which is the delay resolution of the 10 MHz sounder rather than a time-domain coherence interval. At 80 km/h and 2.16 GHz, fmax = 160 Hz, so a standard coherence-time estimate is about 1/(4fmax) ≈ 1.56 ms; substituting that value into Eq. (3) yields L = 1, not 5. The advertised 5-tap count therefore rests on the conflation of delay resolution with coherence time, and it is not a robustly derived model parameter.
  2. [Table I and Fig. 3] The tap amplitude parameter is specified inconsistently: Table I reports αl ~ LN(-3.66, 1.08), while the simulation diagram in Fig. 3 states 'LN(-1.91, 0.67)'. Since Fig. 3 is the generator used for the validation in Section III-C, the reader cannot tell which distribution was actually simulated. This ambiguity makes the reported model parameters non-reproducible.
  3. [Section III-B and Section III-C] The validation is circular. Section III-B states that the data from the two measurement cycles not used in [33] are used 'to establish Markov TDL model and then verify it,' and Section III-C validates against the same data. Because the amplitudes, transition probabilities, and correlations are all estimated from these laps, the RMS DS PDF comparison in Fig. 4 partly re-describes the fitting data and provides no independent evidence for the model's predictive accuracy.
  4. [Section III-A and Table I] The Markov state decision interval is defined as the channel coherence time, but the coherence time is never measured or reported. The transition probabilities p00, p11, and p1 in Table I are estimated from state sequences sampled at the sounder's snapshot interval, which is also not stated. These probabilities are therefore tied to an unknown time granularity, and the model cannot be transferred to a different snapshot rate or train speed as claimed.
  5. [Section III-C, Fig. 4] The RMS delay spread is an aggregate statistic that is insensitive to the time scale of MPC birth-death switching; even a WSSUS model could produce a similar RMS DS distribution. The validation therefore does not exercise the Markov switching mechanism, which is the central novelty of the model. A validation of the non-stationary behavior would need to compare metrics such as tap lifetime distributions or state-switching dynamics, not only the RMS DS PDF.
minor comments (6)
  1. [Section III-B, Fig. 3] There are typographical errors: 'Notely' in Section III-B and 'fallowing' in Fig. 3 should be corrected.
  2. [Eq. (2)] The notation in Eq. (2) uses S for the steady-state vector, but S is typeset in a way that suggests a matrix; clarify that S = [p0, p1]^T is a vector and define its relationship to the transition matrix T.
  3. [Section III-B, item 2] The phase distribution is reported as U[0, π] for all taps; since phase is a circular quantity, clarify whether the support is meant to be [0, 2π) or whether the interval [0, π] is intentional.
  4. [References] Reference [28] (Jin and Wang, on MEMS capacitive microwave power sensors) appears unrelated to non-stationary TDL channel modeling and should be replaced or removed.
  5. [Fig. 4] The goodness of fit in Fig. 4 is judged visually; report a quantitative test such as a two-sample Kolmogorov-Smirnov test on the RMS DS distributions to support the claimed agreement.
  6. [Section II] The paper states that two cycles were previously used in [33] and two cycles are used here, but the total number of measurement laps and the snapshot interval are not reported; include this information for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; model parameters are fitted to one pair of measurement cycles and validated against a separate pair, and no load-bearing claim reduces by construction to its own inputs.

full rationale

The paper's parameter extraction and validation use distinct measurement cycles. Section III-B states that two cycles were previously used in [33] and that "the data from the other two cycles" are used to establish the Markov TDL model; Section III-C then says "We use the other group of measured data mentioned in Section III-B to validate the proposed TDL model," i.e., the two cycles not used for fitting. This is a hold-out split rather than an in-sample re-description. The tap-count rule in Eq. (3) is a standard formula (max RMS delay spread divided by time resolution, citing [25]); although the text mislabels the 100 ns delay resolution as 'coherence time,' that is a parameterization/correctness issue, not a circular one, because the tap count is not used as evidence for the Markov switching claim. The Markov chain form, Lognormal amplitude, uniform phase, and uniform Doppler distributions are adopted as modeling choices from prior literature ([12], [24], [26], [35]), not derived from the validation statistic. The self-citations ([30], [33]) supply calibration methodology and measured delay-spread data, and they do not smuggle in the central non-stationarity result. Overall, no derivation step reduces a prediction to a fitted input or imports a conclusion through a self-citation chain, so the circularity score is 0.

Assumptions & free parameters 11 free parameters · 6 assumptions · 1 invented entities

The central claim rests on a small number of parameters fitted to one measurement campaign. The free parameters are tap powers, amplitudes, Markov transition probabilities, and correlations, all derived from the same two laps. The key structural assumptions (state interval, uniform Doppler, uniform phase, lognormal amplitude, first-order Markov) are either cited from prior work or chosen ad hoc, and the paper does not validate them against independent data.

free parameters (11)
  • Number of TDL taps L = 5
    Determined by Eq. (3), but the stated inputs (max RMS DS slightly >350 ns, resolution 100 ns) give floor(350/100)+1=4, not 5; L=5 is in effect a chosen value, possibly using ~400 ns.
  • Tap 2 relative power = -3.14 dB
    Extracted from normalized average power delay profile (Table I).
  • Tap 3 relative power = -17.02 dB
    Extracted from normalized average power delay profile (Table I).
  • Tap 4 relative power = -26.31 dB
    Extracted from normalized average power delay profile (Table I).
  • Tap 5 relative power = -39.35 dB
    Extracted from normalized average power delay profile (Table I).
  • Lognormal amplitude distribution parameters = LN(-3.66, 1.08) in Table I; LN(-1.91, 0.67) in Fig. 3
    Fitted to measured tap amplitudes above a 6 dB threshold; values conflict between table and flowchart.
  • Markov transition probabilities for Tap 2 = p00=0.9227, p11=0.9485, p1=0.9209
    Estimated from state sequences assigned by a tap threshold (Table I).
  • Markov transition probabilities for Tap 3 = p00=0.8403, p11=0.8571, p1=0.7670
    Estimated from state sequences assigned by a tap threshold (Table I).
  • Markov transition probabilities for Tap 4 = p00=0.7668, p11=0.6975, p1=0.5676
    Estimated from state sequences assigned by a tap threshold (Table I).
  • Markov transition probabilities for Tap 5 = p00=0.7978, p11=0.8875, p1=0.4647
    Estimated from state sequences assigned by a tap threshold (Table I).
  • Tap amplitude correlation coefficients = 10 upper-triangular values in Table II
    Estimated from measured tap magnitudes; handling of absent taps (state 0) is not described.
assumptions (6)
  • domain assumption WSSUS assumption is invalid in high-speed railway channels
    Invoked in Sections I and III-A citing [22], [23], [31]; motivates the Markov switching structure.
  • ad hoc to paper First-order two-state Markov chain adequately captures MPC birth-death dynamics
    Chosen in Section III-A following [12], [24]; no model-order selection or comparison with higher-order chains.
  • domain assumption Doppler shift distribution is uniform on [-fmax, fmax]
    Section III-B item 3 assumes this based on [20], [35] rather than extracting Doppler from measurements; fmax=160 Hz computed from speed.
  • domain assumption Tap phase follows uniform distribution on [0, pi]
    Section III-B item 2 asserts uniformity; no goodness-of-fit test is shown.
  • domain assumption Amplitude distribution is lognormal
    Section III-B item 2 fits lognormal; Fig. 2(b) shows a visual fit but no statistical test.
  • ad hoc to paper State decision interval equals channel coherence time
    Section III-A defines it but never estimates coherence time; Section III-B Eq. (3) uses Tc=100 ns as delay resolution, an inconsistency.
invented entities (1)
  • Two-state Markov switching function z_l(t)
    purpose: Models the birth and death of each TDL tap to capture non-stationarity
    A modeling device introduced in Eq. (1); no independent falsifiable handle outside the fitted model.

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Cite this review

Pith. "Pith review of Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways." pith.science (2026). https://pith.science/paper/NIC5QCN5

@misc{pith2026250115729,
  author       = {Pith},
  title        = {Pith review of: Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NIC5QCN5}},
  note         = {Machine review of arXiv:2501.15729}
}
read the original abstract

5G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non stationarity. Key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes are also obtained. Finally, accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design, optimization, and link-level simulation and validation of 5G-R systems.

Figures

Figures reproduced from arXiv: 2501.15729 by the authors.

Figure 1
Figure 1. Measurement system and scenario. Satellite System (GNSS) signals are employed. A frequency of 2.16 GHz with bandwidth of 10 MHz is used during the measurement campaign, which is consistent with the 5G-R dedicated test frequency band allocated by China, resulting in a resolution delay of ∆τ = 100 ns. The test train maintain a constant speed of 80 km/h as shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) An example of PDP in one snapshot. (b) CDFs of the Lognormal [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Comparison of normalized RMS DS PDF between Markov-based TDL [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (1 more)
Figure 3
Figure 3. Figure 3: Diagram of Markov-based TDL channel simulation. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png]

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Works this paper leans on

37 extracted references · 37 canonical work pages

  1. [33]

    Measurement-based channel characterization and modeling for 5G-Railways at 2.16 GHz,

    X. Zhang et al. , “Measurement-based channel characterization and modeling for 5G-Railways at 2.16 GHz,” in Proc. IEEE 16th Int. Conf. Wireless Commun. Signal Process. (WCSP) , 2024, pp. 254–259

    work page 2024

  2. [1]

    Channel models for performance evaluation of wireless systems in railway environments,

    M. Berbineau et al. , “Channel models for performance evaluation of wireless systems in railway environments,” IEEE Access , vol. 9, pp. 45 903–45 918, 2021

    work page 2021

  3. [2]

    Site-specific radio channel representation for 5G and 6G,

    T. Zemen et al. , “Site-specific radio channel representation for 5G and 6G,” IEEE Commun. Mag. , pp. 1–8, 2024. 5

    work page 2024

  4. [3]

    High-speed railway communications: From GSM-R to LTE-R,

    R. He et al. , “High-speed railway communications: From GSM-R to LTE-R,” IEEE V eh. Technol. Mag., vol. 11, no. 3, pp. 49–58, 2016

    work page 2016

  5. [4]

    Channel modeling for future high-speed railway communication systems: A survey,

    T. Zhou, H. Li, Y . Wang, L. Liu, and C. Tao, “Channel modeling for future high-speed railway communication systems: A survey,” IEEE Access, vol. 7, pp. 52 818–52 826, 2019

    work page 2019

  6. [5]

    5G for railways: Next generation railway dedicated communications,

    R. He et al. , “5G for railways: Next generation railway dedicated communications,” IEEE Commun. Mag. , vol. 60, no. 12, pp. 130–136, 2022

    work page 2022

  7. [6]

    Mainline railway modeled with 2100 MHz 5G-R channel based on measured data of test line of loop railway,

    Y . Liang, H. Li, Y . Li, and A. Li, “Mainline railway modeled with 2100 MHz 5G-R channel based on measured data of test line of loop railway,” Symmetry, vol. 16, no. 4, p. 431, 2024

    work page 2024

  8. [7]

    European union horizon 2020 research and innovation programme,

    5GRail, “European union horizon 2020 research and innovation programme,” 2020. [Online]. Available: https://5grail.eu/

    work page 2020

Show all 37 references
  1. [8]

    Radio communication scenarios in 5G-railways,

    R. He et al. , “Radio communication scenarios in 5G-railways,” China Commun., vol. 20, no. 9, pp. 235–246, 2023

    2023

  2. [9]

    Measurements and modeling of large-scale channel characteristics in subway tunnels at 1.8 and 5.8 GHz,

    X. Zhang et al. , “Measurements and modeling of large-scale channel characteristics in subway tunnels at 1.8 and 5.8 GHz,” IEEE Antennas Wireless Propag. Lett. , vol. 22, no. 3, pp. 561–565, 2023

    2023

  3. [10]

    COST CA20120 INTERACT framework of artificial intelligence based channel modeling,

    R. He, N. D. Cicco, B. Ai, M. Yang, Y . Miao, and M. Boban, “COST CA20120 INTERACT framework of artificial intelligence based channel modeling,” 2024. [Online]. Available: https://arxiv.org/abs/2411.11798

    2024 arXiv

  4. [11]

    An empirical path loss model and fading analysis for high-speed railway viaduct scenarios,

    R. He, Z. Zhong, B. Ai, and J. Ding, “An empirical path loss model and fading analysis for high-speed railway viaduct scenarios,” IEEE Antennas Wirel. Propag. Lett. , vol. 10, pp. 808–812, 2011

    2011

  5. [12]

    The dynamic evolution of multipath components in high-speed railway in viaduct scenarios: From the birth-death process point of view,

    L. Liu, C. Tao, J. Qiu, T. Zhou, R. Sun, and H. Chen, “The dynamic evolution of multipath components in high-speed railway in viaduct scenarios: From the birth-death process point of view,” in Proc. IEEE 23rd Int. Symp. Pers. Indoor Mobile Radio Commun. , 2012, pp. 1774– 1778

    2012

  6. [13]

    Measurements and analysis of short-term fading behavior in high-speed railway communication networks,

    T. Zhou, C. Tao, S. Salous, and L. Liu, “Measurements and analysis of short-term fading behavior in high-speed railway communication networks,” IEEE Trans. V eh. Technol., vol. 68, no. 1, pp. 101–112, 2019

    2019

  7. [14]

    Study on channel model for frequencies from 0.5 to 100 GHz (release 18),

    3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz (release 18),” Tech. Rep. 38.901, V18.0.0 , Mar. 2024

    2024

  8. [15]

    WINNER II channel models,

    I. Winner, “WINNER II channel models,” IST-4-027756, 2007

    2007

  9. [16]

    He and B

    R. He and B. Ai, Wireless Channel Measurement and Modeling in Mobile Communication Scenario: Theory and Application . CRC Press, 2024

    2024

  10. [17]

    Broadband wireless channel in composite high-speed railway scenario: Measurements, sim- ulation, and analysis,

    J. Ding, L. Zhang, J. Yang, B. Sun, and J. Huang, “Broadband wireless channel in composite high-speed railway scenario: Measurements, sim- ulation, and analysis,” Wireless Commun. Mobile Comput. , vol. 2017, no. 1, p. 2897636, 2017

    2017

  11. [18]

    Emulation of radio technologies for railways: A tapped- delay-line channel model for tunnels,

    H. Qiu et al. , “Emulation of radio technologies for railways: A tapped- delay-line channel model for tunnels,” IEEE Access , vol. 9, pp. 1512– 1523, 2021

    2021

  12. [19]

    An efficient MIMO channel model for LTE-R network in high-speed train environment,

    J. Yang et al. , “An efficient MIMO channel model for LTE-R network in high-speed train environment,” IEEE Transactions on V ehicular Technology, vol. 68, no. 4, pp. 3189–3200, 2019

    2019

  13. [20]

    Measurement- based delay and doppler characterizations for high-speed railway hilly scenario,

    Y . Zhang, Z. He, W. Zhang, L. Xiao, and S. Zhou, “Measurement- based delay and doppler characterizations for high-speed railway hilly scenario,” Int. J. Antennas Propagation , vol. 2014, no. 1, p. 875345, 2014

    2014

  14. [21]

    Measurement-based tapped- delay-line (tdl) models for wireless channels under high speed railway scenarios at 2.6 ghz,

    W. Qian, X. Chunxiu, Z. Min, and Z. Siyu, “Measurement-based tapped- delay-line (tdl) models for wireless channels under high speed railway scenarios at 2.6 ghz,” in Proc. 21st Int. Conf. Telecommun. (ICT) , 2014, pp. 353–357

    2014

  15. [22]

    3D non- stationary GBSMs for high-speed train tunnel channels,

    Y . Liu, L. Feng, J. Sun, W. Zhang, C.-X. Wang, and P. Fan, “3D non- stationary GBSMs for high-speed train tunnel channels,” in Proc. IEEE 87th V eh. Technol. Conf. (VTC Spring) , Porto, Portugal, Jun. 2018, pp. 1–5

    2018

  16. [23]

    A general 3-D non-stationary 5G wireless channel model,

    S. Wu, C.-X. Wang, e.-H. M. Aggoune, M. M. Alwakeel, and X. You, “A general 3-D non-stationary 5G wireless channel model,” IEEE Trans. Commun., vol. 66, no. 7, pp. 3065–3078, 2018

    2018

  17. [24]

    Markov chain based channel characterization for high speed railway in viaduct scenarios,

    L. Liu, C. Tao, R. Sun, H. Chen, and Z. Lin, “Markov chain based channel characterization for high speed railway in viaduct scenarios,” in Proc. IEEE Int. Conf. Commun. (ICC) , 2014, pp. 5896–5901

    2014

  18. [25]

    Vehicle–vehicle channel models for the 5- GHz band,

    I. Sen and D. W. Matolak, “Vehicle–vehicle channel models for the 5- GHz band,” IEEE Trans. Intell. Transp. Syst. , vol. 9, no. 2, pp. 235–245, 2008

    2008

  19. [26]

    Measurement-based determination of parameters for non- stationary TDL models with reduced number of taps,

    N. Hassan, M. K ¨aske, C. Schneider, G. Sommerkorn, R. Thom ¨a, and D. Matolak, “Measurement-based determination of parameters for non- stationary TDL models with reduced number of taps,” IET Microw., Antennas Propag., vol. 14, no. 14, pp. 1719–1732, 2020

    2020

  20. [27]

    A TDL based non-wssus vehicle-to-vehicle channel model,

    Y . Li et al., “A TDL based non-wssus vehicle-to-vehicle channel model,” Int. J. Antennas Prop. , vol. 2013, no. 1, p. 103461, 2013

    2013

  21. [28]

    Study on static deflection model of MEMS capacitive microwave power sensors,

    Y . Jin and D. Wang, “Study on static deflection model of MEMS capacitive microwave power sensors,” Chinese J Electron, vol. 33, no. 5, pp. 1188–1195, 2024

    2024

  22. [29]

    Narrowband channel measurements and statistical characterization in subway tunnels at 1.8 and 5.8 GHz,

    X. Zhang et al. , “Narrowband channel measurements and statistical characterization in subway tunnels at 1.8 and 5.8 GHz,” IEEE Trans. V eh. Technol., vol. 73, no. 7, pp. 10 228–10 240, 2024

    2024

  23. [30]

    V2V channel characterization and modeling for underground parking garages,

    M. Yang et al. , “V2V channel characterization and modeling for underground parking garages,” China Commun. , vol. 16, no. 9, pp. 93– 105, 2019

    2019

  24. [31]

    Non-stationary channel characterization for high-speed railway under viaduct scenarios,

    L. Liu et al. , “Non-stationary channel characterization for high-speed railway under viaduct scenarios,” Chin. Sci. Bull. , vol. 59, pp. 4988– 4998, 2014

    2014

  25. [32]

    Measurement-based markov modeling for multi-link channels in railway communication systems,

    B. Zhang et al. , “Measurement-based markov modeling for multi-link channels in railway communication systems,”IEEE Trans. Intell. Transp. Syst., vol. 20, no. 3, pp. 985–999, 2019

    2019

  26. [34]

    Dynamic V2V channel measurement and modeling at street intersection scenarios,

    M. Yang et al. , “Dynamic V2V channel measurement and modeling at street intersection scenarios,” IEEE Trans. Antennas Propagat. , vol. 71, no. 5, pp. 4417–4432, May 2023

    2023

  27. [35]

    Analyzing non-stationary TDL channel models based on measurement data,

    M. Ansari, L. Thielecke, and T. K ¨urner, “Analyzing non-stationary TDL channel models based on measurement data,” in Proc. IEEE 100th V eh. Technol. Conf. (VTC Fall) , 2024, pp. 1–6

    2024

  28. [36]

    Orthogonal delay-doppler division multiplexing modula- tion with tomlinson-harashima precoding,

    Y . Ma et al. , “Orthogonal delay-doppler division multiplexing modula- tion with tomlinson-harashima precoding,” IEEE Trans. Commun. , pp. 1–17, 2024

    2024

  29. [37]

    MIMO-TDL model parameter estimation from V2I channel sounding,

    N. Hassan, D. A. Dupleich, C. Schneider, R. Thom ¨a, and G. Del Galdo, “MIMO-TDL model parameter estimation from V2I channel sounding,” in Proc. 15th Eur . Conf. Antennas Propag. (EuCAP) , 2021, pp. 1–5

    2021

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.