Statistical Modeling of Signal Variation for Propagation Along a Lift Shaft

Mao, Xiao Hong; Lee, Yee Hui; Ng, Boon Chong

doi:10.1109/lawp.2010.2061831

Cited by 15 publications

(14 citation statements)

References 15 publications

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“…In the following, the amplitude variations are studied statistically. Figure 6 show the plots of the Akaike weights for different candidate members as well as the normalized mean PDP (blue curve) obtained from the experiment results in campus [6]. Similar plots for the ship environment are shown in parameter from campus measurements [6].…”

Section: Statistically Modeling Of the Signal Variationsmentioning

confidence: 71%

“…Figure 6 show the plots of the Akaike weights for different candidate members as well as the normalized mean PDP (blue curve) obtained from the experiment results in campus [6]. Similar plots for the ship environment are shown in parameter from campus measurements [6]. and 7(f) indicate peaks whose normalized signal strength are larger than −20 dB, these peaks are considered significant and indicate the existence of multipath components.…”

Section: Statistically Modeling Of the Signal Variationsmentioning

confidence: 79%

“…In this paper, the size of the data set, N , is equal to 5000. The AIC based method is applied to statistically model the signal effect along the lift shaft for both the campus environment [6] and the ship environment.…”

Section: Statistically Modeling Of the Signal Variationsmentioning

confidence: 99%

“…Application focused research work has studied the effect of the moving elevator on the number of handoffs required for effective cell planning purposes [3]. Wideband channel characteristics were analyzed in order to identify different propagation mechanisms and to study the signal variations in [4][5][6]. In [4], the propagation mechanisms associated with the lift shaft in a campus have been studied through wideband channel measurements and simulations.…”

Section: Introductionmentioning

confidence: 99%

“…It was shown that the guiding effect of the lift shaft in the military UHF band of 225 to 400 MHz [5] is important for urban warfare. The statistical modelling of the signal variation along the lift shaft is reported in [6]. The Weibull distribution is found to be the best function for describing the signal variation associated with the signals guided by the lift shaft.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Propagation Along a Lift Shaft in Two Complex Environments

Lee

2012

PIER B

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Abstract-Signal variation caused by motions along the lift shaft in a campus environment and on board a ship is compared. The guiding effect is common for both lift shafts, and the variation in amplitude of the guided signals is more significant for the lift shaft with larger dimensions. Unlike the lift shaft within the campus, the ship with its lift shaft forms a 'waveguide within waveguide' structure. Therefore, the reflected signals within the ship enclosure outside the lift shaft are significantly affected by the motion along the lift shaft. Due to the difference in the degree of the signal variations in these two environments, the rms delay spread is found to be closely related to the lift door status and the lift car position in the campus environment, whereas it is not significantly affected by the motions along the lift shaft in the ship environment. From the statistical study and comparison of the signal variations in the two environments, the Weibull probability density function is found to be the most suitable model to describe analogous waveguide channels such as the lift shaft and the ship enclosure.

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Section: Statistically Modeling Of the Signal Variationsmentioning

confidence: 71%

Section: Statistically Modeling Of the Signal Variationsmentioning

confidence: 79%

Section: Statistically Modeling Of the Signal Variationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Propagation Along a Lift Shaft in Two Complex Environments

Lee

2012

PIER B

Self Cite

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Measurements and analysis of vehicular radio channels in the inland lake bridge area

Chen

et al. 2019

IET Microwaves, Antennas & Propagation

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Vehicular communication plays a vital role in intelligent transportation systems and smart cities. The huge data transmission and high mobility require a high‐speed and stable wireless channel as a basis. This study presents a 5.9 GHz vehicle‐to‐vehicle (V2V) measurement, covering a complete inland lake bridge communication environment. Analysis of the channel characteristics is carried out according to three main propagation scenarios: ramp merging, a rear vehicle overtaking the front vehicle, and an urban scenario. Channel characteristics including the power delay profile, Doppler spectral density, amplitude distribution, and fading depth (FD) is extracted and analysed. An FD of ∼2.5 dB is determined in V2V communication. Furthermore, it is observed that, in addition to the impact from the ramp structure, metallic traffic signs, road lights, pedestrian bridges, and large trucks can also make significant contributions to vehicular communication. Moreover, a triple Gaussian mixture distribution is proposed to model the channel characterisation. The results demonstrate that the specific structure of the ramp will influence the channel performance more significantly than the general traffic road structure, which can provide a meaningful reference for the future research of V2V radio channels and is useful for the design and layout of vehicular communication systems.

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Measurement‐based wireless channel analysis and modelling for shipping environments

Chen

et al. 2020

IET Microwaves, Antennas & Propagation

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To guarantee shipping safety, it is crucial to build a reliable wireless communication network. This study presents characteristic analysis of shipping wireless channel based on measurements. The authors conduct five channel measurements for two important water mobile communications, covering inland river and sea scenarios. Channel characteristics, e.g. power delay profile, amplitude distribution, delay spread and shadow fading, are extracted and compared for the two scenarios. It is found that the main difference between the them lies in more obstacles and scatterers in inland river propagation environment. The results show that more obstacles and scatterers can result in richer multipath effect and larger shadow fading decorrelation distance. A triple Gaussian mixture distribution (TGMD) is proposed to characterize the wireless channel with LOS, obstructed LOS and non‐LOS situations in the practical propagation. To verify the effectiveness of TGMD, the goodness of fit between measurement and analysis results in terms of Rician K‐factor, root mean square (RMS) delay spread and RMS Doppler spread is calculated. Furthermore, the cross‐correlation among shadow fading, Rician K‐factor and RMS delay spread are studied. Larger cross‐correlation can be found in inland river and sea than vehicular communication due to the open propagation environment.

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Statistical Modeling of Signal Variation for Propagation Along a Lift Shaft

Cited by 15 publications

References 15 publications

Comparison of Propagation Along a Lift Shaft in Two Complex Environments

Comparison of Propagation Along a Lift Shaft in Two Complex Environments

Measurements and analysis of vehicular radio channels in the inland lake bridge area

Measurement‐based wireless channel analysis and modelling for shipping environments

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