Root Mean Square Doppler Spread Research Articles

Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

Understanding multi-node vehicular wireless communication channels is crucial for future time-sensitive safety applications for human-piloted as well as partly autonomous vehicles on roads, railways and in the air. These highly dynamic wireless communication channels are characterized by rapidly changing channel statistics. In this paper we present the first fully mobile multi-node vehicular wireless channel sounding system, which is capable of simultaneously capturing multiple channel frequency responses, ensuring that measurement conditions are identical for all observed links. We use it to analyze road scenarios with multiple vehicles and a large obstructing double-decker bus. The empirical measurement data is used to parametrize a model for the large obstructing vehicle within a geometry-based stochastic channel model. We compare the time-variant channel statistics obtained from our channel model with the ones from the measurement campaign. By means of a channel emulator we obtain the packet error rates of commercial modems for the measured and the simulated wireless communication channels and compare them, in order to validate the model at the link level. We find that the path loss, the root mean square (RMS) delay spread, and the RMS Doppler spread deviate by less than 3.6 dB, 78 ns, and 52 Hz, respectively, for 80% of the total simulation duration. The PER obtained from measured data is within the maximum and minimum bounds of our model for 86% of the simulation duration.

Measurement‐based wireless channel analysis and modelling for shipping environments

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.

Channel Measurement and Modeling of the Small-Scale Fading Characteristics for Urban Inland River Environment

In this study, radio channel measurements were conducted in an urban inland river environment at 5.9GHz. The measurements consisted of both the line of sight (LOS) and non-line of sight (NLOS) cases. We estimate and model channel characteristics based on the measured data. For small-scale fading properties, best fit small-scale fading distribution is determined. Statistical models of the Ricean K-factors and Weibull shape parameters are provided. In addition, a distance-dependent Ricean K-factor model for the urban inland river environment is proposed. For the power delay profile (PDP), we perform cluster identification and obtain the cumulative distribution function (CDF) of the inter-cluster interval. A statistical model of the intracluster decay time constant is also presented, and an exponential decay model is applied for modeling the inter-cluster decay of the PDP. For delay spread and Doppler spread, the root mean square (RMS) delay spreads in the LOS case are compared with those in the NLOS case. Moreover, we estimate and analyze RMS Doppler spreads in both the LOS and NLOS cases. Finally, a bimodal Gaussian mixture distribution (BGMD) is employed to characterize the RMS delay spreads and RMS Doppler spreads. The BGMDs exhibit good matching levels.

Measurement‐based V2V radio channel analysis and modelling for bridge scenarios at 5.9 GHz

In this study, vehicle-to-vehicle (V2V) radio channel measurements at 5.9 GHz were conducted on a suspension bridge and a beam bridge. Given that a different structure of the two bridges will result in different channel properties, the authors study small-scale and large-scale propagation characteristics for the bridge scenarios based on measurement data. By using Akaike's information criteria, the study determines optimal small-scale fading distribution. Owing to that Ricean distribution occupies the dominant position, Ricean K-factors in the two bridge scenarios are modelled separately and compared with each other. Afterwards, power delay profiles, Doppler power spectral densities, root-mean-square (RMS) delay spread and RMS Doppler spread are estimated and analysed in different cases. They employ bimodal Gaussian mixture distribution (BGMD) to model the RMS delay spreads and the RMS Doppler spreads. Fitting results of the BGMDs show good matching levels. For the large-scale propagation characteristics, path loss model for the beam bridge scenario is proposed based on the two-ray theory. On the other hand, path loss for the suspension bridge scenario is modelled by using an empirical function derived from WINNER II. Finally, grey relational grade–mean absolute percentage error, and RMS error criteria prove the effectiveness of the two proposed path loss models.

Vehicle-to-Vehicle Propagation Channel Performance for Overtaking Cases Based on Measurements

Overtaking case is a common traffic situation, but it is likely to pose a threat to traffic safety. To ensure reliable communication, it is necessary to study the characteristics of wireless channel under overtaking cases. In this paper, we focus on Vehicle-to-Vehicle (V2V) radio channel characteristics based on measurements at 5.9GHz. Through research and analysis, we can present empirical results of propagation channels in four scenarios. Our main concern is the difference in channel characteristics between overtaking and non-overtaking case. We propose a method to divide overtaking cases and non-overtaking cases points based on small-scale fading distribution, and results show that the dividing point is about 11 meters between the measured vehicles. Judging by Akaike Information Criterion(AIC), Weibull distribution is best under overtaking cases, while it follows Ricean distribution under non-overtaking. Then, we prove that the result of the AIC decision is valid by the Kolmogorov-Smirnov test. Therefore, we further compare Weibull $\beta $ -factor and Ricean K-factor under two cases. The values of root-mean-square (RMS) delay spreads and average fade duration under overtaking cases are higher than those under non-overtaking, but RMS Doppler spreads and the level crossing rate are smaller than those under non-overtaking. The cross-correlation between RMS delay spread and Ricean K-factor is also calculated. The correlation value in OT case is higher than that in NOT case. The value of the correlation in the case of overtaking is higher than the case of non-overtaking. By comparing the above methods, it is proved that the dividing point between overtaking cases and non-overtaking cases is effective.

Delay and Doppler Spreads of Nonstationary Vehicular Channels for Safety-Relevant Scenarios

Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to rapid changes in the environment. The non-stationary fading process can be characterized by assuming local stationarity for a region with finite extent in time and frequency. For this finite region the wide-sense stationarity and uncorrelated-scattering (WSSUS) assumption holds approximately and we are able to calculate a time and frequency dependent local scattering function (LSF). In this paper, we estimate the LSF from a large set of measurements collected in the DRIVEWAY'09 measurement campaign, which focuses on scenarios for intelligent transportation systems. We then obtain the time-frequency-varying power delay profile (PDP) and the time-frequency-varying Doppler power spectral density (DSD) from the LSF. Based on the PDP and the DSD, we analyze the time-frequency-varying root mean square (RMS) delay spread and the RMS Doppler spread. We show that the distribution of these channel parameters follows a bi-modal Gaussian mixture distribution. High RMS delay spread values are observed in situations with rich scattering, while high RMS Doppler spreads are obtained in drive-by scenarios.

Sample size required in error-rate measurement on fading channels

This paper presents analytical and numerical results on the sample size required to achieve a specified root mean square (RMS) error in estimating the error rate for flat fading channels having complex Gaussian statistics. The analysis shows that for the large sample sizes normally used in estimating error rates, k, the required sample size normalized to the required sample size for independent symbol fading, can be expressed in the form k=1+d/spl beta/ where d is the symbol rate normalized to the Doppler spread of the channel. For a given modem, /spl beta/ is a function of the error probability and the order of diversity. It is shown that if the Doppler spread measure used is proportional to the RMS Doppler spread, P will be relatively insensitive to the shape of the Doppler power spectrum. Numerical results are presented for Lth order diversity reception of binary phase shift keying (PSK), differential PSK, and frequency shift keying (FSK) signals and for five different Doppler power spectra. Ideal maximal ratio combining is assumed for the PSK modem, and square law combining is assumed for the DPSK and FSK modems.