V2V Radio Channel Performance Based on Measurements in Ramp Scenarios at 5.9 GHz

Li, Changzhen; Yang, Kun; Yu, Jiguo; Fang, Li; Shui, Yishui; Chang, Fuxing; Chen, Wei

doi:10.1109/access.2017.2788399

Cited by 29 publications

(14 citation statements)

References 38 publications

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“…While β is a shape parameter that determines the steepness of the distribution curve. In our fitting, the values of β for the LOS and NLOS case are (1.41, 1.74, 1.83, 1.89) and (1.12, 1.38, 1.42, 1.65) at (1,4,12,24) GHz, respectively. The results show that the values increase with the frequency, which indicates that the AG channel in the high frequency is more vulnerable to experience severe fading.…”

Section: B Best Fit Distribution Of Small-scale Fadingmentioning

confidence: 99%

“…The second-order statistics of the propagation channel are provided in terms of two major parameters: LCR and AFD, which can be obtained from the received signal strength variations at a single frequency as a function of time or distance [24], [25]. In this paper, due to vertical flights of UAV, the LCR is modeled as a function of the vertical distance.…”

Section: Second-order Statistical Characteristicsmentioning

confidence: 99%

“…Moreover, the speed of the UAV is almost constant at 1 m/s, the AFD can also be modeled as a function of the vertical distance because of t = h U /v where v is the velocity, and t is time. According to the definitions, the LCR is the number of the times at which the specified level is crossed in the positive-going direction (down to up), whereas the AFD is the duration (or distance) that the signal remains below the level [24]. For example, for a given threshold of -0.5 dB, Fig.…”

Section: Second-order Statistical Characteristicsmentioning

confidence: 99%

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Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems

et al. 2020

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In order to implement unmanned aerial vehicle (UAV)-based wireless communications, a better understanding of wireless channels and the corresponding channel characterizations are critical. In this paper, air-to-ground (AG) channel measurements are carried out at some candidate frequencies, i.e., 1 GHz, 4 GHz, 12 GHz, and 24 GHz. With measurement data, the crucial channel parameters are comprehensively analyzed. Firstly, based on the channel model in the 3rd Generation Partnership Project (3GPP), the essential coefficients for modeling path loss, including path loss exponents (PLEs) and heightdependent factors, are obtained for AG channels. Then, a novel autocorrelation model for shadow fading is proposed. Besides, the small-scale fading is statistically analyzed, where the log-logistic distribution is found as the best fit among popular distributions. Moreover, the second-order statistical characteristics, including the level crossing rate (LCR) and average fade duration (AFD), are extracted to describe indepth the fading behavior. Overall, the results and findings in this paper are essential for realizing reliable communications in AG wireless systems. INDEX TERMS Air-to-ground (AG) channel, characterizations, fading behavior, measurements, UAV.

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Section: B Best Fit Distribution Of Small-scale Fadingmentioning

confidence: 99%

Section: Second-order Statistical Characteristicsmentioning

confidence: 99%

Section: Second-order Statistical Characteristicsmentioning

confidence: 99%

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Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems

et al. 2020

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“…In this paper, we adopt Akaike's Information Criteria (AIC) [26][27][28] to select the best fit distribution from the above four candidates. According to the AIC described in previous works [29,30], AIC weights for the four candidate distributions are calculated and presented in Fig. 3.…”

Section: Amplitude Distributionmentioning

confidence: 99%

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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“…As mentioned previously, four main scenarios are generally studied for vehicular communications: urban [6,21,22], suburban [23], rural [24], and highway [24,25]. Furthermore, several special communication scenarios have been investigated in previous works such as ramp [26], overpass [27], and tunnel [28]. In this paper, we consider a specific urban traffic case, namely bridges over inland rivers and lakes.…”

Section: Literature Reviewmentioning

confidence: 99%

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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V2V Radio Channel Performance Based on Measurements in Ramp Scenarios at 5.9 GHz

Cited by 29 publications

References 38 publications

Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems

Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems

Measurement‐based wireless channel analysis and modelling for shipping environments

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

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