Statistical Modeling and Estimation of Censored Pathloss Data

Gustafson, Carl; Abbas, Taimoor; Bolin, David; Tufvesson, Fredrik

doi:10.1109/lwc.2015.2463274

Cited by 31 publications

(35 citation statements)

References 11 publications

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“…For the OLOS case, the following parameters in (1) are estimated using ordinary least squares: the pathloss at a reference distance of 10 m, P L(d 0 ), the pathloss exponent, α, and the large scale fading standard deviation, σ. Here, we note that the uncertainty (more specifically, the standard error) of the estimated parameters depend on the number of samples, the measurement sample distances, the amount of censored samples as well as the pathloss model and its parameters [40]. Furthermore, the effective number of samples are likely reduced due to correlated large scale fading samples that are measured closely in time.…”

Section: A Pathloss Model Estimationmentioning

confidence: 98%

“…The ordinary least square (OLS) estimation method does not consider this issue, i.e., when only successfully decoded packets are used in the estimation. As mentioned in [34]- [36] it is important to use the information regarding distances between TX and RX also for the lost packets when estimating pathloss parameters for LOS and OLOS channel models. In this paper we are using the ML estimation method described in [36].…”

Section: Data Processingmentioning

confidence: 99%

“…As mentioned in [34]- [36] it is important to use the information regarding distances between TX and RX also for the lost packets when estimating pathloss parameters for LOS and OLOS channel models. In this paper we are using the ML estimation method described in [36]. The censoring due to lost packets does not occur at a fixed level.…”

Section: Data Processingmentioning

confidence: 99%

See 2 more Smart Citations

A Measurement-Based Multilink Shadowing Model for V2V Network Simulations of Highway Scenarios

Nilsson

Gustafson

Abbas

et al. 2017

IEEE Trans. Veh. Technol.

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Abstract-Shadowing from vehicles can significantly degrade the performance of vehicle-to-vehicle (V2V) communication in multilink systems, e.g., vehicular ad-hoc networks (VANETs). It is thus important to characterize and model the influence of common shadowing objects like cars properly when designing these VANETs. Despite the fact that for multilink systems it is essential to model the joint effects on the different links, the multilink shadowing effects of V2V channels on VANET simulations are not yet well understood. In this paper we present a measurement based analysis of multilink shadowing effects in a V2V communication system with cars as blocking objects. In particular we analyze, characterize and model the large scale fading, both regarding the autocorrelation and the joint multilink cross-correlation process, for communication at 5.9 GHz between four cars in a highway convoy scenario. The results show that it is essential to separate the instantaneous propagation condition into line-of-sight (LOS) and obstructed LOS (OLOS), by other cars, and then apply an appropriate pathloss model for each of the two cases. The choice of the pathloss model not only influences the autocorrelation but also changes the cross-correlation of the large scale fading process between different links. By this, we conclude that it is important that VANET simulators should use geometry based models, that distinguish between LOS and OLOS communication. Otherwise, the VANET simulators need to consider the cross-correlation between different communication links to achieve results close to reality.

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Section: A Pathloss Model Estimationmentioning

confidence: 98%

Section: Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

A Measurement-Based Multilink Shadowing Model for V2V Network Simulations of Highway Scenarios

Nilsson

Gustafson

Abbas

et al. 2017

IEEE Trans. Veh. Technol.

Self Cite

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“…During online classification, the MAP is used to estimate the user's location. First, the posterior is calculated as follows: (10) In Eq. (10), K and N AP represent the total number of RPs and APs, respectively.…”

Section: The Online Classification and Positioning Phasementioning

confidence: 99%

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Vu¹,

Le²

2019

VJSTE

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In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc. By accounting for these problems, this paper proposes to model the RSSI probability density distributions using the Censoring Gaussian Mixture Model (C-GMM) and develop the Expectation-Maximization (EM) algorithm to estimate the parameters of this model in the offline phase of the Wi-Fi fingerprinting based Indoor Positioning Systems (IPS). The simulation results demonstrate the effectiveness of the proposed method.

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“…Mostly, network planners find it easier to use pathloss models to estimate the pathloss that can be experienced by the signal in any given area [6][7][8][9][10]. In this wise, empirical pathloss models are usually the first option in view of their simplicity and quite acceptable pathloss prediction capability [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model

Chimaobi¹,

Nnadi²,

Nzegwu³

2017

AJSEA

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Abstract:In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.

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Statistical Modeling and Estimation of Censored Pathloss Data

Cited by 31 publications

References 11 publications

A Measurement-Based Multilink Shadowing Model for V2V Network Simulations of Highway Scenarios

A Measurement-Based Multilink Shadowing Model for V2V Network Simulations of Highway Scenarios

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model

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