ANN Prediction Models for Outdoor Environment

Popescu, I.; Nikitopoulos, D.; Constantinou, P.; Nafornita, Ioan

doi:10.1109/pimrc.2006.254270

Cited by 37 publications

(20 citation statements)

References 7 publications

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“…Therefore, artificial neural networks (ANNs) have been proposed in order to obtain prediction models that are more accurate than standard empirical models whilst being more computationally efficient than deterministic models. In recent years, ANNs have been shown to successfully perform path loss predictions in rural [6], suburban [7], urban [8] and indoor [9] environments. An ANN prediction model can be trained to perform well in environments similar to where the training data is collected.…”

Section: Introductionmentioning

confidence: 99%

Macrocell Path-Loss Prediction Using Artificial Neural Networks

Ostlin

Zepernick

Suzuki

2010

IEEE Trans. Veh. Technol.

183

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Section: Introductionmentioning

confidence: 99%

Macrocell Path-Loss Prediction Using Artificial Neural Networks

Ostlin

Zepernick

Suzuki

2010

IEEE Trans. Veh. Technol.

183

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“…The propagation model used throughout this paper is a modified version of the log normal model that is widely used in the literature [27][28][29][30]. The PL exponent (PLE) is the key parameter in the log normal model.…”

Section: Rss-based Location Estimation Modelmentioning

confidence: 99%

“…f n e n d n (30) For the case of three base stations with unequal path loss exponents, the FIM matrices are of size 5 × 5 and the unknown vector parameter is given by θ = [ x, y, α 1 , α 2 …”

Section: Accuracy Measuresmentioning

confidence: 99%

Location estimation using RSS measurements with unknown path loss exponents

Zeytinci

Sari

Harmanci

et al. 2013

J Wireless Com Network

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The location of a mobile station (MS) in a cellular network can be estimated using received signal strength (RSS) measurements that are available from control channels of nearby base stations. Most of the recent RSS-based location estimation methods that are available in the literature rely on the rather unrealistic assumption that signal propagation characteristics are known and independent of time variations and the environment. In this paper, we propose an RSS-based location estimation technique, so-called multiple path loss exponent algorithm (RSS-MPLE), which jointly estimates the propagation parameters and the MS position. The RSS-MPLE method incorporates antenna radiation pattern information into the signal model and determines the maximum likelihood estimate of unknown parameters by employing the Levenberg-Marquardt method. The accuracy of the proposed method is further examined by deriving the Cramer-Rao bound. The performance of the RSS-MPLE algorithm is evaluated for various scenarios via simulation results which confirm that the proposed scheme provides a practical position estimator that is not only accurate but also robust against the variations in the signal propagation characteristics.

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“…Different algorithms have been adopted to train prediction models in traditional terrestrial communication scenarios. For example, artificial neural networks (ANNs) were used for path loss prediction in urban [14], suburban [15], rural [16], and railway [17] scenarios. Support vector regression (SVR) was applied for the prediction of path loss in suburban environment in [18].…”

Section: Introductionmentioning

confidence: 99%

Air-to-Air Path Loss Prediction Based on Machine Learning Methods in Urban Environments

Zhang

Wen

Yang

et al. 2018

Wireless Communications and Mobile Computing

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Recently, unmanned aerial vehicle (UAV) plays an important role in many applications because of its high flexibility and low cost. To realize reliable UAV communications, a fundamental work is to investigate the propagation characteristics of the channels. In this paper, we propose path loss models for the UAV air-to-air (AA) scenario based on machine learning. A ray-tracing software is employed to generate samples for multiple routes in a typical urban environment, and different altitudes of Tx and Rx UAVs are taken into consideration. Two machine-learning algorithms, Random Forest and KNN, are exploited to build prediction models on the basis of the training data. The prediction performance of trained models is assessed on the test set according to the metrics including the mean absolute error (MAE) and root mean square error (RMSE). Meanwhile, two empirical models are presented for comparison. It is shown that the machine-learning-based models are able to provide high prediction accuracy and acceptable computational efficiency in the AA scenario. Moreover, Random Forest outperforms other models and has the smallest prediction errors. Further investigation is made to evaluate the impacts of five different parameters on the path loss. It is demonstrated that the path visibility is crucial for the path loss.

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ANN Prediction Models for Outdoor Environment

Cited by 37 publications

References 7 publications

Macrocell Path-Loss Prediction Using Artificial Neural Networks

Macrocell Path-Loss Prediction Using Artificial Neural Networks

Location estimation using RSS measurements with unknown path loss exponents

Air-to-Air Path Loss Prediction Based on Machine Learning Methods in Urban Environments

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