A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

Ayadi, Mohamed; Zineb, Aymen Ben; Tabbane, Sami

doi:10.1109/tap.2017.2705112

Cited by 125 publications

(107 citation statements)

References 14 publications

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“…We employ a typical three-layer BPNN structure for singlevalued regression prediction is shown in Fig. 3 [11]. The neurons in the input layer are used to receive the input features and the neuron in the output layer is to produce the predicted value.…”

Section: A Prediction Model Based On Bpnnmentioning

confidence: 99%

“…Nevertheless, deterministic models require detailed environment information and usually rely on a three-dimensional (3D) map, which leads to high computational complexity. Besides, these models are site-specific, i.e., repeated calculations are needed once the environment changes [11].…”

Section: Introductionmentioning

confidence: 99%

“…Path loss prediction can be viewed as a supervised regression problem which can be handled by machine learning methods. In recent years, the existing research results have proved that the path loss models based on machine learning can provide more accurate path loss prediction results than the empirical models, and are even more computationally efficient than deterministic ones [11]- [15]. In indoor environments, the ANN-based models proposed in [16] showed good prediction performance.…”

Section: Introductionmentioning

confidence: 99%

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Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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Wireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning methods, including back propagation neural network (BPNN), support vector regression (SVR), random forest, and AdaBoost, are used to build path loss prediction models for an MD-82 aircraft cabin. Firstly, machine-learning-based models are designed to predict the path loss values at different locations at a fixed frequency. It is shown that these models fit the measured data well, e.g., at 2.4 GHz central frequency the root mean square errors (RMSEs) of BPNN, SVR, random forest, and AdaBoost predictors are 1.90 dB, 2.20 dB, 1.76 dB, and 2.12 dB. Subsequent research is engaged to forecast path loss at a new frequency based on available information at known frequencies. Additionally, to solve the data limitation problem at the new frequency, we propose a path loss prediction scheme combining empirical models and machinelearning-based models. This scheme uses estimated values generated by the empirical model according to prior information to expand the training set. To verify the performance of this scheme, measured samples at 2.4 GHz and 3.52 GHz, as well as samples generated by the empirical model are employed as the training set for the path loss prediction at 5.8 GHz. The RMSEs of BPNN, SVR, random forest, and AdaBoost models are 2.49 dB, 2.78 dB, 2.54 dB, and 3.76 dB. In contrast, without samples generated by the empirical model, the RMSEs of those models are 3.84 dB, 4.94 dB, 6.57 dB, and 6.77 dB. Results show that the proposed data expansion scheme improves prediction performance when there are few measurement samples at the new frequency. INDEX TERMS Aircraft cabin, data expansion, machine learning, path loss prediction, propagation characteristics.

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Section: A Prediction Model Based On Bpnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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“…However, the actual path loss at a specific location cannot be obtained. Besides, the accuracy of these models decreases when they are applied to more general environments [12].…”

Section: Introductionmentioning

confidence: 99%

“…It has been proved that machine-learning-based models are able to provide more accurate path loss prediction results than the empirical ones and are more computationally efficient than the deterministic approaches [12]. Different algorithms have been adopted to train prediction models in traditional terrestrial communication scenarios.…”

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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Support Vector Machine for Path Loss Predictions in Urban Environment

Abolade

Famakinde

Popoola

et al. 2020

Lecture Notes in Computer Science

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Path Loss (PL) propagation models are important for accurate radio network design and planning. In this paper, we propose a new radio propagation model for PL predictions in urban environment using Support Vector Machine (SVM). Field measurement campaigns are conducted in urban environment to obtain mobile network and path loss information of radio signals transmitted at 900, 1800 and 2100 MHz frequencies. SVM model is trained with field measurement data to predict path loss in urban propagation environment. Performance of SVM model is evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Standard Error Deviation (SED). Results show that SVM achieve MAE, MSE, RMSE and SED of 7.953 dB, 99.966 dB, 9.998 dB and 9.940 dB respectively. SVM model outperforms existing empirical models (Okumura-Hata, COST 231, ECC-33 and Egli) with relatively low prediction error.

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A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

Cited by 125 publications

References 14 publications

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

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

Support Vector Machine for Path Loss Predictions in Urban Environment

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