Radio propagation measurements and modeling for standardization of the site general path loss model in International Telecommunications Union recommendations for 5G wireless networks

Salous, Sana; Lee, J; Kim, M-D; Sasaki, Motoharu; Yamada, Wataru; Raimundo, Xavier; Cheema, Adnan Ahmad

doi:10.1029/2019rs006924

Cited by 19 publications

(9 citation statements)

References 16 publications

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“…The dataset is considered as presented in [ 36 ]. It has been measured in three countries (UK, South Korea, and Japan) from 0.8

to 73

in different environments including urban-low rise, urban high-rise, and suburban environments.…”

Section: Use Case and Datasetmentioning

confidence: 99%

A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

Nguyen

Cheema

2021

Sensors

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Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.

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“…The dataset is considered as presented in [ 36 ]. It has been measured in three countries (UK, South Korea, and Japan) from 0.8

to 73

in different environments including urban-low rise, urban high-rise, and suburban environments.…”

Section: Use Case and Datasetmentioning

confidence: 99%

A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

Nguyen

Cheema

2021

Sensors

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“…Popoola et al presented extensive driving test measurement data of radio wave propagation in a campus environment and analyzed the correlation between the path loss and terrain information [2]. For 5G networks, standardization organizations, such as the International Telecommunications Union Radiocommunication Sector, have conducted channel measurements in urban and suburban environments for path loss modeling [3]. Shabbir et al presented the comparison of 5G path loss models based on simulation results and evaluated the suitability of the models in different scenarios [4].…”

Section: Introductionmentioning

confidence: 99%

Explainable Deep-Learning-Based Path Loss Prediction from Path Profiles in Urban Environments

Juang

2021

Applied Sciences

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This paper applies a deep learning approach to model the mechanism of path loss based on the path profile in urban propagation environments for 5G cellular communication systems. The proposed method combines the log-distance path loss model for line-of-sight propagation scenarios and a deep-learning-based model for non-line-of-sight cases. Simulation results show that the proposed path loss model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by 34% when compared to the conventional models. To explain the internal behavior of the proposed deep-learning-based model, which is a black box in nature, eight relevant features were selected to model the path loss based on a linear regression approach. Simulation results show that the accuracy of the explanatory model reached 72% when it was used to explain the proposed deep learning model. Furthermore, the proposed deep learning model was also evaluated in a non-standalone 5G New Radio network in the urban environment of Taipei City. The real-world measurements show that the standard deviation of prediction error can be reduced by 30–43% when compared to the conventional models. In addition, the transparency of the proposed deep learning model reached 63% in the realistic 5G network.

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“…Over the past few years, there is an exponential rise in wireless applications e.g., augmented reality, IoT, 5G, healthcare, agriculture, etc. due to which there is a rise in the optimization of coverage of BTS [1][2][3]. To locate the correct geographical location of a Base Station Transmitter in an outdoor wireless network, path loss prediction is essential for the interest of researchers.…”

Section: Introductionmentioning

confidence: 99%

Mobility Improvement by Optimizing Channel Model Coverage Through Fine Tuning

Gupta

Ghanshala

Joshi

2021

JCSANDM

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Empirical channel models were always an important tool for proper wireless network planning. These models consider the properties of electromagnetic waves and terrain conditions. The efficiency and accuracy of these Empirical models suffer when they are used for an area other than where they have been designed. So tuning of these models is required for proper and accurate prediction of coverage and it is done by taking the correction factor into account. Comparison of four Empirical models i.e, the Lee, the ECC-33 model, the WI model, the Ericsson model, the COST 231, and SUI is done with Measured path loss, and the best model with minimum error is then selected for tuning. Field data of LTE network at 2300 MHz is collected at two sites of Uttarakhand-India. It is analysed that the Ericsson model shows minimum RMSE, Standard Deviation, and Mean error as compared to measured path loss, followed by the Okumura model. The Ericsson model is then tuned to further reduce the error. Validation of the tuned model is done at Haridwar.

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Radio propagation measurements and modeling for standardization of the site general path loss model in International Telecommunications Union recommendations for 5G wireless networks

Cited by 19 publications

References 16 publications

A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

Explainable Deep-Learning-Based Path Loss Prediction from Path Profiles in Urban Environments

Mobility Improvement by Optimizing Channel Model Coverage Through Fine Tuning

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