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

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5100
Author(s):  
Chi Nguyen ◽  
Adnan Ahmad Cheema
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Path Loss ◽  
Loss Model ◽  
Path Loss Model ◽  
Proposed Model ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Loss Models

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.

scholarly journals Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

2016 ◽  
Vol 1 (2) ◽  
pp. 77-84
Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Artificial Neural Network Model ◽  
Path Loss ◽  
Signal Propagation ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Edo State ◽  
Artificial Neural

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.

Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

2016 ◽  
Vol 1 (2) ◽  
pp. 77-84
Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Artificial Neural Network Model ◽  
Path Loss ◽  
Signal Propagation ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Edo State ◽  
Artificial Neural

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.

scholarly journals Investigating Automated Hyper-Parameter Optimization for a Generalized Path Loss Model

2021 ◽  
Author(s):  
Usman Sammani Sani ◽  
Daphne Teck Ching Lai ◽  
Owais Ahmed Malik
Keyword(s):  
Machine Learning ◽  
Parameter Optimization ◽  
Path Loss ◽  
Machine Learning Algorithms ◽  
Bayesian Optimization ◽  
Loss Model ◽  
Path Loss Model ◽  
International Telecommunication ◽  
Loss Prediction ◽  
Path Loss Prediction

This work aims at developing a generalized and optimized path loss model that considers rural, suburban, urban, and urban high rise environments over different frequencies, for use in the Heterogenous Ultra Dense Networks in 5G. Five different machine learning algorithms were tested on four combined datasets, with a sum of 12369 samples in which their hyper-parameters were automatically optimized using Bayesian optimization, HyperBand and Asynchronous Successive Halving (ASHA). For the Bayesian optimization, three surrogate models (the Gaussian Process, Tree Structured Parzen Estimator and Random Forest) were considered. To the best of our knowledge, few works have been found on automatic hyper-parameter optimization for path loss prediction and none of the works used the aforementioned optimization techniques. Differentiation among the various environments was achieved by the assignment of the clutter height values based on International Telecommunication Union Recommendation (ITU-R) P.452-16. We also included the elevation of the transmitting antenna position as a feature so as to capture its effect on path loss. The best machine learning model observed is K Nearest Neighbor (KNN), achieving mean Coefficient of Determination (R2), average Mean Absolute Error (MAE) and mean Root Mean Squared Error (RMSE) values of 0.7713, 4.8860dB, and 6.8944dB, respectively, obtained from 100 different samplings of train set and test set. Results show that machine learning can also be used to develop path loss models that are valid for a certain range of distances, frequencies, antenna heights, and environment types. HyperBand produced hyper-parameter configurations with the highest accuracy in most of the algorithms.

A new path loss model based on the volumetric occupancy rate for the pine forests at 5G frequency band

2020 ◽  
pp. 1-10
Author(s):  
Abdullah Genc
Keyword(s):  
Path Loss ◽  
Forest Area ◽  
Occupancy Rate ◽  
Loss Model ◽  
Path Loss Model ◽  
Model Based ◽  
Proposed Model ◽  
Pine Trees ◽  
Foliage Density ◽  
Reference Measurements

Abstract In this paper, a new empirical path loss model based on frequency, distance, and volumetric occupancy rate is generated at the 3.5 and 4.2 GHz in the scope of 5G frequency bands. This study aims to determine the effect of the volumetric occupancy rate on path loss depending on the foliage density of the trees in the pine forest area. Using 4.2 GHz and the effect of the volumetric occupancy rate contributes to the literature in terms of novelty. Both the reference measurements to generate a model and verification measurements to verify the proposed models are conducted in three different regions of the forest area with double ridged horn antennas. These regions of the artificial forest area consist of regularly sorted and identical pine trees. Root mean square error (RMSE) and R-squared values are calculated to evaluate the performance of the proposed model. For 3.5 and 4.2 GHz, while the RMSEs are 3.983 and 3.883, the values of R-squared are 0.967 and 0.963, respectively. Additionally, the results are compared with four path loss models which are commonly used in the forest area. The proposed one has the best performance among the other models with values 3.98 and 3.88 dB for 3.5 and 4.2 GHz.

scholarly journals Two-step path loss prediction by artificial neural network for wireless service area planning

2019 ◽  
Vol 8 (12) ◽  
pp. 611-616 ◽  
Author(s):  
Kentaro Saito ◽  
Yongri Jin ◽  
CheChia Kang ◽  
Jun-ichi Takada ◽  
Jenq-Shiou Leu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Path Loss ◽  
Service Area ◽  
Wireless Service ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Artificial Neural ◽  
Area Planning

Path Loss through Pine Forest around Beach for Wireless Sea Wave Energy Sensor Network

2012 ◽  
Vol 433-440 ◽  
pp. 3954-3958 ◽  
Author(s):  
Supachai Phaiboon ◽  
Supanuch Seesaiprai
Keyword(s):  
Sensor Network ◽  
Wave Energy ◽  
Path Loss ◽  
Low Noise ◽  
Loss Model ◽  
Path Loss Model ◽  
Proposed Model ◽  
Energy Sensor ◽  
Sea Wave ◽  
Good Agreement

This paper presents an empirical path loss model through forest for measuring sea wave energy using 2.4 GHz wireless sensor network (WSN). The empirical path loss model was determined from measurement campaign by using 18 dBm transmitter and the receivers with a low noise amplify. The conventional path loss models for forest environments were carried out such as Weissberger, ITU-R, COST 235 and Torrico models. From the results it is found that the proposed model provides a good agreement and is used for planning WSN.

scholarly journals Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 672 ◽  
Author(s):  
Ahmed Al-Samman ◽  
Tharek Rahman ◽  
MHD Hindia ◽  
Abdusalama Daho ◽  
Effariza Hanafi
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Millimeter Wave ◽  
Path Loss ◽  
Loss Model ◽  
Frequency Bands ◽  
Path Loss Model ◽  
Parking Lot ◽  
Loss Models ◽  
5G Systems

It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times less and access delay of less than 1 ns that will be provided by future 5G systems. To increase the current mobile broadband capacity in future 5G systems, the millimeter wave (mmWave) band will be used with huge amounts of bandwidth available in this band. Hence, to support this wider bandwith at the mmWave band, new radio access technology (RAT) should be provided for 5G systems. The new RAT with symmetry design for downlink and uplink should support different scenarios such as device to device (D2D) and multi-hop communications. This paper presents the path loss models in parking lot environment which represents the multi-end users for future 5G applications. To completely assess the typical performance of 5G wireless network systems across these different frequency bands, it is necessary to develop path loss (PL) models across these wide frequency ranges. The short wavelength of the highest frequency bands provides many scatterings from different objects. Cars and other objects are some examples of scatterings, which represent a critical issue at millimeter-wave bands. This paper presents the large-scale propagation characteristics for millimeter-wave in a parking lot environment. A new physical-based path loss model for parking lots is proposed. The path loss was investigated based on different models. The measurement was conducted at 28 GHz and 38 GHz frequencies for different scenarios. Results showed that the path loss exponent values were approximately identical at 28 GHz and 38 GHz for different scenarios of parking lots. It was found that the proposed compensation factor varied between 10.6 dB and 23.1 dB and between 13.1 and 19.1 in 28 GHz and 38 GHz, respectively. The proposed path loss models showed that more compensation factors are required for more scattering objects, especially at 28 GHz.

scholarly journals Path Loss Model with Antenna Height Dependency under Indoor Stair Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yu Yu ◽  
Yang Liu ◽  
Wen-Jun Lu ◽  
Hong-Bo Zhu
Keyword(s):  
Standard Deviation ◽  
Maximum Likelihood ◽  
Path Loss ◽  
Likelihood Estimation ◽  
Least Square ◽  
Line Of Sight ◽  
Loss Model ◽  
Path Loss Model ◽  
Proposed Model ◽  
Receiving Antenna

A novel, receiving antenna-height-dependent path loss model under indoor stair environment is presented. The effect of a cross-beam in the stairs is also considered. The proposed model can be applied to describe both of the line-of-sight (LOS) and the non-LOS (NLOS) cases. By using least square criterion, the parameters of proposed model are extracted. Finally, using the maximum likelihood estimation, the precision of the proposed model is evaluated by the standard deviation of shadowing.

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