scholarly journals New Empirical Path Loss Model for 28 GHz and 38 GHz Millimeter Wave in Indoor Urban under Various Conditions

2018 ◽  
Vol 8 (11) ◽  
pp. 2122 ◽  
Author(s):  
Zyad Nossire ◽  
Navarun Gupta ◽  
Laiali Almazaydeh ◽  
Xingguo Xiong
Keyword(s):  
Urban Areas ◽  
Building Materials ◽  
Rapid Development ◽  
High Capacity ◽  
Path Loss ◽  
Window Size ◽  
Loss Model ◽  
Path Loss Model ◽  
Wireless Signal ◽  
Loss Prediction

Due to rapid development in mobile communication technology in recent years, the demand for high quality and high capacity networks with thorough coverage has become a major necessity. Several models have been developed for predicting wireless signal coverage in urban areas, but these models suffer from inadequately calculating certain conditions, such as weather and building materials, especially window size. In this paper, we propose a new path loss prediction model based on the measurement of new indicators, such as window size, temperature, and humidity conditions, after which an extensive statistical analysis using a linear regression technique was implemented in order to validate the new indicators. As the new indicators were incorporated into the Okumura model to derive a new path loss model, the results showed that the proposed model provides an accurate prediction of the received signal strength in a given propagation environment. Our model enhanced the prediction of path loss by 10% when compared to the Okumura and by 15% when compared to the COST-Hata.

scholarly journals Determination of an Outdoor Path Loss Model and Signal Penetration Level in Some Selected Modern Residential and Office Apartments in Ogbomosho, Oyo State, Nigeria

2018 ◽  
pp. 1-25
Author(s):  
V. O. A. Akpaida ◽  
F. I. Anyasi ◽  
S. I. Uzairue ◽  
A. I. Idim
Keyword(s):  
Building Materials ◽  
Path Loss ◽  
Signal Strength ◽  
Base Stations ◽  
Propagation Path ◽  
Loss Model ◽  
Path Loss Model ◽  
Path Loss Exponent ◽  
Received Power

This article involves the site specific determination of an outdoor path loss model and Signal penetration level in some selected modern residential and office apartments in Ogbomosho, Oyo State. Measurements of signal strength and its associated location parameters referenced globally were carried out. Propagation path loss characteristics of Ogbomosho were investigated using three different locations with distinctively different yet modern building materials. Consequently, received signal strength (RSS) was measured at a distance d in meters, from appropriate base stations for various environments investigated. The data were analyzed to determine the propagation path loss exponent, signal penetration level and path loss characteristics. From calculations, the average building penetration losses were, 5.93dBm, 6.40dBm and 6.1dBm outside the hollow blocks B1, solid blocks B2 and hollow blocks mixed with pre cast asbestos B3, buildings respectively with a corresponding path loss exponent values of, 3.77, 3.80 and 3.63. Models were developed and validated, and used to predict the received power inside specific buildings. Moreover, the propagation models developed for the different building types can be used to predict the respective signal level within the building types, once the transmitter – receiver distance is known. The readings obtained from the developed models were compared with both the measured values and values computed using some existing models with satisfactory results obtained.

scholarly journals Heterogeneous modelling framework for 5G urban macro ultra dense networks

2019 ◽  
Vol 15 (2) ◽  
pp. 962
Author(s):  
Fayad Ghawbar ◽  
Faiz A. Saparudin ◽  
Jumadi A. S. ◽  
Aimi S. A. Ghafar ◽  
N. Katiran
Keyword(s):  
High Capacity ◽  
Path Loss ◽  
Base Stations ◽  
High Definition ◽  
Loss Model ◽  
Path Loss Model ◽  
Modelling Framework ◽  
Power Simulation ◽  
User Terminal ◽  
Simulation Results

The explosive growth of mobile devices is the main engine to continue evolution in the communications field. The amount of traffic generated by today’s users in applications such as high definition videos, cloud computing, and wearable devices, require a drastic change in mobile telecommunications. 5G Ultra Dense Network (UDN) is one of the key components leading in achieving the high capacity for all users. In UDN, the number of base stations or access nodes equals or exceeds the number of active users by unit area. In this paper, different modeling techniques of UDN are studied. Moreover, a heterogeneous framework modeling was proposed. This framework illustrated a system model for UDN based on Urban Macro (UMa) Scenario. The distance dependent path loss model for UMa was presented and analyzed. The Simulation results of path loss model indicated an increase in the path loss with increasing the distance range from 10m to 500m. The received power simulation results of User Terminal (UT) displayed the power is approaching zero when the distance between the BS and UT goes beyond 250m. Therefore, it is assumed that UTs located 250m away from the BS can reuse the subchannel of AN in another sector with negligible interference.

scholarly journals Extension of ITU-R Site-General Path Loss Model in Urban Areas Based on Measurements from 2 to 66 GHz Bands

2021 ◽  
Author(s):  
Motoharu Sasaki ◽  
Mitsuki Nakamura ◽  
Nobuaki Kuno ◽  
Wataru Yamada ◽  
Naoki Kita ◽  
...  
Keyword(s):  
Urban Areas ◽  
Path Loss ◽  
Loss Model ◽  
Path Loss Model

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 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.

Path Loss Model in Crowded Outdoor Environments Considering Multiple Human Body Shadowing of Multipath at 4.7GHz and 26.4GHz

2019 ◽  
Vol E102.B (8) ◽  
pp. 1676-1688 ◽  
Author(s):  
Mitsuki NAKAMURA ◽  
Motoharu SASAKI ◽  
Wataru YAMADA ◽  
Naoki KITA ◽  
Takeshi ONIZAWA ◽  
...  
Keyword(s):  
Human Body ◽  
Path Loss ◽  
Loss Model ◽  
Path Loss Model ◽  
Outdoor Environments

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.

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