The Effect of Antenna Height on the Performance of the Okumura/Hata Model Under Different Environments Propagation

Mahmood, Tahreer; AL-Qaysi, Hayder Khaleel; Hameed, Abbas Salman

doi:10.1109/conit51480.2021.9498395

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…The real BS settings are adopted in the tool: the radiated power of the BS was 36 dBm, Okumura-Hata as PL model for a suburban area [14], and a frequency of 800 MHz. The LTE network simulation is executed for an extraordinary situation (i.e., festivity day) where there are many users (i.e., 650@2Mbps) connected at the same time in a small area (4.8 km 2 ).…”

Section: E Lte Networkmentioning

confidence: 99%

Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels

Santana¹,

David

Alonso

et al. 2022

2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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Accurate estimation of Propagation Path Loss is important for reliable and optimized coverage of a service. In literature, a diversity of theoretically or experimentally based propagation models have been documented to estimate the received signal level. The goal of this work is to estimate the effective coverage area of service, predict the Path Loss, and build a Radio Environment Map (REM) using a sensor network. To this end, a sensor's correlation area is defined. By using Machine Learning (ML), the received signal level variation in this area can be estimated correctly 92.3% of the time, with a Mean Absolute Error (MAE) of 1.57 dB. Finally, a proper distribution of sensors based on the correlation area, and ML tools leads to building a REM for the effective coverage area. This approach is applied to a Long-Term Evolution network.

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Section: E Lte Networkmentioning

confidence: 99%

Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels

Santana¹,

David

Alonso

et al. 2022

2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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“…Deep-learning methods are usually used in solving complex problems, but require more computational time. The extraction of elevation and azimuth angles from CIR to distinguish the NLoS and LoS scenarios in urban places was performed in [11]. Also, an accurate classification performance was achieved using convolutional networks in the problem of fingerprint feature extraction and classification [12].…”

Section: Introductionmentioning

confidence: 99%

Wireless Communication Channel Scenarios: Machine-Learning-Based Identification and Performance Enhancement

et al. 2022

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Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions in 6G. Machine learning (ML) has been used for scenario identification tasks. In this paper, the least absolute shrinkage and selection operator (LASSO) is used instead of ElasticNet in order to reduce the computational time of data preprocessing for ML. Moreover, the computational time and performance of different ML models are evaluated based on a regularization technique. The obtained results reveal that the LASSO operator achieves the same feature selection performance as ElasticNet; however, the LASSO operator consumes less computational time. The achieved run time of LASSO is 0.33 s, while the ElasticNet corresponding value is 0.67 s. The identification for each specific class for K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and k-Means and Gaussian Mixture Model (GMM) is evaluated using Receiver Operating Characteristics (ROC) curves and Area Under the Curve (AUC) scores. The KNN algorithm has the highest class-average AUC score at 0.998, compared to SVM, k-Means, and GMM with values of 0.994, 0.983, and 0.989, respectively. The GMM is the fastest algorithm among others, having the lowest classification time at 0.087 s, compared to SVM, k-Means, and GMM with values of 0.155, 0.26, and 0.087, respectively.

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Enhancement the Time Variation of Channel Gain by Realize Doppler Spectrum Using Filtered White Gaussian Noise (FWGN) Model

Mahmood

Ahmed

Hameed

2023

Wireless Pers Commun

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The Effect of Antenna Height on the Performance of the Okumura/Hata Model Under Different Environments Propagation

Cited by 8 publications

References 21 publications

Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels

Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels

Wireless Communication Channel Scenarios: Machine-Learning-Based Identification and Performance Enhancement

Enhancement the Time Variation of Channel Gain by Realize Doppler Spectrum Using Filtered White Gaussian Noise (FWGN) Model

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