Propagation Characteristics of Suburban Environments Using Hybrid Ray-Tracing Simulation

Lee, Kyung-Gyu; Oh, Seong-Jun; Woo, Jeong-Soo; Lee, Kyung-Tak

doi:10.1109/vtcfall.2016.7881181

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Aster is a high-performance propagation model for Atoll that supports macro, micro, and small cell urban propagation scenarios. Aster is based on two major components: Vertical diffraction over rooftops, based on the Walfisch Ikegami model, and the multiple knife-edge Deygout method and horizontal diffraction, based on ray tracing [4,5,24,25]. In [24], the Walfisch Ikegami model facilitates radio frequency (RF) path-loss predictions in typical suburban and urban environments, where the building heights are quasi-uniform.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Machine Learning Scheme for mmWave Path Loss Modeling for 5G Communications in Dense Urban Scenarios

Jin¹,

Kim²,

Lee³

2022

Electronics

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Accurate and efficient path loss prediction in mmWave communication plays an important role in large-scale deployment of the mmWave-based 5G mobile communication systems. Existing methods often present limitations in accuracy and efficiency and fail to fulfill the requirements of cell planning, especially in dense urban environments. In this paper, we propose a novel training method called multi-way local attentive learning, which allows for learning from multiple perspectives on the same set of training samples with local attention paid to each subset of the entire dataset. The sample data set can be partitioned in various ways with respect to different attributes, such that a larger amount of knowledge can be extracted from the same data set. The proposed scheme outperforms the existing schemes in terms of prediction accuracy at the average RMSE of 6.01 dBm.

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Section: Related Workmentioning

confidence: 99%

“…The existing path loss modeling methods are grouped into three categories: empirical methods [2,3], deterministic methods [4,5], and machine learning-based methods [6][7][8][9]. The empirical method builds a model based on curve fitting methods by using measurement data collected in a typical path loss environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning Scheme for mmWave Path Loss Modeling for 5G Communications in Dense Urban Scenarios

Jin¹,

Kim²,

Lee³

2022

Electronics

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“…Therefore, path loss modeling for mmWave propagation plays a vital role in designing and analyzing 5G communication systems. Three types of conventional path loss modeling methods have been investigated in previous studies, namely empirical methods [3]- [7], deterministic methods [8], [9], and machine learning-based (ML-based) methods [10]- [18].…”

Section: Introductionmentioning

confidence: 99%

Millimeter Wave Path Loss Modeling for 5G Communications Using Deep Learning With Dilated Convolution and Attention

Chen

Lee

et al. 2021

IEEE Access

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An accurate and efficient path loss modeling method for millimeter wave communications plays a significant role in the large-scale deployment of a fifth-generation (5G) mobile communication system. Conventional path loss modeling methods such as deterministic methods, empirical methods, and machine learning-based methods, in practice, cannot achieve the desired level of performance in terms of accuracy. This paper proposes a novel path loss model for 5G communications in suburban scenarios using deep learning with dilated convolution and attention. Dilated convolution is used to alleviate the locality of feature extraction and capture the global information on input images. Attention rendered by global context blocks helps the attention-enhanced convolutional neural network (AE-CNN) model utilize the global information on inputs to extract essential features of propagation environments. A distance-embedded local area multi-scanning algorithm which generates input images that can improve learning the latent features with dependency on distance is proposed. The experimental results indicate that the AE-CNN model can outperform state-of-the-art deterministic and empirical methods in terms of root mean square error in test scenarios.

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Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

Afape,

Willoughby,

Sanyaolu

et al. 2024

Results in Engineering

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Propagation Characteristics of Suburban Environments Using Hybrid Ray-Tracing Simulation

Cited by 6 publications

References 6 publications

A Novel Machine Learning Scheme for mmWave Path Loss Modeling for 5G Communications in Dense Urban Scenarios

A Novel Machine Learning Scheme for mmWave Path Loss Modeling for 5G Communications in Dense Urban Scenarios

Millimeter Wave Path Loss Modeling for 5G Communications Using Deep Learning With Dilated Convolution and Attention

Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

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