.png){kind=icon}
返回
Enhancing Machine Learning Models for Path Loss Prediction Using Image Texture Techniques
.png){kind=icon}
PRE
AI
DOI：10.1109/LAWP.2021.3086180.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The performance of machine learning (ML)-based path loss models relies heavily on the data they use at their inputs. Feature engineering is, therefore, essential for the model's success. In this letter, at hand, we extract a new set of input features, based on image texture. The image that we use represents the footprint of the urban built-up area, where the gray scale values of the building blocks are analogue to their heights. We extract texture information by applying the segmentation-based fractal texture analysis algorithm on the orthogonal area that is bounded between the transmitter and the receiver. To the best of our knowledge this is the first time that such a technique is applied to a path loss modeling problem in electromagnetics. The algorithm, thus, delivers a new set of features, based on the image's texture, which eventually reveal the built-up profile of the area. These new features are injected to an already existing feature set. Comparative analysis shows that the addition of texture-based features leads to enhanced predictions, for a diverse set of transmitter heights, ML algorithms, and performance metrics.
Keyword:
Feature extraction
Gray-scale
Buildings
Transmitters
Predictive models
Receivers
Data models
Image texture
machine learning
mobile communications
path loss prediction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
1.0W
被引数:
2.8W
机构
引用论文
分享收藏
Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel甚高频无线信道路径损耗预测的神经网络参数确定.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications纳米硅薄膜作为光电和光伏应用的多功能材料
PREAI
分享收藏分享收藏
查看更多内容