NDLI: Random forests-enabled context detections for long-term evolution network for railway

Content Provider	IET Digital Library
Author	Zhang, Lei Ni, Qin Zhang, Guanglin Zhai, Menglin Moreno, Juan Briso, Cesar
Abstract	An ensemble learning-based approach for context detection for high-speed railway (HSR) is proposed, evaluated, and compared against various machine learning algorithms. The context information is collected and formatted from the realistic physical data, which are measured by the field test in a commercial 4G cellular network on-board high-speed train in an open area, HSR station, and typical urban area. The radio propagation knowledge implies that the channel model is implemented in feature extractions. The independent out-of-bag errors show ensemble learning approaches; especially, random forests-based algorithm achieves very accurate context detection (up to 93.5%), which is much higher than single tree (66%). Other ensembles with sub-spaces of discriminant (67%) and K-nearest neighbour (69%), as well as linear discriminant analysis (55.7%), and support vector machine (SVM) (27.7%). Furthermore, the features are selected based on the feature importance evaluation in the first round training. The selected predominant features and the reasonable number of individual trees construct the refined random forests, which obtain a high accuracy (92.6%) and 70% time reducing. This experimental study benefits from physical radio knowledge to support advanced long-term evolution (LTE) network for railway and future smart rail applications.
Starting Page	1080
Ending Page	1086
Page Count	7
ISSN	17518725
Volume Number	13
e-ISSN	17518733
Issue Number	Issue 8, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-map/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-map.2018.6025
Journal	IET Microwaves, Antennas & Propagation
Publisher Date	2019-03-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	4G Mobile Communication Accurate Context Detection Cellular Radio Combinatorial Mathematics Commercial 4G Cellular Network Context Information Ensemble Learning-based Approach Feature Extraction Feature Extractions High-speed Railway HSR Station Independent Out-of-bag Error K-nearest Neighbour Knowledge Engineering Technique Learning in AI Linear Discriminant Analysis Long-term Evaluation Network Machine Learning Algorithm Mathematics Mobile Radio System Nearest Neighbour Method On-board High-speed Train Physical Radio Knowledge Radio Propagation Knowledge Railway Communication Random Forest Random Forests-enabled Context Detections Refined Random Forests Smart Rail Application Statistics Support Vector Machine Trees Typical Urban Area
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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