Construction of an indoor radio environment map using gradient boosting decision tree
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Construction of an indoor radio environment map using gradient boosting decision tree Syahidah Izza Rufaida1 • Jenq-Shiou Leu1
•
Kuan-Wu Su1 • Azril Haniz2 • Jun-Ichi Takada3
Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Radio environment maps represent a signal strength map or a coverage area of radio networks. Constructing such maps involves gathering signal coverage information in sparse locations, which can be conventionally performed by measurement methods such as the manual drive test. Nevertheless, as this process is large-scale, time-consuming, and costly, several methods for minimization of drive tests have been introduced. Machine learning is commonly used in solving regression or classification problems; in several studies, its performance even surpassed human abilities. In this study, we applied the gradient boosting algorithm to construct radio environment maps from sparse data gathered by user equipments. XGBoost and light gradient boosting machine were experimentally evaluated in constructing base station coverage, reference signal received power, reference signal received quality, and signal-to-noise ratio heatmaps, under various configuration settings. Results validated the superior performance of the two approaches against existing baseline methods k-nearest neighbor and support vector machine. Furthermore, we also assessed our model’s ability to construct radio environment maps based on unseen configuration settings, which confirmed reliable results even if they were trained using completely different sets of configuration settings.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11276-020-02428-7) contains supplementary material, which is available to authorized users. & Jenq-Shiou Leu [email protected] Syahidah Izza Rufaida [email protected] Kuan-Wu Su [email protected] Azril Haniz [email protected] Jun-Ichi Takada [email protected] 1
Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
2
Wireless Systems Laboratory, Wireless Networks Research Center, National Institute of Information and Communications Technology, Tokyo, Japan
3
Department of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, Tokyo, Japan
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Wireless Networks
Keywords Radio environment map Machine learning Gradient boosting Coverage predictions Constructions methods RSRP RSRQ SNR
1 Introduction Coverage area construction remains an open problem in wireless network, and signal strength visualized as radio environment map (REM) can be used as a tool to help resolve this problem. Mobile Network Operators (MNO) analyze signal quality and strength to optimize their services; one such approach is the drive test, which they use for gathering and interpolating signal coverage through mobile signal measurement vehicles.
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