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Construction of an indoor radio environment map using gradient boosting decision tree

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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.

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Acknowledgements

The authors gratefully acknowledge the support extended by the Taiwan Tech-Tokyo Tech Joint Research Program, under Grant TIT-NTUST-107-05 and the Ministry of Science and Technology, Taiwan, under grant MOST- MOST-108-2218-E-011-029-.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Syahidah Izza Rufaida, Jenq-Shiou Leu & Kuan-Wu Su

  2. Wireless Systems Laboratory, Wireless Networks Research Center, National Institute of Information and Communications Technology, Tokyo, Japan

    Azril Haniz

  3. Department of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, Tokyo, Japan

    Jun-Ichi Takada

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  1. Syahidah Izza Rufaida
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Correspondence to Jenq-Shiou Leu.

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Rufaida, S.I., Leu, JS., Su, KW. et al. Construction of an indoor radio environment map using gradient boosting decision tree. Wireless Netw 26, 6215–6236 (2020). https://doi.org/10.1007/s11276-020-02428-7

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