RPMA Low-Power Wide-Area Network Planning Method Basing on Data Mining
A network planning method based on data mining was proposed for Random Phase Multiple Access (RPMA) low-power wide-area network (LPWAN) with large density of base stations and uneven traffic distribution. First, a signal quality prediction model was established by using the boosting regression trees algorithm, which was used to extract the coverage distribution spacial pattern of the network. Then, the weighted K-centroids clustering algorithm was utilized to obtain the optimal base station deployment for the current spacial pattern. Finally, according to the total objective function, the best base station topology was determined. Experimental results with the real data sets show that compared with the traditional network planning method, the proposed method can improve the coverage of low-power wide-area networks.
KeywordsLow power wide area network Boosting regression trees Weighted K-centroids Base station deployment
This work was supported by National Science & Technology Key Project of China (2017ZX03001008), Natural Science Foundation of China (61871237), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0766) and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA510005).
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