Abstract
Aiming at the problem of abnormal location often occurring in traditional weak coverage area detection algorithm of intelligent network, a detection algorithm of weak coverage area in intelligent network based on large data is proposed. Firstly, the detection data is collected by data acquisition method based on local characteristics, and then the gray level conversion of these detection data is used to realize the pre-processing of the detection data and the detection after pre-processing. The feature vectors are used to describe the feature points so as to realize the accelerated feature matching of the detected data. Then the region feature detection of the detected data is carried out, and finally the weak coverage area detection algorithm of the intelligent network based on large data is realized. Experiments verify the detection performance of the weak coverage area detection algorithm based on large data in intelligent networks, and draw a conclusion that the detection algorithm based on large data has a much smaller probability of abnormal location than the traditional weak coverage area detection algorithm in intelligent networks.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Kang, Yj., Ma, L., Gong, Gc. (2019). Weak Coverage Area Detection Algorithms for Intelligent Networks Based on Large Data. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_13
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DOI: https://doi.org/10.1007/978-3-030-36402-1_13
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