A Network Planning Method for Low Power Wide Area Network Based on Data Mining

Abstract

The invention discloses a low power consumption wide area network network planning method based on data mining, obtains the actual measurement data of the low power consumption wide area network network, starts from the coverage target, comprehensively considers the influencing factors of the weak coverage of the network, and uses the lifting regression tree algorithm to establish The signal quality prediction model is used to extract the coverage distribution spatial pattern of the network; then, the selection of the base station location is treated as a weighted problem based on the coverage distribution spatial pattern, and the weighted K-centroids clustering algorithm is used to obtain the optimal solution for the current pattern. Base station deployment; finally, the final base station topology is determined according to the overall objective function. The invention can well improve the coverage quality of the low power consumption wide area network, and has certain reference value for its network planning.

Description

technical field [0001] The invention relates to the field of wireless network planning and planning, in particular to a method for network planning of a low-power wide area network based on big data mining. Background technique [0002] With the rapid development of the Internet of Things, the number of connected devices is expected to grow to 50 billion, and the business volume will increase by more than a thousand times. Traditional short-distance wireless technology and cellular network technology can no longer meet the diverse business needs of the Internet of Things. Therefore, a new communication mode——Low Power Wide Area Network (LPWAN) emerged at the historic moment, which mainly satisfies large connections and low-speed Internet of Things services, and has the characteristics of large coverage, low connection cost, and low power consumption. . LPWAN mainly includes wireless communication technologies such as NB-IoT, LORA, and RPMA, which can support a very large nu...

AI Technical Summary

Problems solved by technology

However, the determination of the base station site is an NP-hard problem. If the traditional site selection model is used to analyze various factors of the site site problem, it will lead to the dimension disaster of the variables and constraints in the built model. This site selection method not scientific

In addition, network planning needs to consider not only coverage, but also service distribution, and the need to process and integrate space-time characteristics, which makes network planning more complicated and requires a reasonable network planning solution

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