Propagation-free model wireless network planning method based on machine learning

Abstract

A received signal strength (RSS) predictor of a propagation-free model is trained based on a large number of actual network data sets, and the coverage performance of base station (BS) deployment is optimized through a multi-target heuristic method. In particular, more practical features of signal propagation, such as geographical environment and operating parameters of a base station, are fed into a machine learning (ML) model to predict received signal strength; besides, a multi-objective greedy algorithm is designed based on the prediction model, a feasible solution is initialized to meet geographical constraints and is fixed to be related to longitude and latitude of an optimization area, the optimization step length in the search direction is fixed, the step length is set according tothe upper limit and the lower limit of parameters, and the optimization objective is that the coverage rate reaches the standard with the least base stations. Numerical simulation results show that the multi-layer perceptron is superior to other machine learning algorithms in the aspect of received signal strength prediction, convergence and availability of the method are verified through base station deployment simulation, the coverage rate is better than that of actual deployment, and the number of base stations needing to be deployed is smaller.

Description

technical field [0001] The present invention relates to the technical field of wireless communication, in particular to machine learning and dense wireless network planning Background technique [0002] In recent years, ultra-dense networking (Ultra-Dense Network, UDN) is considered to be the main technical means to meet the demand for mobile data traffic in 2020 and in the future. Ultra-dense networking is based on the small coverage and large capacity of the cellular network. By increasing the deployment density of base stations, it effectively supplements the coverage blind spots of traditional 3G and 4G networks, and can provide seamless connections while maintaining high data rates to achieve capacity and Great improvement in frequency reuse efficiency. After years of construction and optimization, my country's mobile communication network has become the largest mobile communication network with complete functions on the earth. The current mobile communication network ...

AI Technical Summary

Problems solved by technology

[0005] At present, the construction of base stations requires a lot of manpower and material resources to fit complex propagation models. This is because the diversity of propagation environments makes some traditional empirical propagation models no longer applicable.

Images