Network optimization method and device, electronic equipment and computer readable storage medium

Abstract

The embodiment of the invention provides a network optimization method and device, electronic equipment and a computer readable storage medium, and relates to the field of wireless communication. The method comprises the following steps: acquiring current network parameter data of a target optimization cell; and then, on the basis of the network parameter data and the network optimization index of the target optimization cell, according to a preset judgment strategy, determining to generate a network optimization strategy by simulating a neural network or a deep reinforcement learning model, the simulated neural network is generated by training a predetermined neural network based on a pre-obtained network parameter tuning empirical data set of a target optimization cell, and the deep reinforcement learning model is generated based on the simulated neural network; and then, according to a network optimization strategy, optimizing network parameters of the target optimization cell. According to the embodiment of the invention, the simulation neural network and the deep reinforcement learning model can be fused for network optimization, and a solution is provided for perception decision-making of dynamic optimization of the mobile communication network.

Description

technical field [0001] The present application relates to the field of mobile communication technologies, and in particular, the present application relates to a network optimization method, apparatus, electronic device, and computer storage medium. Background technique [0002] Various expert methods have been developed in the field of mobile communications and have been used to optimize wireless networks with various objectives to solve various performance problems. These expert methods include methods for radio resource management (RRM) and self-organizing networks (SON). As a global optimization process, network optimization is likely to affect the network status of its neighboring cells when parameters are adjusted for a cell. Therefore, network optimization should not only be locally (or cell) optimal, but also globally ( or adjacent multi-cell) is optimal. [0003] At present, the existing solutions for mobile communication network optimization mainly include: (1) n...

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Problems solved by technology

[0003] At present, the existing solutions for mobile communication network optimization mainly include: (1) network self-optimization by manually formulating rule tables, by querying various network states, and making corresponding adjustments according to the rule tables formulated by experts; However, the rule table of this scheme cannot cover all possible situations, and the optimization effect of the rule table applied to different devices varies.

(2) Network optimization based on the iterative optimization model. By traversing the parameters of the base station antenna, testing and analyzing the collected network data information, with the goal of maximizing the network performance in the surrounding areas, the collected sample data and Different combinations of antenna parameters are iteratively optimized, and finally the best parameter combination is obtained to achieve the optimization goal; however, the optimization model of this scheme cannot cover all possible situations, and if an unknown state is encountered, optimization cannot be performed, resulting in optimization failure

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