Antenna feed optimization method based on gain pre-estimation

Abstract

The invention discloses an antenna feed optimization method based on gain pre-estimation, and the method comprises the steps: obtaining a network data source, generating an abnormal value and a neighbor CI, and performing the matching and splicing of neighbor working parameters; automatically generating a coverage type index problem cell list in a network according to the optimization demands; calculating the neighbor interference degree through neighbor information; modeling the antenna feed optimization according to an optimization algorithm, and obtaining the azimuth angle and downward inclination angle adjustment value of an antenna feed; building a gain actual model of an antenna, and estimating the gain of a terminal user; determining the station adjustment or the updating of the working parameters according to the working parameter errors, and then outputting a new station adjustment scheme; accumulating the network data, and carrying out the result assessment. An RSRP calculation model is applied to the evolutionary algorithm research of the antenna feed optimization, and the proposed method can achieve the calculation of the gain pre-estimation and RSRP estimation caused by the angle adjustment of the antenna feed, thereby effectively solving problems that a path loss propagation calculation model in a complex environment is high in complexity and low in precision, andproviding a gain model and an application method for the intelligent adjustment and optimization of the antenna feed.

Description

technical field [0001] The invention relates to mobile communication technology, in particular to an antenna feed optimization method based on gain estimation. Background technique [0002] Traditional antenna adjustment is dominated by the manual experience of engineers, and it is impossible to determine the optimal adjustment plan through quantitative analysis. There are problems such as low efficiency of repeated adjustment, insufficient consideration of regional impact, single analysis data source, and difficulty in guaranteeing the optimization effect. How to streamline, standardize, and automate the work of wireless antenna feeder optimization, which involves the largest resource investment in traditional optimization, to achieve qualitative analysis and quantitative adjustment, is an urgent problem to be studied and solved. [0003] At present, the typical antenna feeder optimization methods mainly include simulation methods based on experimental simulation, neural ne...

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Problems solved by technology

[0003] At present, the typical antenna feeder optimization methods mainly include simulation methods based on experimental simulation, neural network methods, swarm intelligence optimization methods, etc. These methods can only train the model under the given ideal data, but cannot adapt to reality well. Various complex environments in the environment, and often do not consider the communication characteristics such as antenna combination, partition, and correlation in reality, making it difficult to use the output adjustment scheme directly

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