Field intensity prediction method based on modularized neural network

Abstract

The invention relates to the technology of electromagnetic field intensity prediction, so as to enable a prediction model to be more suitable for a complex electromagnetic propagation environment and solve a problem that a conventional scheme is poor in prediction precision and low in convergence speed. The technical scheme employed in the invention is that a field intensity prediction method based on a modularized neural network comprises the following steps: 1, building a radio propagation scene, selecting a number of receiving sample points from the scene, and obtaining the intensity values of the points through measurement or simulation; 2, carrying out the clustering of all sample points through a K-mean clustering method according to the distribution characteristics of field intensity data of received signals, so as to achieve the decomposition of an input sample space, and build corresponding sub-neural-network modules; 3, carrying out the training of the sub-neural-network modules of the modularized neural network through the above sample points; 4, carrying out the prediction through the trained modularized neural network. The method is mainly used in an electromagnetic field prediction occasion.

Description

technical field [0001] The invention relates to a technology for predicting electromagnetic wave field strength, in particular to a method for predicting field strength based on a modular neural network. Background technique [0002] The prediction of wireless electromagnetic wave propagation characteristics is crucial to the planning, design and optimization of wireless communication networks. Traditional radio wave propagation prediction models mainly include empirical models and deterministic models. The prediction accuracy of the empirical model is not high; while the deterministic model requires accurate scene environment information and requires a large calculation; while the received signal field strength prediction model based on the neural network does not need to give accurate environmental information, it can obtain Field strength prediction with sufficient accuracy and good generalization ability for different propagation environments. In the existing neural ne...

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Problems solved by technology

[0003] In order to overcome the deficiencies of the prior art, the present invention aims to make the prediction model more adaptable to the complex electromagnetic wave propagation environment, and solve the problem of poor prediction accuracy and slow convergence speed of the existing solutions

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