A wireless transmission method for high-dimensional damaged data based on denoising autoencoder

Abstract

The invention discloses a wireless transmission method for high-dimensional damaged data based on a noise-reduction self-encoder. The method of the invention includes model training and end-to-end transmission. Model training firstly performs data preprocessing on the historical perception data set, and divides it based on the K-fold cross-validation method; then builds a noise reduction autoencoder model, and trains based on the proposed new noise adding method of introducing random Gaussian noise in batches Denoising autoencoder models. In end-to-end transmission, firstly, the trained noise reduction autoencoder is split into two parts and deployed at the sending end and the receiving end, and then preprocessing and dimensionality reduction operations are performed on the perception data of unknown types of noise interference at the sending end, and the dimensionality reduction The data is transmitted to the receiving end, and finally the reconstruction operation is performed on the receiving end to obtain the reconstructed data of the undamaged sensing data. The method of the invention can effectively perform dimension reduction transmission, noise reduction processing and reconstruction of high-dimensional damaged perception data, and can filter out noise interference and dimension reduction transmission when collecting data in harsh environments.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a reliable transmission method for high-dimensional damaged data dimensionality reduction and noise reduction integration based on a noise reduction autoencoder in a wireless sensor network. Background technique [0002] With the rapid development of communication and information technology, the demand for information and the amount of data to be processed are also increasing. How to efficiently process a large amount of network data and minimize transmission, storage, and computing costs has become a major problem that we need to solve urgently. . Wireless sensor networks have been widely used in many fields, such as environmental monitoring, military reconnaissance, precision agriculture and so on. However, due to the size of the sensors, their computing, storage, and communication resources are very limited, and they cannot load the computing and transmission of huge amounts ...

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Problems solved by technology

[0006] The purpose of the present invention is to solve the situation that the wireless sensor network deployment environment is harsh, the starting node resources are limited, and the amount of data to be transmitted is large and damaged. The current network signal processing method has high calculation and design costs, is limited by the scene, accuracy and compression In order to solve the problem of low rate, a wireless transmission method of high-dimensional damaged data based on noise reduction autoencoder is proposed

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