基于深度学习的无线网络通信错误数据修复系统

Wireless network communication error data repair system based on deep learning

无线网络通信受到数据多重属性因素和信道畸变影响,导致数据错误,为了提高通信稳定性,提出基于深度学习的无线网络通信错误数据修复及系统设计方法。采用通信信号欠采样方法实现对无线网络通信数据感知,在无线网络的信道模型中采用畸变参数提取和压缩感知方法实现对网络通信错误数据特征提取,通过干扰滤波抑制方法,建立无线网络通信错误数据的深度学习模型,根据欠采样和压缩感知理论进行无线网络通信错误数据的特征重构,结合相关性的正交基分解方法实现无线网络通信错误数据修复。在嵌入式的集成开发平台中实现数据修复系统的集成开发设计。测试结果表明,采用该方法进行无线网络通信错误数据修复的召回性较好,提高了无线网络通信的抗干扰能力和纠错能力。

Wireless network communication is affected by multiple attribute factors of data and channel distortion,which leads to data errors.In order to improve communication stability,a method of repairing data errors in wireless network communication and system design based on deep learning is proposed.The communication signal undersampling method is used to realize the perception of wireless network communication data,the distortion parameter extraction and compressed sensing method are used in the channel model of wireless network to realize the feature extraction of network communication error data,the deep learning model of wireless network communication error data is established by the interference filtering suppression method,the feature reconstruction of wireless network communication error data is carried out according to the undersampling and compressed sensing theory,and the wireless network communication error data is repaired by the orthogonal basis decomposition method of correlation.Realize the integrated development and design of the data repair system in the embedded integrated development platform.The test results show that this method has a good recall in repairing the wrong data of wireless network communication,and improves the anti-interference ability and error correction ability of wireless network communication.