Method for recovering signal based on BPNN

Abstract

The invention discloses a method for recovering a signal based on a BPNN (Back Propagation Neural Network), comprising the following steps: S1. acquiring insertion pilot frequency information of a signal transmitter and receiving pilot frequency information of a signal receiver in a unknown channel, and accordingly constructing a training sample set; S2. building a BPNN model consisting of an input layer, a hidden layer and an output layer; S3. successively inputting each group of sample information in the training sample set into the BPNN model to perform training, so as to obtain a well trained BPNN model; and S4. receiving a signal from the unknown channel and inputting the signal into the well trained BPNN model by the signal receiver, so as to recover an original signal transmitted bythe signal transmitter. Through adoption of the method of the invention, the original signal transmitted by the signal transmitter can be recovered according to the signal received from the unknown channel by the signal receiver, thereby avoiding signal distortion caused by the unknown channel, and improving accuracy and stability of signal transmission.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a signal restoration method based on a reverse propagation neural network. Background technique [0002] In the secure communication model based on physical channel characteristics, channel information is the key point, therefore, more accurate channel information can recover more accurate information signal, theoretically, given perfect channel information (CSI), the transmitter can perform more Reasonable secure coding and other advanced signal processing techniques to ensure security; usually the channel estimation process is assumed to be perfect in secure communication models of physical channel characteristics. However, there are estimation errors in actual channel estimation, and high channel estimation errors seriously affect the accuracy of received signals. [0003] Therefore, channel estimation is very important for the communication security of physical channels...

AI Technical Summary

Problems solved by technology

However, there is an estimation error in the actual channel estimation, and a high channel estimation error seriously affects the accuracy of the received signal

[0003] Therefore, channel estimation is very important for the communication security of physical channels, and neural networks are powerful tools for learning, feedback and tracking; in particular, neural networks can recover channel information in the event of channel estimation failures, for the accuracy of transmitted signals, so neural networks Networks are widely used in the channel estimation process; however, the neural networks used in channel modeling are mainly real-valued neural networks, and there is a large difference between them and the actual channel, which is not conducive to accurate estimation of channel information. There are still some deficiencies in practical application

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