Implementation method of underwater acoustic communication receiver by utilizing multipath effect of underwater acoustic channel

Abstract

The invention discloses an implementation method of an underwater acoustic communication receiver by utilizing a multipath effect of an underwater acoustic channel. The method comprises the following steps: on the basis of an orthogonal frequency division multiplexing multicarrier technology, carrying out time reversal signal processing and feature extraction based on a neural network model consisting of a time reversal layer, a batch normalization layer and a convolutional layer by utilizing the multipath effect, and taking as a receiving end of the communication system; realizing the final output of the network through the full connection layer; in a training phase, extracting and learning input features, and inferring a relationship between a received signal and binary data by minimizing a loss function; and in a test stage, inputting the received signal into the trained network model, and recovering binary data. The performance of the underwater acoustic communication receiver is improved, and the method can adapt to different underwater acoustic channel environments.

Description

technical field [0001] The invention relates to the technical field of digital multi-carrier transmission, in particular to an implementation method of an underwater acoustic communication receiver utilizing the multipath effect of an underwater acoustic channel. Background technique [0002] Orthogonal Frequency Division Multiplexing has been widely used in underwater acoustic communication due to its excellent performance in resisting inter-symbol interference and reducing multipath fading effects. Underwater acoustic channel is one of the most challenging communication media, and its fast time-varying, long delay spread and Doppler frequency shift characteristics make the receiver design of underwater acoustic communication particularly important. [0003] In recent years, due to the powerful learning ability of deep neural networks, their introduction into wireless communication has achieved better performance than traditional methods and exhibited great potential. Exis...

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Problems solved by technology

[0004] However, the existing receiver model based on deep neural network has great limitations for the channel environment, that is, it cannot well match the channel environment which is significantly different from the training channel.

In addition, the existing network models are all fully connected neural network structures, which usually involve a large number of training parameters and require high computational complexity

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