A realization method of underwater acoustic communication receiver utilizing multipath effect of underwater acoustic channel

Abstract

The present invention disclosed a method of implementing a water acoustic communication receiver with a multi -diameter effect of water sound channels. The method includes: on the basis of orthogonal frequency division of multi -carrier wave technology, the multi -path effect is based on the time reversal layer ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑The neural network model composed of a normalized layer 积 convolutional layer performs time reversal signal processing and feature extraction as a receiver of the communication system; the final output of the network is realized through the full connection layer;Learn the input characteristics and infer the relationship between the receiving signal and binary data through the minimum loss function; in the test stage, the receiving signal is entered into the training network model to restore binary data.The present invention improves the performance of the water acoustic communication receiver and can adapt to different water channels.

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 technology has been widely used in underwater acoustic communication due to its excellent performance in resisting intersymbol interference and reducing multipath fading effects. The 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, the introduction of deep neural networks into wireless communication has achieved better performance than traditional methods and demonstrated great potential due to their powerful learnin...

AI Technical Summary

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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