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Air-sea wireless channel estimation method based on deep learning

A technology of wireless channel and deep learning, applied in the field of air-sea wireless channel estimation based on deep learning, to achieve the effect of reducing model calculation complexity, reducing model parameter redundancy, and breaking performance bottlenecks

Active Publication Date: 2021-09-21
SUN YAT SEN UNIV
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  • Application Information

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Problems solved by technology

However, the above scheme is aimed at the land wireless channel scenario, and does not combine the characteristics of the sea wireless channel for customized design, so there is a large room for improvement

Method used

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  • Air-sea wireless channel estimation method based on deep learning
  • Air-sea wireless channel estimation method based on deep learning
  • Air-sea wireless channel estimation method based on deep learning

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Embodiment 1

[0059] Such as figure 1 As shown, a deep learning-based air-sea wireless channel estimation method includes the following steps:

[0060] S1: Build a maritime wireless channel simulation platform to generate maritime wireless channel transfer function CTF data;

[0061] S2: In the OFDM downlink communication system, collect the OFDM pilot received signal, and calculate the least squares estimate of the CTF at the pilot position;

[0062] S3: Preprocess the CTF data and least squares estimation to construct a training data set for the wireless channel at sea;

[0063] S4: Construct an efficient channel estimation convolutional neural network and perform offline training on it according to the training data set, and update the weight parameters of the efficient channel estimation convolutional neural network;

[0064] S5: Solidify the weight parameters of the convolutional neural network for efficient channel estimation, and test it online to realize the estimation of the air-...

Embodiment 2

[0102] More specifically, on the basis of Embodiment 1, in order to more fully illustrate the beneficial effects of the present invention, the effectiveness and advancement of the present invention will be further described below in conjunction with the simulation analysis and results of a specific embodiment.

[0103] In this solution, an OFDM-based downlink air-sea communication system is considered, and an LTE downlink pilot pattern, namely trellis pilot, is used. In the channel simulation platform, the height of the transmitting antenna is 800m, the height of the receiving antenna is 10m, the relative speed between the aircraft and the ship is 50km / h, the minimum horizontal distance of the transceiver is 1km, and the maximum horizontal distance is 30km. In the model test stage, the horizontal distance of the transceiver is fixed at 10km. The system uses a carrier frequency of 2GHz, a bandwidth of 1.4MHz, a conventional CP subframe format, the number of subcarriers in a sub...

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Abstract

The invention provides an air-sea wireless channel estimation method based on deep learning, and the method comprises the steps: generating channel transmission function (CTF) data by building an offshore wireless channel simulation platform; collecting an orthogonal frequency division multiplexing pilot frequency receiving signal, and calculating least square estimation of the CTF at a pilot frequency position; preprocessing the least square estimation of the CTF data and the CTF at the pilot frequency position to construct a training data set; constructing an efficient channel estimation convolutional neural network, performing offline training on the efficient channel estimation convolutional neural network according to the training data set, and updating weight parameters of the efficient channel estimation convolutional neural network; and solidifying weight parameters of the efficient channel estimation convolutional neural network, and performing online testing. According to the air-sea wireless channel estimation method provided by the invention, sparse features on a channel delay domain are extracted by using trainable sparse transformation, so that model parameter redundancy can be reduced; meanwhile, according to the method, interpolation calculation of any up-sampling factor is achieved through a sub-pixel convolution layer and a cutting layer, the model calculation complexity can be reduced, and the mean square error performance bottleneck is broken through.

Description

technical field [0001] The present invention faces the field of maritime wireless communication, and proposes a method for estimating air-sea wireless channels based on deep learning. Background technique [0002] Thanks to the continuous development of theoretical research and technological innovation, terrestrial wireless communication has undergone the development from the first generation of mobile communication technology to the fifth generation of mobile communication technology, bringing earth-shaking changes to human society. However, due to the complex and changeable marine environment, the development of wireless communication at sea lags far behind that of wireless communication on land. How to realize high-speed data transmission, highly reliable wireless access, and high-quality user experience of maritime terminals is a challenge for current communication technologies. [0003] As the core technology of 3GPP LTE, Orthogonal Frequency Division Multiplexing (OFD...

Claims

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Application Information

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IPC IPC(8): H04L25/02G06N3/04G06N3/08
CPCH04L25/0254G06N3/08G06N3/045
Inventor 江明陈俊羽
Owner SUN YAT SEN UNIV
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