阅读说明：本技术 一种信道估计模型训练方法及设备 (Channel estimation model training method and device ) 是由 黄鸿基 胡慧 刘劲楠 杨帆 于 2019-04-30 设计创作，主要内容包括：本申请实施例提供一种信道估计模型训练方法及设备,该方法包括：将第一信道矩阵转换为码字信息,并利用所述码字信息重建信道矩阵,得到第二信道矩阵；利用所述第一信道矩阵和所述第二信道矩阵对信道估计模型进行深度学习,得到训练后的信道估计模型,其中,所述信道估计模型是基于深度神经网络构建的；获取终端发射的第一信号；利用所述训练后的信道估计模型,对所述第一信号进行信道估计。采用本申请实施例,能够降低信道估计的误差。(The embodiment of the application provides a channel estimation model training method and equipment, wherein the method comprises the following steps: converting the first channel matrix into code word information, and reconstructing a channel matrix by using the code word information to obtain a second channel matrix; performing deep learning on a channel estimation model by using the first channel matrix and the second channel matrix to obtain a trained channel estimation model, wherein the channel estimation model is constructed based on a deep neural network; acquiring a first signal transmitted by a terminal; and performing channel estimation on the first signal by using the trained channel estimation model. By adopting the embodiment of the application, the error of channel estimation can be reduced.)

A method of channel estimation, comprising:

converting the first channel matrix into code word information, and reconstructing a channel matrix by using the code word information to obtain a second channel matrix;

performing deep learning on a channel estimation model by using the first channel matrix and the second channel matrix to obtain a trained channel estimation model, wherein the channel estimation model is constructed based on a deep neural network;

acquiring a first signal transmitted by a terminal;

and performing channel estimation on the first signal by using the trained channel estimation model.

The method of claim 1, wherein converting the first channel matrix into codeword information and reconstructing a channel matrix using the codeword information to obtain a second channel matrix comprises:

converting the real and imaginary parts of the first channel matrix into two real vectors;

converting the two real vectors into codeword information;

and reconstructing a channel matrix by using the code word information to obtain a second channel matrix.

The method of claim 2, wherein reconstructing the channel matrix using the codeword information to obtain a second channel matrix comprises:

extracting a second signal and first white noise from the code word information, wherein the second signal is a sending signal;

and reconstructing a channel matrix according to the second signal and the white noise to obtain a second channel matrix.

The method according to any of claims 1-3, wherein before converting the first channel matrix into codeword information and reconstructing the channel matrix using the codeword information to obtain the second channel matrix, the method further comprises:

generating the first channel matrix according to a third signal, a fourth signal and a second white noise, wherein the third signal is a signal sent by network equipment; the fourth signal is a signal obtained when the terminal receives the third signal, and the second white noise is white noise fed back by the terminal.

The method of any of claims 1-4, wherein the channel estimation model comprises a coding network and a decoding network, wherein the coding network comprises a first convolutional layer and a first fully-connected layer; the decoding network comprises a second full-connection layer, a second convolution layer, a maximum pooling layer, a first insertion layer, a second insertion layer, a depth connection module, a global pooling layer and a third convolution layer; the channel matrix is configured to be input to a first convolutional layer of the coding network, an output of the first convolutional layer is configured to be input to the first fully-connected layer, an output of the first fully-connected layer is configured to be input to a second fully-connected layer of the decoding network, an output of the second fully-connected layer is configured to be input to the largest pooling layer, an output of the largest pooling layer is configured to be input to the first insertion layer, an output of the first insertion layer is configured to be input to the second insertion layer, an output of the second insertion layer is configured to be input to the deep connection module, an output of the deep connection module is configured to be input to the full pooling layer, an output of the full pooling layer is configured to be input to the third convolutional layer, and the third convolutional layer is configured to generate the second channel matrix.

The method of any of claims 1-4, wherein the channel estimation model comprises a coding network and a decoding network, wherein the coding network comprises a first convolutional layer and a first fully-connected layer; the decoding network comprises a second full-connection layer, a second convolution layer, a maximum pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, a sixth convolution layer and a seventh convolution layer; the channel matrix is configured to be input to a first convolutional layer of the coding network, an output of the first convolutional layer is configured to be input to the first fully-connected layer, an output of the first fully-connected layer is configured to be input to a second fully-connected layer of the decoding network, an output of the second fully-connected layer is configured to be input to the maximum pooling layer, an output of the maximum pooling layer is configured to be input to the third convolutional layer, an output of the third convolutional layer is configured to be input to the fourth convolutional layer, an output of the fourth convolutional layer is configured to be input to the fifth convolutional layer, an output of the fifth convolutional layer is configured to be input to the sixth convolutional layer, an output of the sixth convolutional layer is configured to be input to the seventh convolutional layer, and the seventh convolutional layer is configured to generate the second channel matrix.

The method of any of claims 1-4, wherein the channel estimation model comprises a coding network and a decoding network, wherein the coding network comprises a first convolutional layer and a first fully-connected layer; the decoding network comprises a second fully connected layer, a residual network and a third fully connected layer; the channel matrix is configured to be input to a first convolutional layer of the coding network, an output of the first convolutional layer is configured to be input to the first fully-connected layer, an output of the first fully-connected layer is configured to be input to a second fully-connected layer of the decoding network, an output of the second fully-connected layer is configured to be input to the residual network, an output of the residual network is configured to be input to a third fully-connected layer, and the third fully-connected layer is configured to generate the second channel matrix.

The method according to any one of claims 1 to 7, wherein:

the channel estimation model employs a mechanism of compressed sensing.

The method according to any one of claims 5-7, wherein:

the first convolutional layer is used for converting a real part and an imaginary part of the first channel matrix into two real vectors; the first fully-connected layer is used to convert the two real vectors into the codeword information.

A channel estimation device comprising a processor and a memory, the memory storing program instructions and model parameters, the processor being configured to invoke the program instructions and model parameters to perform the following operations:

converting the first channel matrix into code word information, and reconstructing a channel matrix by using the code word information to obtain a second channel matrix;

performing deep learning on a channel estimation model by using the first channel matrix and the second channel matrix to obtain a trained channel estimation model, wherein the channel estimation model is constructed based on a deep neural network;

acquiring a first signal transmitted by a terminal;

and performing channel estimation on the first signal by using the trained channel estimation model.

The apparatus according to claim 10, wherein the converting the first channel matrix into codeword information, and reconstructing a channel matrix using the codeword information to obtain a second channel matrix specifically comprises:

converting the real and imaginary parts of the first channel matrix into two real vectors;

converting the two real vectors into codeword information;

and reconstructing a channel matrix by using the code word information to obtain a second channel matrix.

The apparatus according to claim 11, wherein the reconstructing a channel matrix using the codeword information to obtain a second channel matrix specifically comprises:

extracting a second signal and first white noise from the code word information, wherein the second signal is a sending signal;

and reconstructing a channel matrix according to the second signal and the white noise to obtain a second channel matrix.

The device according to any one of claims 10 to 12, wherein before the first channel matrix is converted into codeword information and the codeword information is used to reconstruct the channel matrix to obtain a second channel matrix, the processor is further configured to generate the first channel matrix according to a third signal, a fourth signal and a second white noise, where the third signal is a signal sent by a network device; the fourth signal is a signal obtained when the terminal receives the third signal, and the second white noise is white noise fed back by the terminal.

The apparatus of any of claims 10-13, wherein the channel estimation model comprises a coding network and a decoding network, wherein the coding network comprises a first convolutional layer and a first fully-connected layer; the decoding network comprises a second full-connection layer, a second convolution layer, a maximum pooling layer, a first insertion layer, a second insertion layer, a depth connection module, a global pooling layer and a third convolution layer; the channel matrix is configured to be input to a first convolutional layer of the coding network, an output of the first convolutional layer is configured to be input to the first fully-connected layer, an output of the first fully-connected layer is configured to be input to a second fully-connected layer of the decoding network, an output of the second fully-connected layer is configured to be input to the largest pooling layer, an output of the largest pooling layer is configured to be input to the first insertion layer, an output of the first insertion layer is configured to be input to the second insertion layer, an output of the second insertion layer is configured to be input to the deep connection module, an output of the deep connection module is configured to be input to the full pooling layer, an output of the full pooling layer is configured to be input to the third convolutional layer, and the third convolutional layer is configured to generate the second channel matrix.

The apparatus of any of claims 10-13, wherein the channel estimation model comprises a coding network and a decoding network, wherein the coding network comprises a first convolutional layer and a first fully-connected layer; the decoding network comprises a second full-connection layer, a second convolution layer, a maximum pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, a sixth convolution layer and a seventh convolution layer; the channel matrix is configured to be input to a first convolutional layer of the coding network, an output of the first convolutional layer is configured to be input to the first fully-connected layer, an output of the first fully-connected layer is configured to be input to a second fully-connected layer of the decoding network, an output of the second fully-connected layer is configured to be input to the maximum pooling layer, an output of the maximum pooling layer is configured to be input to the third convolutional layer, an output of the third convolutional layer is configured to be input to the fourth convolutional layer, an output of the fourth convolutional layer is configured to be input to the fifth convolutional layer, an output of the fifth convolutional layer is configured to be input to the sixth convolutional layer, an output of the sixth convolutional layer is configured to be input to the seventh convolutional layer, and the seventh convolutional layer is configured to generate the second channel matrix.

The apparatus of any of claims 10-13, wherein the channel estimation model comprises an encoding network and a decoding network, wherein the encoding network comprises a first convolutional layer and a first fully-connected layer; the decoding network comprises a second fully connected layer, a residual network and a third fully connected layer; the channel matrix is configured to be input to a first convolutional layer of the coding network, an output of the first convolutional layer is configured to be input to the first fully-connected layer, an output of the first fully-connected layer is configured to be input to a second fully-connected layer of the decoding network, an output of the second fully-connected layer is configured to be input to the residual network, an output of the residual network is configured to be input to a third fully-connected layer, and the third fully-connected layer is configured to generate the second channel matrix.

The apparatus according to any one of claims 10-16, wherein:

the channel estimation model employs a mechanism of compressed sensing.

The apparatus according to any of claims 14-16, wherein the first convolutional layer is configured to convert a real part and an imaginary part of the first channel matrix into two real vectors; the first fully-connected layer is used to convert the two real vectors into the codeword information.

A computer-readable storage medium, in which program instructions are stored, which, when run on a processor, implement the method of any of claims 1-9.

A computer program product, characterized in that it implements the method of any of claims 1-9 when run on a processor.