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Time-varying channel estimation method and system based on deep learning

A time-varying channel and deep learning technology, applied in baseband systems, baseband system components, transmission systems, etc., can solve problems such as increased computational complexity and limitations, and achieve low complexity, improved accuracy, and improved estimation accuracy. Effect

Active Publication Date: 2021-08-20
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, these existing channel estimation methods all learn channel features from more training samples, and larger training samples will cause a rapid increase in computational complexity, which limits the practical application of such methods

Method used

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  • Time-varying channel estimation method and system based on deep learning
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  • Time-varying channel estimation method and system based on deep learning

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Embodiment Construction

[0066] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0067] The present invention provides a time-varying channel estimation method based on deep learning, such as figure 1 shown, including the following steps:

[0068] Step 1: Obtain the frequency-domain pilots and transmission pilots received at the m-1th moment; calculate the base coefficient matrix of the channel at the m-1th moment by the LS algorithm

[0069] Indicates the pilot sent at time m-1, f l is the lth column of the Fourier transform matrix F whose dimension is N×L, and the F matrix can be specifically expressed as l=0,...,L-1; k=0,...,N-1,M q is an N×N-dimensional basis function matrix, and its expression is

[0070] k=0,...,N-1; k'=0,...,N-1, q=0,...,Q-1,b n,q Repre...

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Abstract

The invention discloses a time-varying channel estimation method and system based on deep learning. A network input sample is reasonably constructed; the method is based on a single hidden layer neural network, and comprises the following steps: firstly, fully utilizing channel change characteristics in historical channel information and other characteristics in a received pilot signal; and further improving the performance of channel estimation by using the advantages of least square estimation; secondly, carrying out offline training on a back propagation neural network by using the constructed sample, and then obtaining time-varying channel information in real time in an online mode. In order to reduce the calculation complexity, only the received pilot signals and the information of the pilot sub-channels are adopted, and the pilot sub-channels are modeled by adopting a polynomial basis expansion model to reduce to-be-estimated parameters so as to carry out time-varying channel estimation. According to the method, the channel estimation precision can be remarkably improved, the calculation complexity is low, and the method is suitable for efficient acquisition of time-varying channel information in a high-speed moving scene.

Description

technical field [0001] The invention relates to a time-varying channel estimation method and system based on deep learning, and belongs to the technical field of wireless communication. Background technique [0002] In recent years, the large-scale deployment and rapid development of high-speed railways and expressways have made wireless communication in high-speed mobile environments attract more and more attention worldwide. However, in the high-speed mobile environment supported by 5G and post-5G communication systems, higher vehicle speeds, more frequent handoffs, and wider bandwidths make the design of high-speed mobile wireless communication systems more challenging. Therefore, there is an urgent need for high-performance wireless communication technologies to support future high-speed mobile scenarios to achieve low-latency and highly reliable (URLLC) communications, among which anti-Doppler shift technology is the key. [0003] Among many anti-Doppler frequency shif...

Claims

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Application Information

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IPC IPC(8): H04L25/02
CPCH04L25/0254H04L25/0224
Inventor 杨丽花呼博聂倩任露露杨钦
Owner NANJING UNIV OF POSTS & TELECOMM
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