Channel prediction method combining deep learning and basis extension model

Abstract

The invention discloses a channel prediction method combining deep learning and a basis extension model in the technical field of wireless communication. The channel prediction method comprises the following steps of: step 1, acquiring a correlation matrix of a channel according to channel information at a historical moment; step 2, carrying out eigenvalue decomposition on the correlation matrix to obtain an optimal primary function; step 3, modeling a channel by using the basis expansion model; step 4, acquiring a basis coefficient estimation value based on historically received pilot signals and an optimal basis function; step 5, constructing a training sample set according to the basis coefficient estimation value; step 6, training a BP neural network by using the training sample set; step 7, acquiring a channel prediction model with an optimal weight and an optimal threshold value; step 8, performing online prediction based on the channel prediction model; and step 9, converting the basis coefficient estimation value into a frequency domain channel matrix. The channel prediction method has low calculation complexity and high prediction precision, and is suitable for efficient acquisition of time-varying channel information in a high-speed mobile environment in the future.

Description

technical field

[0001] The invention relates to a channel prediction method. Background technique

[0002] In recent years, with the large-scale deployment and operation of high-speed railways and highways, wireless communication in high-speed mobile environments has attracted more and more attention worldwide. Moreover, in the future high-speed mobile scenario (B5G), the vehicle speed will be higher and higher, and the higher vehicle speed will cause a greater Doppler frequency shift, which will lead to rapid time-varying wireless channels, thus This makes the acquisition of channel information more challenging in this scenario. Due to the existence of processing delay, the traditional time-varying channel estimation technology causes the estimated channel to be seriously outdated, and the channel prediction technology is widely used in high-speed mobile scenarios because it can predict the channel in the future based on historical channel information. Efficient acquisiti...

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