Joint design method of IRS reflection pattern and channel estimation based on deep learning

Abstract

The invention discloses a joint design method of IRS reflection pattern and channel estimation based on deep learning, and the method aims at an intelligent reflection surface assisted wireless communication system, and comprises the following steps: (1) generating a training data set needed for training a nonlinear neural network; (2) building a nonlinear neural network, and jointly training the nonlinear neural network by using the training data set generated in the step (1) to obtain an intelligent reflection surface reflection pattern and channel estimation; (3) enabling the base station to send the reflection pattern obtained through training in the step (3) to the intelligent reflection surface and configure the reflection pattern; and (4) enabling the base station to carry out online channel estimation by adopting the channel estimation nonlinear neural network obtained by training in the step (3). Compared with a traditional channel estimation method, the method has the advantages that the overhead of the pilot frequency can be remarkably reduced on the premise of the same channel estimation precision, the online calculation complexity is low, and engineering implementation is facilitated.

Description

technical field [0001] The invention relates to an intelligent reflective surface assisted communication system, especially a joint design method based on deep learning, IRS reflection pattern and channel estimation. Background technique [0002] For smart reflective surface communication, the traditional channel estimation methods are Least Squares (LS) and Linear Minimization of Mean Squared Error (LMMSE) algorithms. However, in order to obtain higher channel estimation accuracy, traditional methods require more pilot overhead. [0003] In recent years, deep learning methods have been widely used in the field of communication. It can train the neural network offline, and then use the trained neural network for online deployment. Compared with traditional methods, deep learning-based methods have the advantages of low computational complexity and strong robustness. [0004] The existing intelligent reflective surface communication channel estimation method based on deep ...

AI Technical Summary

Problems solved by technology

[0004] The existing intelligent reflective surface communication channel estimation method based on deep learning, on the one hand, uses the LS or LMMSE channel estimation value as the input of the neural network, which requires a large pilot overhead; Reflective surface reflection patterns are designed, resulting in performance loss

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