A beam angle prediction method based on fusion of neural network and Kalman filter

By combining LSTM neural networks with Kalman filtering, the problems of high path loss and insufficient robustness in millimeter-wave beam tracking are solved, achieving high-precision beam angle prediction with low overhead and improving the robustness and accuracy of beam tracking.

CN115906923BActive Publication Date: 2026-06-19UNIV OF ELECTRONICS SCI & TECH OF CHINA +1
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Patent Information

Application Number
CN202211403081.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-11-10
Publication Date
2026-06-19
Estimated Expiration
2042-11-10

AI Technical Summary

Technical Problem

Existing millimeter-wave beam tracking technology struggles to maintain accurate tracking in environments with high path loss, and existing algorithms suffer from high overhead and insufficient robustness.

Method used

A method combining a single-layer neural network based on LSTM and Kalman filtering is adopted. By learning the historical changes of beam direction data, the robustness of Kalman filtering and the prediction accuracy of machine learning are combined to reduce the observation frequency and reduce overhead. At the same time, SLNN is used to learn the state transition matrix of Kalman filtering for beam angle prediction.

Benefits of technology

It achieves high-precision prediction of beam direction with low overhead, improving the robustness and accuracy of beam tracking, especially maintaining high tracking accuracy when the beam path changes.

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Abstract

The application discloses a beam angle prediction method based on neural network and Kalman filtering fusion, and belongs to the technical field of millimeter wave communication.The method adopts a single-layer neural network without an activation function to learn the behavior of LSTM, extracts a weight matrix of the single-layer neural network as a state transition matrix of a Kalman filtering algorithm, and performs beam tracking by executing the Kalman filtering algorithm.The application fuses the strong robustness of the Kalman filtering algorithm on the basis of accurate prediction of the beam tracking angle; and since the LSTM has strong learning ability, dense observation of a communication target is not needed, the observation interval can be artificially set according to cost, the prediction cost is greatly reduced, and the performance of the beam tracking system is improved.
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