Channel prediction method based on particle filtration correction

Abstract

The invention relates to a channel prediction method based on particle filtration correction, which comprises the following steps: (a) obtaining an AR linear prediction model by training sequence of the historical information of a channel, then carrying out prediction by an LRP channel prediction algorithm, and outputting the prediction value; (b) carrying out error calculation on the output prediction value and an actual value; if the error e between the prediction value and the actual value is smaller than a set value E, using the prediction value of the LRP channel prediction algorithm as the channel estimation value; if the error e between the prediction value and the actual value is larger than the set value E, and the system is disturbed by nonlinear non-Gaussian noise, entering a particle filter in the next period of time to carry out particle filtration correction, and using the prediction value of the particle filtration as the channel estimation value under the prior probability; and (c) updating the coefficient of the AR linear prediction model, and carrying out channel prediction for the next period of time. The channel prediction method based on particle filtration correction has the characteristics of stable channel estimation performance, strong robustness and strong anti-noise ability, and can be realized easily.

Description

technical field [0001] The invention belongs to a wireless communication channel prediction method, in particular to a HSDPA channel prediction method based on particle filter correction in a high-speed mobile environment. Background technique [0002] In recent years, people's requirements for improving the transmission capacity of wireless communication systems and improving communication capacity in high-speed environments are constantly increasing. It is hoped that a large number of data signals can be transmitted at high speed even in high-speed driving environments. Due to the time-varying and nonlinear nature of wireless channels in the frequency domain and time domain, mathematical modeling and quantitative analysis are difficult. In practical applications, linear signal processing methods are used to approximate nonlinear wireless channels. However, wireless The complexity of channel changes in mobile communication limits the performance of wireless communication sy...

AI Technical Summary

Problems solved by technology

However, Kalman filtering is also a kind of linear prediction like the AR model. When the system suffers from nonlinear noise, this method has limitations. The

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