Fast-convergence semi-blind equalization method

Abstract

The invention discloses a fast-convergence semi-blind equalization method which belongs to the field of channel estimation and equalization. The method comprises the following steps: comparing a real part symbol of a current error of an LMS (Least Mean Square) algorithm with a real part symbol of a current error of a MCMA (Modified Constant Modulus Algorithm); if the real part symbols of the two algorithms are the same, utilizing the LMS algorithm to perform real part compensation on the MCMA; comparing a virtual part symbol of the current error of the LMS algorithm with a virtual part symbol of the current error of the MCMA; and if the virtual part symbols of the two algorithms are the same, utilizing the LMS algorithm to perform virtual part compensation on the MCMA. The semi-blind equalization method has a convergence speed higher than that of a blind equalization method. Compared with a linear equalization method, the half-blind equalization method has high convergence precision and higher frequency band utilization rate. And meanwhile, by the adoption of the half-blind equalization method, unreliable error signal caused by LMS algorithm under the condition of inaccurate judgment can be reduced, and the demands of the equalization method on convergence speed and convergence precision can be met at the same time.

Description

technical field [0001] The invention relates to the field of channel estimation and equalization, in particular to a fast-converging semi-blind equalization method. Background technique [0002] In recent years, people have higher and higher requirements for communication quality, but the randomness of the wireless channel will restrict the communication performance of the wireless communication system. Channel estimation and equalization technology can accurately estimate the characteristics and parameters of the wireless channel and express them in a certain mathematical way, so as to estimate the possible input signal of the channel and minimize the estimation error with the original prior input signal, Reduce decision errors and improve communication performance. The equalization techniques to solve the intersymbol interference caused by multipath effects and limited channel bandwidth can be roughly divided into three categories: the first category is the adaptive equal...

AI Technical Summary

Problems solved by technology

Such a semi-blind equalization method does not prejudge the adaptive equalization algorithm, and directly uses it to compensate the blind equalization method, which is easy to introduce some inherent shortcomings of adaptive equalization into the equalization system.

These disadvantages include: First, the error signal calculated by the adaptive equalization method is unreliable when the judgment is inaccurate. If the

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