Channel equalization method and channel equalizer based on RLS (recursive least square) and LMS (least mean square) combined algorithm

Abstract

The invention relates to a channel equalization method and a channel equalization system based on an RLS (recursive least square) and LMS (least mean square) combined algorithm. The method includes: step 101), training tapping coefficients of an equalizer on the basis of training data by an RLS equalization algorithm until the equalizer reaches convergence, and assuming that the equalizer reaches convergence when the training data are subjected to Ncth iteration; step 102), iterating the 'jth' bit of received user data, windowing an error value acquired by iteration, and calculating an average error autocorrelation estimation value of data, with a fixed length, in a sliding window; step 103), comparing the acquired average error autocorrelation estimation value with a preset threshold value, and selecting one of equalization algorithms including the RLS equalization algorithm and an LMS algorithm; step 104), equalizing the jth user data by the selected equalization algorithm, updating j as j+1, and then returning to the step 102) until all received user data are processed. The channel equalization method and the channel equalization system have the advantages that excellent performance in time-varying channels is achieved, and requirements on real-time performance can be met.

Description

technical field [0001] The present invention relates to the field of communications, in particular to recursive least square (Recursive Least Square, RLS) and least mean square (Least Mean Square, LMS) equalizer techniques in adaptive equalization techniques. Specifically, it involves adaptively selecting different equalization techniques according to channel changes. Background technique [0002] In the field of adaptive equalization in communication, LMS equalization and RLS equalization are the two most widely used technologies. The LMS algorithm is obtained by minimizing the mean square error. The algorithm is simple and the complexity is low, but its convergence is slow, and it often cannot achieve convergence in fast time-varying channels, and its performance is poor. The RLS algorithm makes up for the slow convergence of the LMS algorithm by minimizing the weighted sum of the square errors. Compared with the LMS algorithm, it greatly reduces the length of the trainin...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a more practical RLS-LMS joint algorithm in order to overcome the relatively high complexity of the RLS equalizer

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