Method and apparatus for estimating channel based on implicit training sequence

Abstract

The present invention provides a channel estimation method and apparatus based on implicit training sequences for use in wireless communication systems, characterized in that stationary training sequences are superimposed with information sequences for transmission at the transmitting side and channel estimation is performed at the receiving side by using the uncorrelated characteristic between the training sequences and the information sequences, that is, channel estimation is done based on the principle that the estimation result of channel parameter converges to a Wiener solution under the condition that the training sequences and information sequences in the transmission signals are uncorrelated.; The method of the invention comprises steps of: obtaining a set of training sequences comprising a plurality of subsets of training sequences, each of the plurality of subsets is generated based on a known initial training sequence and a predetermined channel order; calculating the cross-correlation matrix for the subsets of training sequences and the corresponding inverse matrix; calculating the cross-correlation matrix for the set of training sequences and the received signals; and estimating channel parameters based on the inverse cross-correlation matrix for the subsets of training sequences and the cross-correlation matrix for the set of training sequences and the received signals.

Description

A Method and Device for Estimating Channel Based on Hidden Training Sequence field of invention The present invention relates to a wireless communication system, in particular to a method and device for estimating a channel. Background technique Signal transmission on a wireless channel usually experiences signal fading and distortion due to transmission distance, obstruction by obstacles, and relative motion between the receiver and the transmitter, making the data received by the receiver different from the data sent by the transmitter . In order to recover data correctly at the receiving end, it is usually used to insert a known training sequence at a specific position in the sent communication data segment, so that the receiving end can obtain the training sequence used for the expected signal distortion by processing the expected training sequence. Channel parameter estimation for data recovery. FIG. 1 shows a typical burst signal structure in the third generation m...

AI Technical Summary

Problems solved by technology

However, this method assumes that the phase shift of each arrival path is known when estimating the time-varying channel, which is unrealistic, and the exponential base expansion model has many parameters to be estimated, and cannot track channel changes in real time

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