Channel estimation method of OFDM (orthogonal frequency division multiplexing) system

Abstract

The invention discloses a channel estimation method of an OFDM (orthogonal frequency division multiplexing) system. The channel estimation method comprises the following steps of: generating a Hankel matrix of each sub-segment, performing SVD (singular value decomposition) on the Hankel matrix of each sub-segment, retaining one bit or multiple bits before diagonal entries in a singular value diagonal matrix corresponding to the Hankel matrix of each sub-segment, forming a new singular value diagonal matrix corresponding to the Hankel matrix of each sub-segment, further generating a new Hankel matrix of each sub-segment according to the new singular value diagonal matrix, extracting estimation channel information of each sub-segment according to the new Hankel matrix and finally combining the estimation channel information of each sub-segment to form the complete estimation channel information. According to the method disclosed by the invention, the accuracy in channel estimation can be improved.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to an OFDM system channel estimation method. Background technique [0002] Channel estimation is an important part of Orthogonal Frequency Division Multiplexing (OFDM) system, and the accuracy of channel estimation directly affects the performance of the whole system. In the prior art, there are roughly two channel estimation methods based on pilot patterns: one is a time-domain denoising channel estimation method based on discrete Fourier transform (DFT), and the other is a linear minimum mean mean method based on a Wiener filter. square error (LMMSE) method. The above two methods are described in detail below. [0003] figure 1 It is a flow chart of the DFT-based time-domain denoising channel estimation method in the prior art, such as figure 1 As shown, the method includes the following steps: [0004] Step 101, the receiver receives the pilot signal at the pilot positi...

AI Technical Summary

Problems solved by technology

[0022] In the LMMSE method based on the Wiener filter, in the process of generating the Wiener filter coefficients, when calculating R hh and R * hh When , it is necessary to obtain the multipath delay of the current channel in advance. When calculating , it is necessary to estimate the SNR value in advance, but due to the time-varying nature of the channel in the actual environment, it is difficult to accurately obtain the above statistical information of the current channel, thus affecting the accuracy of this channel estimation method

In practical applications, even with a large number of simulated filter coefficient tables, the robustness in real environments is poor

[0023] In summary, according to the above analysis, the accuracy of these two channel estimation methods in the prior art is not high

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