Channel estimation method and device

Abstract

The invention discloses channel estimation method and device, which are used for reducing time slot CIR (Channel Impulse Response) energy leakage, enhancing the accuracy of time slot treatment and improving channel estimation property, and are easy to realize. The channel estimation method comprises the steps of: carrying out frequency-domain sideband expansion on a least square (LS) estimation value of channel frequency response, windowing a middle expanded frequency-domain sideband by the channel frequency response subjected to the frequency-domain sideband expansion, then carrying out time-domain denoising and filtering treatment, and removing the expanded frequency-domain sideband for the channel frequency response hnCOMB subjected to time domain denoising to obtain an estimation value of the channel frequency response.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to a channel estimation method and device. Background technique [0002] At present, the channel estimation algorithm based on discrete Fourier transform (DFT, Discrete Fourier Transform) is mainly used in Orthogonal Frequency Division Multiplexing (OFDM, Orthogonal Frequency Division Multiplexing) system. (IDFT) converts the estimated value of the channel frequency response to the time domain, estimates the noise in the time domain, and then sets the threshold according to the estimated noise and denoises the channel impulse response (CIR, Channel Impulse Response) through this threshold After filtering, DFT is performed on the estimated value of the filtered channel impulse response to obtain the final estimated channel frequency response. In addition, there are some improved algorithms that preprocess the channel frequency response obtained by the least squares (L...

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Problems solved by technology

[0015] 1. The time-domain Judgment Method does not estimate the channel frequency response in the frequency domain For any processing, there is a non-negligible CIR energy leakage, and the sidelobe of the channel impulse response in the time domain is mixed with the noise, which seriously affects the accuracy of noise calculation and judgment

[0016] 2. The frequency domain plus Hanning window algorithm based on DFT needs to be multiplied and divided by one and For a Hanning window with the same length, that is, N points, the coefficient of the window needs to be calculated in real time according to the different values ​​of N points, especially when N is large, the algorithm complexity is too large

Since the attenuation of the two sidebands of the Hanning window near its cutoff point is very large, once an error occurs in the denoising filter in the time domain, especially when the sideband is close to the cutoff point, it will be amplified a lot when divided by the original Hanning window after DFT times, resulting in an intolerable error

Simply put, prior to IDFT for The modification of its own data within the range of N points, after time-domain denoising filtering, will not be able to recover in the frequency domain due to some small errors that cannot be avoided by time-domain processing

[0017] 3. The frequency domain symmetric extension algorithm based on DFT does not estimate the frequency response of the N-point channel The original value is modified to avoid the above situation, but it will The length of is doubled and becomes 2N, the points of IDFT and DFT are doubled, and the final output result needs to be obtained by calculating the mirror image mean value. In the case of a large N, the complexity of the algorithm is greatly increased.

[0018] To sum up, DFT-based channel estimation methods are commonly used in OFDM systems in the prior art. The advantage of this type of algorithm is that it does not require any channel prior information, and can achieve relatively low complexity. Good estimation performance, but at the same time there are some problems. Since the multipath delay is not always an integer multiple of the OFDM minimum sampling interval, there is a CIR energy leakage in the time domain, which makes the CIR energy and noise mixed in the entire time domain. , resulting in inaccurate noise estimation, and the denoising filtering process will inevitably remove the energy component of the CIR leakage while filtering out the noise, which will eventually affect the performance of channel estimation

In order to improve this situation, many algorithms have made some improvements, that is, corresponding processing is performed in the frequency domain before denoising in the time domain to reduce the CIR energy leakage. Although these algorithms reduce the CIR energy leakage to a certain extent, the frequency The complexity of domain processing is high, and it is difficult to implement when the number of samples is large

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