CN101547180A - Distributed channel estimation method - Google Patents

Abstract

The invention discloses a distributed channel estimation method, which belongs to the field of digital communication. The method of the present invention is: 1) the sending end sends the training sequence intermittently; 2) the receiving end converts the received time domain training sequence into a frequency domain training sequence; 3) removes the modulation information from the frequency domain training sequence; The modulus of the frequency domain training sequence after removing the modulation information is jointly estimated to obtain the modulus of the frequency domain channel response; the phase of the frequency domain training sequence after removing the modulation information is jointly estimated to obtain the phase of the frequency domain channel response. Compared with the prior art, the present invention not only greatly simplifies the complexity of channel estimation, but also has high reliability, stability and precision in estimation results.

Description

A Distributed Channel Estimation Method

technical field

The invention belongs to the field of digital communication, and in particular relates to a distributed channel estimation method under channel conditions with insignificant time-varying characteristics.

Background technique

Traditional channel estimation methods generally use training sequences or pilots before data information for channel estimation, and mostly use the channel results estimated from a single frame to equalize a single frame, which is very effective for wireless channels with significant time-varying characteristics. However, this ignores channels with insignificant time-varying characteristics, such as the possibility of jointly estimating channels from frames at different times under wired channel conditions.

In order to estimate the channel accurately, the current communication system generally adopts to send multiple sets of continuous training sequences in the same frame. The real and imaginary parts of the channel estimated by multiple sets of training sequences in the same frame can be directly added and averaged, but the real and imaginary parts of the channel estimated by the training sequences of different frames cannot be directly added and averaged. In digital communication systems, the deviation of sampling start time and carrier start phase is the fundamental reason why channel estimation results at different times cannot be directly weighted and averaged.

In order to jointly use the channel estimation results at different times, one processing method is: select the channel estimation results at a certain time as a reference point, and remove the channel estimation results at other times from the sampling start time deviation relative to the reference point and the initial phase deviation of the carrier, and finally carry out a weighted average of the processing results at each moment. This method of joint processing of channel estimation results at different times is very dependent on the reliability of the reference point. Once there is an error in the channel estimation result selected as the reference point, the error will spread to the channel estimation result at each time, resulting in the final channel The estimation results are wrong and the reliability is low.

Contents of the invention

The purpose of the present invention is to propose a distributed channel estimation method, which uses distributed channel estimation, that is, intermittently sends training sequences, and the receiving end processes the frequency domain channel response according to the modulus and phase respectively. When the time-varying characteristics of the channel are not significant, the modulus and phase of the frequency-domain channel responses estimated at different times can be directly added and averaged to improve the accuracy of channel estimation. The method proposed by the invention not only greatly simplifies the complexity of channel estimation, but also has higher estimation precision.

The technical scheme of the channel estimation method of the present invention is as follows:

a) The sending end sends the training sequence T(n) intermittently. The frequency domain expression of the training sequence at the sending end is Tf(n)

b) The receiving end performs FFT (Fast-Fourier-Transformation, Fast Fourier Transformation) or DFT (Discrete Fourier Transform, Discrete Fourier Transform) on the received time domain training sequence Tr _t (n) to obtain the receiving end frequency domain Training sequence Trf _t (n).

c) Divide the frequency-domain training sequence _Trft (n) at the receiving end by Tf(n) to obtain the frequency-domain sequence _Hft (n) after the modulation information has been removed.

d) Take the modulus of _Hft (n) to get R _t (n), which is the modulus of the channel response in the frequency domain; take the phase of _Hft (n) to get θ _t (n), which is the phase of the channel response in the frequency domain.

(n)，即不同时刻的训练序列联合估计的频域信道响应的相位。这里的平均，是标量的加权平均，也可以是指数滤波、移动平均以及其他平均方法。指数滤波的系数和移动平均的长度可以调节。e) Average R _t1 (n), R _t2 (n)...R _tk (n) at different times to obtain R _H (n), which is the modulus of the frequency domain channel response jointly estimated by training sequences at different times; θ _t1 (n), θ _t2 (n)... θ _tk (n) are averaged to get

(n), that is, the phase of the frequency-domain channel response jointly estimated by training sequences at different times. The average here is a scalar weighted average, and it can also be an exponential filter, a moving average, or other average methods. The coefficient of exponential filter and the length of moving average can be adjusted.

Advantage and technical effect of the present invention are:

1. The channel estimation results at different times are jointly used, and the channel estimation accuracy is high

2. In the channel estimation process, the modulus of the channel response in the frequency domain and the phase of the channel are scalar weighted averages, which is simple to implement.

3. The averaging module (the coefficient of the exponential filter or the length of the moving average) can be adjusted according to actual needs, and a compromise can be made between the speed of tracking the channel changing with time and the accuracy of the channel estimation result.

Description of drawings

FIG. 1 is a schematic diagram of sending a distributed channel estimation training sequence proposed by the present invention;

Fig. 2 is a flow chart of joint channel estimation of training sequences at different times proposed by the present invention.

Detailed ways

The distributed channel estimation method applicable to channel conditions with insignificant time-varying characteristics described in the present invention will be described in detail below in conjunction with the accompanying drawings, but this does not constitute a limitation to the present invention.

As shown in FIG. 1 , the present invention proposes that at the sending end, the training sequence T(n) is sent intermittently.

Fig. 2 describes a method for jointly estimating a channel from sequences at different times. The specific process is:

1--The sender sends the training sequence T(n) intermittently. The frequency domain expression of the training sequence at the sending end is Tf(n)

2—The receiving end performs FFT (Fast-Fourier-Transformation, Fast Fourier Transformation) or DFT on the received time-domain training sequence Tr _t (n) to obtain the frequency-domain training sequence _Trft (n).

3—Divide the frequency-domain training sequence _Trft (n) at the receiving end by Tf(n) to obtain the frequency-domain sequence _Hft (n) after the modulation information has been removed.

4--Take the modulus of _Hft (n) to get R _t (n), which is the modulus of the channel response in the frequency domain; take the phase of _Hft (n) to get θ _t (n), the phase of the channel response in the frequency domain.

(n)，即不同时刻的训练序列联合估计的频域信道响应的相位。这里的平均，是标量的加权平均，可以是指数滤波、移动平均以及其他平均方法。这里不同时刻的估计结果做平均的前提是，不同时刻的范围在信道变化不显著的范围内。5--Average R _t1 (n), R _t2 (n)...R _tk (n) at different times to obtain R _H (n), that is, the modulus of the frequency domain channel response jointly estimated by training sequences at different times; θ _t1 (n), θ _t2 (n)...θ _tk (n) at different moments are averaged to obtain

(n), that is, the phase of the frequency-domain channel response jointly estimated by training sequences at different times. The average here is a scalar weighted average, which can be exponential filtering, moving average and other averaging methods. Here, the premise of averaging the estimation results at different time points is that the range at different time points is within the range where channel changes are not significant.

A specific embodiment is listed below to illustrate the distributed channel estimation method proposed by the present invention. This embodiment is described by jointly estimating the channel with training sequences at four time points.

The sending end sends the training sequence T(n) intermittently. The frequency domain expression of the training sequence at the sending end is Tf(n). Suppose the training sequence received at time _t1 is Tr _t1 (n), the training sequence received at time _t2 is Tr _t2 (n), the training sequence received at time _t3 is Tr _t3 (n), and the training sequence received at time _t4 is Tr t3 (n). The obtained training sequence is Tr _t4 (n).

Perform FFT on the received sequences at four times respectively to obtain Trf _t1 (n), Trf _t2 (n), Trf _t3 (n), and Trf _t4 (n).

Divide Trf _t1 (n), Trf _t2 (n), Trf _t3 (n), and Trf _t4 (n) by Tf (n) respectively to obtain the sequence _Hft1 (n), _Hft2 (n) after removing the modulation information , Hf _t3 (n), Hf _t4 (n).

Suppose the averaging module takes a moving average of length four.

Then the modulus of the jointly estimated frequency-domain channel response is:

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}{\mathrm{<span\; class="notranslate">\; 4</span>}}$

The phase of the jointly estimated frequency-domain channel response is:

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; arg</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; arg</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; arg</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; arg</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 4</span>}}$

Although the specific implementation methods and drawings of the present invention are disclosed for the purpose of illustration, the purpose is to help understand the content of the present invention and implement it accordingly, but those skilled in the art can understand that: without departing from the present invention and the appended claims Various substitutions, changes and modifications are possible within the spirit and scope of . The present invention should not be limited to the contents disclosed in the specification and the accompanying drawings, and the protection scope of the present invention is subject to the scope defined in the claims.

Claims (10)

1. A distributed channel estimation method, the steps of which are: 1) The sending end sends the training sequence intermittently; 2) The receiving end converts the received time-domain training sequence into a frequency-domain training sequence; 3) removing the modulation information of the frequency domain training sequence; 4) Jointly estimating the modulus of the frequency-domain training sequence after removing the modulation information to obtain the modulus of the frequency-domain channel response; performing joint estimation on the phase of the frequency-domain training sequence after removing the modulation information to obtain the phase of the frequency-domain channel response. 2. The method according to claim 1, characterized in that the training sequence in the time domain is converted into a training sequence in the frequency domain using a fast Fourier transform method. 3. The method according to claim 1, characterized in that the training sequence in the time domain is converted into the training sequence in the frequency domain using a discrete Fourier transform method. 4. The method according to claim 1, 2 or 3, wherein the method for removing the modulation information of the frequency-domain training sequence is: dividing the frequency-domain training sequence of the receiving end by the frequency-domain expression of the sending training sequence. 5. The method according to claim 1, wherein the modulus calculation method of the frequency-domain channel response is: for the frame received at the current moment, at first get the modulus of the frequency-domain training sequence after the modulation information is removed as the moment The modulus of the channel response in the frequency domain; then jointly estimate the modulus of the channel response in the frequency domain obtained from frames received at different times to obtain the modulus of the channel response in the frequency domain. 6. The method according to claim 1, wherein the phase calculation method of the frequency-domain channel response is: for the frame received at the current moment, at first get the phase of the frequency-domain training sequence after the modulation information is removed as the moment The phase of the channel response in the frequency domain; then jointly estimate the phase of the channel response in the frequency domain obtained from frames received at different times to obtain the phase of the channel response in the frequency domain. 7. The method according to claim 1, 5 or 6, characterized in that the joint estimation is a direct scalar average or a scalar weighted average. 8. The method according to claim 1, 5 or 6, characterized in that said joint estimation is moving average or exponential filtering. 9. The method according to claim 8, characterized in that the length of the moving average is adjustable. 10. The method according to claim 8, characterized in that coefficients of the exponential filter are adjustable.

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