WO2020135875A1 - 一种基于维纳自适应的信道估计的方法及*** - Google Patents

一种基于维纳自适应的信道估计的方法及*** Download PDF

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WO2020135875A1
WO2020135875A1 PCT/CN2019/129972 CN2019129972W WO2020135875A1 WO 2020135875 A1 WO2020135875 A1 WO 2020135875A1 CN 2019129972 W CN2019129972 W CN 2019129972W WO 2020135875 A1 WO2020135875 A1 WO 2020135875A1
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frequency domain
wiener
channel estimation
calculate
domain response
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French (fr)
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张浩源
刘源
艾星星
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/022—Channel estimation of frequency response
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods

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  • the present invention requires the priority of the Chinese patent application filed on December 29, 2018 in the Chinese Patent Office with the application number 201811645934.5 and the invention titled “a method and system based on Wiener adaptive channel estimation.” The entire contents are incorporated by reference in the present invention.
  • the invention relates to the technical field of reference signal filtering in a communication network, and in particular to a channel estimation method and system based on Wiener adaptation.
  • the channel estimation methods in Orthogonal Frequency Division Multiplexing (OFDM) system are mainly divided into three categories: 1) channel estimation methods based on reference signals; 2) blind estimation methods; 3) semi-blind estimation methods.
  • blind estimation and semi-blind estimation methods are limited by their estimation accuracy and computational complexity, which makes the usage rate in practical applications low, while the estimation accuracy of channel estimation methods based on reference signals is better than blind estimation and semi-blind estimation.
  • the estimation accuracy is high, so it is more widely used in each physical channel of the OFDM system.
  • the reference signal-based channel estimation method aims to estimate the frequency domain response of the equivalent channel at the time-frequency position with the reference signal, and then uses the interpolation algorithm to estimate the frequency domain response of the non-reference signal time-frequency position.
  • a channel estimation method and system with low computational complexity and high accuracy are lacking.
  • the objective of the embodiments of the present invention is to provide a Wiener (Wiener) adaptive filtering method and system to solve the technical problem of lacking a channel estimation method and system with low computational complexity and high accuracy in the prior art .
  • the embodiments of the present invention are implemented as follows:
  • the embodiments of the present invention provide a Wiener adaptive channel estimation method, including: calculating the frequency of the target channel according to the subcarriers of the reference signal Domain response estimate; calculate the coefficients of the Wiener adaptive filter according to the frequency domain channel estimate; calculate the channel estimate of the target channel based on the frequency domain response estimate and the coefficient of the Wiener adaptive filter.
  • a Wiener adaptive channel estimation system includes: a frequency domain response estimation value calculation module for calculating a frequency domain response estimation value of a target channel according to subcarriers of a reference signal ; A filter coefficient calculation module for calculating the coefficients of the Wiener adaptive filter; a channel estimation module, calculating the channel estimation value of the target channel according to the frequency domain response estimate and the coefficients of the Wiener adaptive filter.
  • a network device includes a memory, a processor, and a computer program that can run on the memory and the processor, and the computer program is executed by the processor To implement the Wiener adaptive filtering method described in any of the above.
  • a computer-readable storage medium According to a fourth aspect, a computer-readable storage medium according to an embodiment of the present invention, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the Wiener adaptive filtering method.
  • FIG. 1 is a flowchart of a Wiener adaptive channel estimation method according to an embodiment of the present invention
  • FIG. 2 is a flowchart of another channel estimation method based on Wiener adaptation in an embodiment of the present invention
  • FIG. 3 is a block diagram of a Wiener adaptive channel estimation system according to an embodiment of the present invention.
  • FIG. 4 is a block diagram of a network device according to an embodiment of the present invention.
  • An embodiment of the present invention discloses a Wiener adaptive filtering method, which can be applied to an OFDM system.
  • the method includes at least the following steps.
  • Step S102 Calculate the frequency domain response estimate value of the target channel according to the subcarrier of the reference signal; in the embodiment of the present invention, the frequency domain response before filtering can be obtained based on the least squares (Least Square, LS) criterion, which can be achieved The pilot sequence mother code is removed to obtain an unfiltered frequency domain response estimate based on the LS criterion.
  • LS least squares
  • Step S104 Calculate the coefficients of the Wiener adaptive filter according to the frequency domain channel estimation value.
  • the coefficients of the Wiener adaptive filter may be calculated based on the frequency domain channel estimation value determined in step S102.
  • Step S106 Calculate the channel estimation value of the target channel according to the frequency domain response estimation value and the coefficient of the Wiener adaptive filter.
  • the step S102 may include the following steps.
  • Step S1021 According to each subcarrier of the reference signal, calculate an estimated value of the frequency domain response corresponding to the subcarrier.
  • the corresponding frequency domain response estimation value H LS,k is calculated , and the specific method may be as follows:
  • Y k is the received signal.
  • Step S1023 Determine the frequency domain response estimate value of the target channel according to the frequency domain response estimate value corresponding to each subcarrier.
  • the frequency domain response estimates H LS,k corresponding to each subcarrier are combined to obtain the frequency domain response of the target channel estimated value.
  • the frequency domain response estimate value of the target channel is the frequency domain response estimate value of the target channel in the full frequency band.
  • the frequency domain response estimate value of the target channel in the full frequency band may be:
  • H LS, BWP represents the frequency domain response estimate value of the target channel in the entire frequency band
  • T represents transpose
  • [;] represents the branch in the matrix
  • H LS and BWP are channel estimation values under the unfiltered LS criterion.
  • the step S104 may include the following steps.
  • Step S1041 Calculate the tap input vector u according to the frequency domain response estimate value of the target channel, and also generate a tap input matrix u 0 according to the tap input vector u.
  • the frequency domain response estimate value H LS, BWP, p of the antenna p in the full bandwidth may be first extracted, then H LS, BWP, p may be:
  • the specific method can be:
  • the specific method may be:
  • Step S1043 Determine the correlation matrix of the tap input vector according to the tap input vector u
  • the correlation matrix of the tap input vector u is calculated.
  • (.) H represents the conjugate transpose.
  • the normalized tap input vector u can be used to calculate the correlation matrix
  • norm(.) is a 2-norm.
  • Step S1045 Inverse the correlation matrix R to obtain an inverse matrix.
  • the correlation matrix R is inverted to obtain the inverse matrix of the correlation matrix R.
  • the correlation matrix R needs to be diagonally loaded first, and then inversely applied to it.
  • invR is the result of diagonal loading and then inversion
  • ⁇ is the diagonal loading factor
  • (i,i) represents the diagonal element
  • (.) -1 is matrix inversion.
  • the size of the diagonal loading factor can be adaptively configured.
  • the diagonal loading factor ⁇ can be adaptively adjusted according to the noise power.
  • ⁇ can be 0.1, and the larger the value of ⁇ , the greater the strength of the noise null. .
  • the intensity of the noise trapping in the process of diagonally loading the covariance matrix of the input tap vector, can be controlled by adjusting the size of the diagonal loading factor.
  • Step S1047 Calculate the expected response d according to the tap input vector u, and calculate the cross-correlation vector of the expected response d and the tap input vector u
  • the tap input vector is calculated After that, the unfiltered channel response estimate under the LS criterion can be used As the expected response, the channel estimate of the frequency domain channel response can also be filtered and denoised As the expected response d, it is not specifically limited here.
  • the frequency domain response estimation values H LS and BWP of the target channel can be filtered in the time domain (or frequency domain) to achieve the denoising effect.
  • the time domain channel filtering method may be as follows.
  • W order is set to odd order, but it is not limited to that W order can only be odd order, and when W order is even order, p can be:
  • the order of the filter can be flexibly configured, and a compromise between availability and reduced overhead can be achieved.
  • Step S1049 According to the expected response d and the cross-correlation vector of the tap input vector u And inverse matrix to calculate the adaptive filter coefficients of the p-th antenna
  • the correlation matrix can be used Cross-correlation vector of invR, input tap vector and expected response after diagonal loading Calculate the adaptive filter coefficient of the p-th antenna, Specifically:
  • the adaptive filter coefficient of the p-th antenna can be obtained
  • the correlation matrix of N ante antennas may be calculated And cross-correlation vectors In this way, all antennas can share the same set of adaptive filter coefficients, and a statistical correlation matrix can be obtained And cross-correlation vector At the same time reduce the computational overhead and complexity.
  • the step S106 includes the following steps.
  • Step S1061 The frequency domain response estimated value and the coefficient of the Wiener adaptive filter are convoluted in the frequency domain to filter the frequency domain response estimated value.
  • an adaptive filter w AF, p is used to respond to its frequency domain After filtering, the frequency response H AF, BWP, p after filtering is obtained.
  • Step S1063 Remove the zero-filling term of the convolution to obtain the channel estimation value after adaptive filtering based on the Wiener frequency domain
  • (a:b) means taking the vector elements from index a to b.
  • a time-domain filter can be placed in front of the adaptive filter for preliminary denoising.
  • the advantage of this setting is that the denoised result of the time-domain filter is used as Wiener adaptation
  • the input of the filter can estimate the correlation matrix more accurately
  • cross-correlation vector Can also estimate the expected response vector more accurately
  • the channel estimation method based on Wiener adaptation provided by the embodiment of the present invention adopts a scheme based on Wiener adaptation, which can effectively achieve the technical effect of low computational complexity and high estimation accuracy, effectively overcoming the existing channel estimation The technical problems of high computational complexity or low channel estimation accuracy.
  • an embodiment of the invention uses a system of channel estimation based on Wiener adaptation, as shown in FIG. 3, the illustrated system includes: frequency domain response estimation
  • the value calculation module 32 is used to calculate the frequency domain response estimate value of the target channel according to the subcarrier of the reference signal, and the filter coefficient calculation module 34 is used to calculate the coefficient of the Wiener adaptive filter;
  • the channel estimation module 36 is used to calculate the The frequency domain response estimated value and the coefficient of the Wiener adaptive filter calculate the channel estimated value of the target channel.
  • the frequency domain response estimated value calculation module includes: a first calculation unit for calculating the frequency domain response estimated value corresponding to the subcarriers according to each subcarrier of the reference signal; a second calculation unit , Used to determine the frequency domain response estimate of the target channel according to the frequency domain response estimate corresponding to each subcarrier.
  • the filter coefficient calculation module includes: a tap input matrix generation unit for calculating a tap input vector u according to the frequency domain response estimate value of the target channel, and generating a tap input according to the tap input vector u Matrix u 0 ; correlation matrix determination unit, used to determine the correlation matrix R of the tap input matrix according to the tap input matrix u 0 ; inverse matrix determination unit, used to inverse the correlation matrix R to obtain an inverse matrix; A cross-correlation vector calculation unit for calculating an expected response d based on the tap input vector u, and calculating a cross-correlation vector p for the expected response d and the tap input vector u; an adaptive filter coefficient calculation unit for Calculate the adaptive filter coefficient of the p-th antenna according to the expected response d and the cross-correlation vector p of the tap input vector u, and the inverse matrix
  • the filter coefficient calculation module includes a diagonal loading unit for diagonally loading the correlation matrix R.
  • the channel estimation module includes: a convolution unit for convolution of the frequency domain response estimate value and the coefficient of the Wiener adaptive filter in the frequency domain; a channel estimation unit, It is used to remove the zero-filling term of convolution and obtain the channel estimation value based on Wiener frequency domain adaptation.
  • the channel estimation system based on Wiener adaptation provided by the embodiment of the present invention adopts a scheme based on Wiener adaptation, which can effectively achieve the technical effect of low calculation complexity and high estimation accuracy.
  • the embodiments of the present invention provide a network device.
  • the network device includes a processor 410, a transceiver 420, and a memory 430 And bus interface.
  • the network device 400 further includes: a computer program stored on the memory 430 and executable on the processor 410, and when the computer program is executed by the processor 410, the above-mentioned FIG. 1 or FIG.
  • a computer program stored on the memory 430 and executable on the processor 410, and when the computer program is executed by the processor 410, the above-mentioned FIG. 1 or FIG.
  • Each process in the method shown in 2 can achieve the same technical effect, and to avoid repetition, it will not be repeated here.
  • the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by the processor 410 and various circuits of the memory represented by the memory 430 are linked together.
  • the bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and therefore, they will not be further described in this article.
  • the bus interface provides an interface.
  • the transceiver 420 may be a plurality of elements, including a transmitter and a receiver, and provides a unit for communicating with various other devices on a transmission medium.
  • the processor 410 is responsible for managing the bus architecture and general processing, and the memory 430 may store data used by the processor 410 in performing operations.
  • Embodiments of the present invention also provide a computer-readable storage medium, which stores a computer program on the computer-readable storage medium.
  • the computer program is executed by a processor, the processes of the foregoing method embodiments are implemented, and the same technical effect can be achieved. To avoid repetition, I will not repeat them here.
  • the computer-readable storage medium such as read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
  • the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation.
  • the technical solution of the present invention can be embodied in the form of a software product in essence or part that contributes to the existing technology, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk,
  • the CD-ROM includes several instructions to enable a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the embodiments of the present invention.

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Abstract

本发明实施例提供一种基于维纳自适应的信道估计的方法及***，其中，所述方法，包括：根据参考信号的子载波计算目标信道的频域响应估计值；根据所述频域信道估计值计算Wiener自适应滤波器的系数；根据所述频域响应估计值、所述Wiener自适应滤波器的系数计算所述目标信道的信道估计值。

Description

一种基于维纳自适应的信道估计的方法及***
交叉引用
本发明要求在2018年12月29日提交中国专利局、申请号为201811645934.5、发明名称为“一种基于维纳自适应的信道估计的方法及***”的中国专利申请的优先权，该申请的全部内容通过引用结合在本发明中。
技术领域
本发明涉及通信网络中参考信号的滤波技术领域，尤其涉及一种基于维纳自适应的信道估计的方法及***。
背景技术
正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)***中的信道估计方法主要分为三类：1)基于参考信号的信道估计方法；2)盲估计方法；3)半盲估计方法。总体来说，盲估计和半盲估计方法受限于其估计精度和计算复杂度，使得在实际应用中的使用率较低，而基于参考信号的信道估计方法的估计精度较盲估计、半盲估计的精度高，因此被更加广泛应用在OFDM***各个物理信道中。
基于参考信号的信道估计方法旨在估计具有参考信号的时频位置处等效信道的频域响应，进而使用插值算法估计非参考信号时频位置的频域响应。但由于多径信道的时变特性，以及白噪声和干扰的影响，在一些情形下缺少一种计算复杂度低、且精度高的信道估计方法及***。
发明内容
本发明实施例的目的是提供一种基于维纳(Wiener)自适应的滤波方法及***，以解决现有技术中缺少一种计算复杂度低、且精度高的信道估计方法及***的技术问题。
为解决上述技术问题，本发明实施例是这样实现的：第一方面，本发明实施例提供一种基于维纳自适应的信道估计的方法，包括：根据参考信号的子载波计算目标信道的频域响应估计值；根据所述频域信道估计值计算Wiener自适应滤波器的系数；根据所述频域响应估计值、所述Wiener自适应滤波器的系数计算所述目标信道的信道估计值。
第二方面，根据本发明实施例提供的一种基于Wiener自适应的信道估计的***，包括：频域响应估计值计算模块，用于根据参考信号的子载波计算目标信道的频域响应估计值；滤波系数计算模块，用于计算Wiener自适应滤波器的系数；信道估计模块，根据所述频域响应估计值、所述Wiener自适应滤波器的系数计算所述目标信道的信道估计值。
第三方面，根据本发明实施例提供的一种网络设备，包括存储器、处理器及在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被所述处理器执行时实现上述任一项所述的基于维纳自适应的滤波方法。
第四方面，根据本发明实施例提供的一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现上述任一项所述的基于维纳自适应的滤波方法。
附图说明
此处所说明的附图用来提供对本发明的进一步理解，构成本发明的一部分，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：
图1为本发明实施例中一种基于维纳自适应的信道估计的方法的流程图；
图2为本发明实施例中再一种基于维纳自适应的信道估计的方法的流程图；
图3为本发明实施例中一种基于维纳自适应的信道估计的***的模块图；
图4为本发明实施例中一种网络设备的模块图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明实施例公开一种基于维纳自适应的滤波方法，可应用于OFDM***中，参见图1所示，所述方法至少包括以下步骤。
步骤S102、根据参考信号的子载波计算目标信道的频域响应估计值；在本发明实施例中，可首先基于最小二乘(Least Square,LS)准则，得到滤波前的频域响应，可以达到去除导频序列母码，进而获得未滤波的、基于LS准则下的频域响应估计值。
步骤S104、根据所述频域信道估计值计算Wiener自适应滤波器的系数；在本发明实施例中，可基于步骤S102中确定的频域信道估计值计算维纳自适应滤波器的系数。
步骤S106、根据所述频域响应估计值、所述Wiener自适应滤波器的系数计算所述目标信道的信道估计值。
在本发明实施例中，所述步骤S102，可包括以下步骤。
步骤S1021、根据参考信号的每个子载波，计算所述子载波对应的频域响应估计值。
在本发明实施例中，对于每个子载波k，计算其对应的频域响应估计值H LS,k，具体方法可如下：
Figure PCTCN2019129972-appb-000001
其中，子载波k＝1,2,…,N ante，N ante为接收天线数目，
Figure PCTCN2019129972-appb-000002
为导频序列，Y k为接收信号。
步骤S1023、根据每个子载波对应的频域响应估计值，确定目标信道的 频域响应估计值。
在本发明实施例中，在确定每个子载波对应的频域响应估计值H LS,k之后，将每个子载波对应的频域响应估计值H LS,k进行合并，得到目标信道的频域响应估计值。所述目标信道的频域响应估计值是全频带内的目标信道的频域响应估计值，具体地，全频带内的目标信道的频域响应估计值可为：
Figure PCTCN2019129972-appb-000003
其中，H LS,BWP表示全频带内的目标信道的频域响应估计值，(.) T表示转置，[；]表示矩阵内分行。
在本发明实施例中，H LS,BWP为未滤波的LS准则下的信道估计值。
在本发明实施例中，所述步骤S104，可包括以下步骤。
步骤S1041、根据目标信道的频域响应估计值，计算抽头输入向量u，还可根据所述抽头输入向量u生成抽头输入矩阵u 0。
在本发明实施例中，可首先抽取天线p在全带宽内的频域响应估计值H LS,BWP,p，则H LS,BWP,p可为：
Figure PCTCN2019129972-appb-000004
其中，
Figure PCTCN2019129972-appb-000005
为第p个天线在全带宽内的基于LS准则下的信道估计值，(:,p)表示所有行第p列。
然后，根据所述天线p在全带宽内的频域响应估计值，计算抽头输入向量，
Figure PCTCN2019129972-appb-000006
具体方法可为：
Figure PCTCN2019129972-appb-000007
其中，(n)表示向量H LS,BWP,p的第n个元素，n＝1,2,…,K pilot+W order-1。
再将从n＝1对应的抽头输入向量至n＝K pilot+W order-1对应的抽头输入向量 进行合并，得到抽头输入矩阵u 0，具体方法可为：
Figure PCTCN2019129972-appb-000008
其中
Figure PCTCN2019129972-appb-000009
表示对应导频索引n的输入向量，[,]表示矩阵分列。
步骤S1043、根据所述抽头输入向量u确定所述抽头输入向量的相关矩阵
Figure PCTCN2019129972-appb-000010
在本发明实施例中，在计算出抽头输入向量u之后，计算抽头输入向量u的相关矩阵
Figure PCTCN2019129972-appb-000011
具体方法可为：
Figure PCTCN2019129972-appb-000012
其中，(.) H表示共轭转置。
在此指出，本发明实施例中，可以使用归一化的抽头输入向量u计算相关矩阵
Figure PCTCN2019129972-appb-000013
也可使用归一化的u/norm(u)来计算相关矩阵
Figure PCTCN2019129972-appb-000014
以减小频选影响，其中norm(.)为2范数。
步骤S1045、对所述相关矩阵R求逆，得到逆矩阵。
在本发明实施例中，在确定出相关矩阵R之后，对相关矩阵R求逆，以求取相关矩阵R的逆矩阵。
作为一种具体的实施方式，为了提高Wiener自适应滤波器对噪声的零陷强度，在计算相关矩阵R的逆矩阵之前，首先需要对相关矩阵R对角加载，然后再对其进行求逆，得到：
Figure PCTCN2019129972-appb-000015
其中，invR为对角加载再求逆后的结果，α为对角加载因子，(i,i)表示对角元素，
Figure PCTCN2019129972-appb-000016
为单位阵，(.) -1为矩阵求逆。
本发明实施例，可以自适应配置对角加载因子大小，如可以根据噪声功率大小自适应调整对角加载因子α，如α可为0.1，而α值越大，表征对噪声 零陷强度越大。
在本发明实施例中，在对输入抽头向量的协方差矩阵对角加载的过程中，可通过调整对角加载因子的大小来控制对噪声零陷的强度。
步骤S1047、根据所述抽头输入向量u计算期望响应d,并计算所述期望响应d及所述抽头输入向量u的互相关向量
Figure PCTCN2019129972-appb-000017
在本发明实施例中，在计算出抽头输入向量
Figure PCTCN2019129972-appb-000018
之后，可以采用LS准则下的未滤波的信道响应估计值
Figure PCTCN2019129972-appb-000019
作为期望响应，也可以对频域信道响应滤波去噪后的信道估计值
Figure PCTCN2019129972-appb-000020
作为期望响应d，在此对其不做具体限定。
在此指出，在本发明实施例中，可以对目标信道的频域响应估计值H LS,BWP进行时域(或频域)滤波以达到去噪效果。具体的，时域信道滤波方法可如下所述。
1)对于每天线i，将第i＝{1,...,N ante}个天线的基于最小二乘积的信道估计值
Figure PCTCN2019129972-appb-000021
通过反快速傅里叶变换(Inverse Fast Fourier Transfor,IFFT)变换到时域，得到第i个天线的时域冲击响应估计值h LS,i；
2)以第i天线的时域冲击响应估计值h LS,i的时域最大抽头(最大抽头表示多径中的主径分量)为中心，取预设窗口大小(例如取循环前缀长度的窗口)作为滤波后的时域信道估计值
Figure PCTCN2019129972-appb-000022
3)将
Figure PCTCN2019129972-appb-000023
通过快速傅里叶变换(Fast Fourier Transfor,FFT)变换到频域，得到滤波后的频域响应
Figure PCTCN2019129972-appb-000024
进而可将最小二乘积准则下的第i个天线的频域响应的信道估计值
Figure PCTCN2019129972-appb-000025
变换为第i个天线的滤波后的时域信道估计值
Figure PCTCN2019129972-appb-000026
其次，计算所述期望响应d及所述抽头输入向量u的互相关向量
Figure PCTCN2019129972-appb-000027
具体可为：
以采用
Figure PCTCN2019129972-appb-000028
作为滤波器期望响应d为例进行阐述：即 d(n)＝H LS,BWP,p(n)，n＝{1,...,K pilot}，那么对于全带宽，期望响应向量为
Figure PCTCN2019129972-appb-000029
由于抽头输入矩阵
Figure PCTCN2019129972-appb-000030
的列长度大于滤波器期望响应
Figure PCTCN2019129972-appb-000031
去除抽头输入向量的补零项，得到
Figure PCTCN2019129972-appb-000032
其中n＝1,...,K pilot。这里为了便于描述，W order设为奇数阶，但并非是限定W order只能是奇次阶，而当W order为偶次阶时，p可为：
Figure PCTCN2019129972-appb-000033
本发明实施例中，滤波器的阶数可灵活配置，能实现可获得性与降低开销的折中方案。
步骤S1049、根据所述期望响应d及所述抽头输入向量u的互相关向量
Figure PCTCN2019129972-appb-000034
及逆矩阵计算第p个天线的自适应滤波器系数
Figure PCTCN2019129972-appb-000035
在本发明实施例中，可根据相关矩阵
Figure PCTCN2019129972-appb-000036
对角加载求逆后的invR、输入抽头向量与期望响应的互相关向量
Figure PCTCN2019129972-appb-000037
计算第p个天线的自适应滤波系数，
Figure PCTCN2019129972-appb-000038
具体为：
将相关矩阵
Figure PCTCN2019129972-appb-000039
对角加载求逆后的invR、输入抽头向量与期望响应的互相关向量
Figure PCTCN2019129972-appb-000040
代入如下公式：
Figure PCTCN2019129972-appb-000041
从而可得到第p个天线的自适应滤波系数
Figure PCTCN2019129972-appb-000042
在此指出，上述实施例是针对每个天线p＝1,2,…,N ante分别操作，计算该天线的自适应滤波系数。
作为另一种具体实施方式，在自适应滤波阶数不高于预设值时，可计算N ante个天线的相关矩阵
Figure PCTCN2019129972-appb-000043
以及互相关向量
Figure PCTCN2019129972-appb-000044
如此，所有天线可共用同一组自适应滤波器系数，更能得到统计意义上的相关矩阵
Figure PCTCN2019129972-appb-000045
和互相关向量
Figure PCTCN2019129972-appb-000046
同时降低了计算的开销和复杂度。
在本发明实施例中，所述步骤S106包括以下步骤。
步骤S1061、将所述频域响应估计值与所述Wiener自适应滤波器的系数在频域上做卷积，以对所述频域响应估计值进行滤波。
在本发明实施例中，对应每天线p，使用自适应滤波器w AF,p对其频域响应
Figure PCTCN2019129972-appb-000047
进行滤波，得到滤波后频响H AF,BWP,p，有
Figure PCTCN2019129972-appb-000048
其中，
Figure PCTCN2019129972-appb-000049
表示卷积结果，conv(.)表示两个向量的卷积。
步骤S1063、去除卷积补零项，得到基于Wiener频域自适应滤波后的信道估计值
Figure PCTCN2019129972-appb-000050
其中，(a:b)表示取向量元素从索引a到b。
在本发明一个具体实施例中，可在自适应滤波器之前，可前置一个时域滤波器用以初步去噪，如此设置的优点是：将时域滤波器去噪后的结果作为Wiener自适应滤波器的输入，可以更加准确地估计所述相关矩阵
Figure PCTCN2019129972-appb-000051
和互相关向量
Figure PCTCN2019129972-appb-000052
也可以更加准确地估计期望响应向量
Figure PCTCN2019129972-appb-000053
工程上为了减少计算开销，也灵活选择是否使用前置时域滤波器。
本发明实施例提供的基于维纳自适应的信道估计方法，采用基于维纳自适应的方案，可有效实现计算复杂度低，且估值精度高的技术效果，有效克服了现有的信道估计方法计算复杂度高或者信道估计精度低的技术问题。
基于上述实施例提供的基于维纳自适应的信道估计的方法，办发明实施例通过一种基于维纳自适应的信道估计的***，参见图3所示，所示***包括：频域响应估计值计算模块32，用于根据参考信号的子载波计算目标信道的频域响应估计值，及滤波系数计算模块34，用于计算Wiener自适应滤波器的系数；信道估计模块36，用于根据所述频域响应估计值、所述Wiener 自适应滤波器的系数计算所述目标信道的信道估计值。
在一个实施例中，所述频域响应估计值计算模块，包括：第一计算单元，用于根据参考信号的每个子载波，计算所述子载波对应的频域响应估计值；第二计算单元，用于根据每个子载波对应的频域响应估计值，确定目标信道的频域响应估计值。
在一个实施例中，所述滤波系数计算模块，包括：抽头输入矩阵生成单元，用于根据目标信道的频域响应估计值，计算抽头输入向量u，并根据所述抽头输入向量u生成抽头输入矩阵u 0；相关矩阵确定单元，用于根据所述抽头输入矩阵u 0确定所述抽头输入矩阵的相关矩阵R；逆矩阵确定单元，用于对所述相关矩阵R求逆，得到逆矩阵；互相关向量计算单元，用于根据所述抽头输入向量u计算期望响应d,并计算所述期望响应d及所述抽头输入向量u的互相关向量p；自适应滤波器系数计算单元，用于根据所述期望响应d及所述抽头输入向量u的互相关向量p、及所述逆矩阵计算第p个天线的自适应滤波器系数
Figure PCTCN2019129972-appb-000054
在一个实施例中，所述滤波系数计算模块，包括：对角加载单元，用于对所述相关矩阵R进行对角加载。
在一个实施例中，所述信道估计模块，包括：卷积单元，用于将所述频域响应估计值与所述Wiener自适应滤波器的系数在频域上做卷积；信道估计单元，用于去除卷积补零项，得到基于Wiener频域自适应的信道估计值。
本发明实施例提供的基于维纳自适应的信道估计***，采用基于维纳自适应的方案，可有效实现计算复杂度低，且估值精度高的技术效果。
相应于本发明实施例提供的基于维纳自适应的信道估计的方法、***，本发明实施例提供一种网络设备，参见图4所示，网络设备包括处理器410、收发机420、存储器430和总线接口。
在本发明实施例中，网络设备400还包括：存储在存储器430上并可在 所述处理器410上运行的计算机程序，所述计算机程序被所述处理器410执行时实现上述图1或图2所示的方法中的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
在图4中，总线架构可以包括任意数量的互联的总线和桥，具体由处理器410代表的一个或多个处理器和存储器430代表的存储器的各种电路链接在一起。总线架构还可以将诸如***设备、稳压器和功率管理电路等之类的各种其他电路链接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口提供接口。收发机420可以是多个元件，即包括发送机和接收机，提供用于在传输介质上与各种其他装置通信的单元。
处理器410负责管理总线架构和通常的处理，存储器430可以存储处理器410在执行操作时所使用的数据。
本发明实施例还提供一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，该计算机程序被处理器执行时实现上述方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。其中，所述的计算机可读存储介质，如只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本发明的 技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本发明各个实施例所述的方法。
上面结合附图对本发明的实施例进行了描述，但是本发明并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本发明的启示下，在不脱离本发明宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本发明的保护之内。

Claims (12)

  1. 一种基于维纳自适应的信道估计的方法，其中，包括：
    根据参考信号的子载波计算目标信道的频域响应估计值；
    根据所述频域信道估计值计算Wiener自适应滤波器的系数；
    根据所述频域响应估计值、所述Wiener自适应滤波器的系数计算所述目标信道的信道估计值。
  2. 根据权利要求1所述的方法，其中，所述根据参考信号的子载波计算目标信号的频域响应估计值，包括：
    根据参考信号的每个子载波，计算所述子载波对应的频域响应估计值；
    根据每个子载波对应的频域响应估计值，确定目标信道的频域响应估计值。
  3. 根据权利要求1或2所述的方法，其中，所述根据所述频域信道估计值计算Wiener自适应滤波器的系数，包括：
    根据目标信道的频域响应估计值，计算抽头输入向量u；
    根据所述抽头输入向量u确定所述抽头输入向量的相关矩阵R；
    对所述相关矩阵R求逆，得到逆矩阵；
    根据所述抽头输入向量u计算期望响应d,并计算所述期望响应d及所述抽头输入向量u的互相关向量
    Figure PCTCN2019129972-appb-100001
    根据所述期望响应d及所述抽头输入向量u的互相关向量
    Figure PCTCN2019129972-appb-100002
    及所述逆矩阵计算第p个天线的自适应滤波器系数
    Figure PCTCN2019129972-appb-100003
  4. 根据权利要求3所述的方法，其中，在对所述相关矩阵R求逆之前，还包括：
    对所述相关矩阵R对角加载。
  5. 根据权利要求3所述的方法，其中，所述根据所述Wiener自适应滤波器的系数计算所述目标信道的信道估计值，包括：
    将所述频域响应估计值与所述Wiener自适应滤波器的系数在频域上做 卷积；
    去除卷积补零项，得到基于Wiener频域自适应的信道估计值。
  6. 一种基于维纳自适应的信道估计的***，其中，包括：
    频域响应估计值计算模块，用于根据参考信号的子载波计算目标信道的频域响应估计值，及
    滤波系数计算模块，用于计算Wiener自适应滤波器的系数；
    信道估计模块，用于根据所述频域响应估计值、所述Wiener自适应滤波器的系数计算所述目标信道的信道估计值。
  7. 根据权利要求6所述的***，其中，所述频域响应估计值计算模块，包括：
    第一计算单元，用于根据参考信号的每个子载波，计算所述子载波对应的频域响应估计值；
    第二计算单元，用于根据每个子载波对应的频域响应估计值，确定目标信道的频域响应估计值。
  8. 根据权利要求6或7所述的***，其中，所述滤波系数计算模块，包括：
    抽头输入矩阵生成单元，用于根据目标信道的频域响应估计值，计算抽头输入向量u；
    相关矩阵确定单元，用于根据所述抽头输入向量u确定所述抽头输入向量的相关矩阵R；
    逆矩阵确定单元，用于对所述相关矩阵R求逆，得到逆矩阵；
    互相关向量计算单元，用于根据所述抽头输入向量u计算期望响应d,并计算所述期望响应d及所述抽头输入向量u的互相关向量
    Figure PCTCN2019129972-appb-100004
    自适应滤波器系数计算单元，用于根据所述期望响应d及所述抽头输入向量u的互相关向量
    Figure PCTCN2019129972-appb-100005
    及所述逆矩阵计算第p个天线的自适应滤波器系数
    Figure PCTCN2019129972-appb-100006
  9. 根据权利要求8所述的***，其中，所述滤波系数计算模块，包括：
    对角加载单元，用于对所述相关矩阵R进行对角加载。
  10. 根据权利要求8所述的***，其中，所述信道估计模块，包括：
    卷积单元，用于将所述频域响应估计值与所述Wiener自适应滤波器的系数在频域上做卷积；
    信道估计单元，用于去除卷积补零项，得到基于Wiener频域自适应的信道估计值。
  11. 一种网络设备，其中，包括存储器、处理器及在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被所述处理器执行时实现权利要求1-5中任一项所述的基于维纳自适应的滤波方法。
  12. 一种计算机可读存储介质，其中，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-5中任一项所述的基于维纳自适应的滤波方法。
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