WO2018176334A1 - 一种信道估计方法及装置 - Google Patents

一种信道估计方法及装置 Download PDF

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Publication number
WO2018176334A1
WO2018176334A1 PCT/CN2017/078830 CN2017078830W WO2018176334A1 WO 2018176334 A1 WO2018176334 A1 WO 2018176334A1 CN 2017078830 W CN2017078830 W CN 2017078830W WO 2018176334 A1 WO2018176334 A1 WO 2018176334A1
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channel
matrix
received signal
receiving end
topplitz
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PCT/CN2017/078830
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English (en)
French (fr)
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田智
徐平
王悦
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华为技术有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • 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

Definitions

  • the present application relates to the field of communications technologies, and in particular, to a channel estimation method and apparatus.
  • Massive MIMO technology also known as large antenna array technology, refers to the use of large-scale antenna arrays for signal reception and transmission. In order to perform efficient communication using a MIMO system, it is necessary to estimate information state information of a channel of the MIMO system.
  • the massive MIMO channel exhibits sparse characteristics, and the number of effective propagation paths formed by spatial scatterers is much smaller than the number of antennas.
  • the traditional channel estimation methods are based on the assumption that the channel is dense multipath. It is implemented, so it cannot be used to efficiently and accurately obtain the information status information of the massive MIMO channel.
  • the massive MIMO channel can be estimated by a comprehensive sensing (CS) technique.
  • CS sensing
  • the spatial angle of AoD falls on a uniformly spaced grid, and then a virtual channel is established by two-dimensional space Fourier transform.
  • Virtual channel Vectorization After use Sparseness, recovered by a compressed sensing algorithm. In recovery Then obtain the virtual channel by inverse vectorization
  • the channel matrix H is then obtained from the inverse Fourier transform.
  • the above method is based on the assumption of the sparse grid characteristics of the channel in the angular domain, that is, all AoA and AoD are assumed to fall on a uniformly spaced grid, for example, assuming that the angular range of AoA and AoD is 1°-30°, the emission
  • the number of antennas and receiving antennas is 30.
  • the sparse grid is a 30*30 grid.
  • the rows represent the receiving antennas and the columns represent the transmitting antennas.
  • Each grid is 1°, each strip.
  • the path is for an AoD and an AoA. If one of the paths has an AoD of 12° and an angle of arrival of 15°, the path falls on the grid of the 15th row and the 12th column.
  • AoD and AoA are not necessarily integers.
  • a path corresponds to an AoD of 12.35° and an arrival angle of 15.48°
  • the corresponding point of the path is Will fall outside the grid
  • CS technology only assumes that points on the grid are meaningful, so points outside the grid are ignored, so that only channel based on points on the grid is used for channel estimation. The accuracy is lower.
  • Embodiments of the present invention provide a channel estimation method and apparatus, which solves the problem of low channel estimation accuracy in the prior art.
  • a first aspect provides a channel estimation method, which is applied to a receiving end, where the receiving end includes N receiving antennas, and N is a positive integer greater than or equal to 2.
  • the method includes: receiving, by the receiving end, the received signal Y by using the first channel, The received signal Y is obtained after the pilot signal X transmitted by the transmitting end is transmitted through the first channel, and the transmitting end includes M transmitting antennas, M is a positive integer greater than or equal to 2; the receiving end is based on the pilot signal X, the receiving signal Y and a two-layer Topplitz matrix T 2D (u), using the atomic norm minimization ANM algorithm to estimate the channel vector h of the first channel; wherein T 2D (u) comprises M*M Topplitz matrices, each The dimension of the Topplitz matrix is N*N, and T 2D (u) contains the leaving angular array information and the arrival angle array information.
  • the first atomic norm minimization AMN algorithm is used to estimate the first by using the two-layer Topplitz matrix T 2D (u), the pilot signal X and the received signal Y including the leaving angle array information and the arrival angle array information.
  • the channel vector h of the channel solves the problem that the actual angle of arrival and the angle of departure cannot be accurately estimated by mapping the angle of arrival and the angle of departure on the ideal sparse grid in the prior art, thereby improving the accuracy of the channel estimation. Degree and estimated efficiency.
  • the receiving end is based on the pilot signal X, the received signal Y, and the two-layer Topplitz matrix T 2D (u)
  • Estimating the channel vector h of the first channel using the atomic norm minimization ANM algorithm comprising: based on the pilot signal X, the received signal Y, and the two-layer Topplitz matrix T 2D (u), by using the following formula (1), Determining the channel vector h of the first channel; where t is a variable, trace is the sum of the eigenvalues of the matrix, h H is the conjugate transpose of h, y is the vectorized representation of the received signal Y, and X T is Demodulation of X, I is the unit matrix, Means Kronecker, Indicates semi-positive determination;
  • the channel vector of the first channel in the ideal situation without noise can be estimated, and the accuracy of the channel estimation in the ideal situation without noise is improved compared with the existing CS-based method. Estimate efficiency.
  • the receiving end is based on the pilot signal X, the received signal Y, and the two-layer Topplitz matrix T 2D (u)
  • Estimating the channel vector h of the first channel using the atomic norm minimization ANM algorithm comprising: based on the pilot signal X, the received signal Y, and the two-layer Topplitz matrix T 2D (u), by using the following formula (2), Determining the channel vector h of the first channel; where v is the weight associated with the noise variance, M, N, and channel multipath, t is the variable, and trace is the sum of the eigenvalues of the matrix,
  • the channel vector of the first channel in the presence of noise can be estimated according to the above formula (2), and the accuracy and estimation efficiency of the channel estimation in the case of noisy are improved compared with the existing CS-based method. .
  • the receiving end is based on the pilot signal X, the received signal Y, and the two layers.
  • the method further comprises: determining the channel matrix H of the first channel according to the channel vector h of the first channel .
  • the channel matrix H of the first channel may be determined according to the channel vector h of the first channel, so that the precoding design on the transmitter side and the detection design on the receiver side are performed based on the channel matrix H. Can improve the performance of MIMO systems.
  • the second aspect provides a receiving end device, where the receiving end device includes N receiving antennas, N is a positive integer greater than or equal to 2, and the receiving end device includes: an acquiring unit, configured to acquire a received signal Y, the receiving signal Y is obtained by transmitting the pilot signal X transmitted by the transmitting device through the first channel, and the transmitting device has M transmitting antennas; the estimating unit is configured to be based on the pilot signal X, the received signal Y and the two-layer Topplitz matrix.
  • T 2D (u) estimating the channel vector h of the first channel using an atomic norm minimization ANM algorithm; wherein T 2D (u) comprises M*M Topplitz matrices, and each of said Toplice matrices The dimension is N*N, and T 2D (u) contains the leaving angle array information and the arrival angle array information.
  • the estimating unit is specifically configured to: based on the pilot signal X, the received signal Y, and the two-layer Topplitz matrix T 2D (u), the channel vector h of the first channel is determined by the following formula (3); where t is a variable, trace is the sum of the eigenvalues of the matrix, h H is the conjugate transpose of h, and y is A vectorized representation of the received signal Y, X T is the transpose of the X, and I is an identity matrix, Means Kronecker, Indicates semi-positive determination;
  • the apparatus further includes: a determining unit, configured to use, according to the first channel The channel vector h determines the channel matrix H of the first channel.
  • a receiving end device comprising a processor and a memory, wherein the memory stores code and data, and the processor runs the code in the memory so that the receiving end device performs the first aspect to the third aspect of the first aspect
  • a channel estimation method provided by any of the possible implementations.
  • Yet another aspect of the present application provides a computer readable storage medium having instructions stored therein that, when executed on a computer, cause the computer to perform the methods described in the above aspects.
  • Yet another aspect of the present application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the methods described in the various aspects above.
  • the receiving end device, the computer storage medium or the computer program product of any of the channel estimation methods provided above are used to perform the corresponding method provided above, and therefore, the beneficial effects that can be achieved can be referred to The beneficial effects in the corresponding methods provided by the text are not described here.
  • Figure 1 is a schematic diagram of a sparse grid
  • FIG. 2 is a schematic structural diagram of a MIMO system according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of a channel estimation method according to an embodiment of the present invention.
  • FIG. 4 is a flowchart of another channel estimation method according to an embodiment of the present invention.
  • FIG. 5 is a schematic flowchart of channel estimation according to an embodiment of the present disclosure.
  • FIG. 6 is a schematic diagram of comparison of channel estimation results according to an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of comparison of another channel estimation result according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a receiving end device according to an embodiment of the present disclosure.
  • FIG. 9 is a schematic structural diagram of another receiving end device according to an embodiment of the present invention.
  • Multi-input multi-output (MIMO) technology in mobile communication refers to a technology that uses multiple transmit antennas and multiple receive antennas for wireless transmission.
  • a wireless communication system using this technology can In a so-called MIMO system, MIMO systems can utilize multiple antennas to increase spectral efficiency and can also be used to suppress channel fading.
  • FIG. 2 is a schematic structural diagram of a MIMO system according to an embodiment of the present invention.
  • the MIMO system includes a transmitter and a receiver, and may also be a transmitting end device and a receiving end device.
  • the transmitter may be a base station
  • the receiver may be a user equipment.
  • the transmitter may include M transmit antennas, and the receiver end may include N receive antennas, and M and N are positive integers greater than or equal to 2.
  • the signals may be transmitted and received through M transmit antennas and N antennas.
  • the angle of the scatterer changes during signal transmission.
  • a massive MIMO system refers to a MIMO system in which M and N are large values, that is, a MIMO system includes a large number of transmitting antennas and receiving antennas.
  • the MIMO system may be a communication system based on a uniform linear array, wherein the uniform linear array refers to the same distance between adjacent antennas, and the arrangement of the antennas is straight. If the effective transmission path of the communication system is L and the channel matrix is represented by H, then H is an N*M-order matrix and can be expressed by the following formula (I).
  • ⁇ is the average path loss
  • ⁇ l is the attenuation coefficient on the lth path
  • ⁇ r,l and ⁇ t,l represent the angle of arrival AoA and the exit angle AoD on the lth path , respectively
  • S is ⁇ And ⁇ l matrix
  • a r ( ⁇ r ) is the receiving array response matrix
  • a t ( ⁇ t ) is the transmitting array response matrix
  • specific ⁇ r, l and ⁇ t, l are as shown in FIG. 2 .
  • FIG. 3 is a flowchart of a channel estimation method according to an embodiment of the present invention, for estimating a channel of the MIMO system shown in FIG. 2, and applying to a receiving end, where the receiving end includes N receiving antennas, and N is greater than Or a positive integer equal to 2, see Figure 3, the method includes the following steps.
  • Step 201 The receiving end obtains the received signal Y, which is obtained by transmitting the pilot signal X transmitted by the transmitting end through the first channel, and the transmitting end includes M transmitting antennas, where M is a positive integer greater than or equal to 2. .
  • the pilot signal may also be referred to as a reference signal, and generally refers to a single frequency known signal transmitted by the communication system for monitoring, evaluation, control, equalization, or synchronization.
  • the pilot signal may include a plurality of signals.
  • the pilot signal when performing channel estimation, may be a channel state information reference signal (CSI-RS) or a sounding reference signal (SRS). ).
  • CSI-RS channel state information reference signal
  • SRS sounding reference signal
  • Step 202 The receiving end estimates the channel of the first channel by using an atomic norm minimization (ANM) algorithm based on the pilot signal X, the received signal Y, and the two-layer Toeplitz matrix T 2D (u).
  • ANM atomic norm minimization
  • the two-layer Topplitz matrix T 2D (u) comprises M*M Topplitz matrices, and the dimension of each Topplitz matrix is N*N
  • T 2D (u) contains the information of the exit angle array
  • the departure angle array information may be departure angle information when the M transmit antennas transmit the pilot signal X
  • the arrival angle array information may be the arrival angle information when the N receive antennas receive the received signal Y.
  • the leaving angle array information and the reaching angle array information may be information that is calculated or calculated by the receiving end within a preset time.
  • the specific manner of determining the angle of arrival array information and the information of the exit angle array is not limited herein, and may be determined by referring to the manner in the prior art.
  • the channel vector h is a form after the channel matrix H is vectorized. If the expression of the channel matrix H is the above formula (i), the expression of the channel matrix H is vectorized to obtain an expression of the channel vector h. As shown in the following formula (II), the channel vector h can be used to indicate the channel state of the first channel, and therefore, the estimation of the channel matrix H can be converted into an estimate of the channel vector h.
  • inf represents the lower bound.
  • T 2D (u) a two-layer Topplitz matrix containing the information of the exit angle array and the arrival angle array, which transmits the pilot signal X for the M transmit antennas.
  • the departure angle array information is the arrival angle array information when the N receiving antennas receive the received signal Y.
  • T 2D (u) can be expressed by the following formula (IV), u ⁇ C N ⁇ M .
  • each of the Topplitz matrix T b (0 ⁇ b ⁇ M) included in T 2D (u) is an N*N Topplitz matrix, and can be expressed by the following formula (V).
  • the two-layer Topplitz matrix T 2D (u) can be used to convert the expression of the atomic norm of the channel vector h to the following formula (VI), where t is a variable and trace is the sum of the eigenvalues of the matrix, h H is the conjugate transpose of the h, Indicates a semi-definite.
  • the receiving end may determine the channel vector h of the first channel according to the pilot signal X, the received signal Y, and the above formula (VI), thereby implementing channel state information of the first channel. estimate.
  • v is the weight associated with the noise variance, M, N, and the channel multipath number L, t is the variable, trace is the sum of the matrix eigenvalues, and
  • 2 represents the 2-norm of *.
  • the mathematical model convex optimization tool CVX tool can be used for solving, and the CVX is a system for establishing a convex function optimization problem based on matlab, which can Converting the matlab language to a modeling language, the CVX tool is its corresponding toolbox.
  • the related art which is not described in the embodiment of the present invention.
  • the method may further include: step 203.
  • Step 203 The receiving end determines the channel matrix H of the first channel according to the channel vector h of the first channel.
  • the parameter used to describe the channel state information of the channel is a channel matrix. Therefore, after the receiving end determines the channel vector h of the first channel, the channel matrix of the first channel can be determined according to the relationship between the channel matrix H and the channel vector h. H. After determining the channel matrix H, the precoding design on the transmitter side and the detection design on the receiver side can be performed according to the channel matrix H, thereby improving the performance of the MIMO system.
  • the foregoing channel estimation may be a schematic diagram shown in FIG. 5, that is, the pilot signal X is transmitted, and the pilot signal is transmitted in the first channel, and a certain noise may exist in the transmission process, and the transmission ends.
  • the received signal Y After receiving the received signal Y.
  • the received signal Y is subjected to signal vectorization, and the vectorized channel vector h is restored using the ANM algorithm and the two-layer Topplitz matrix.
  • the channel vector h is converted to the channel matrix H, ie the channel estimation of the first channel is completed.
  • FIG. 6 is a diagram showing the mean square error between the channel matrix estimated by the method provided by the embodiment of the present invention and the CS channel-based method in a different signal to noise ratio (SNR) environment ( Mean square error, MSE). It can be seen from FIG. 6 that the accuracy of the channel estimation method provided by the embodiment of the present invention is higher than the accuracy of the CS technology-based method in the prior art.
  • SNR signal to noise ratio
  • the mean square error between the estimated channel and the actual channel obtained by comparing the two methods of different training durations T is shown in FIG. 7 .
  • the embodiment of the present invention consistently outperforms the prior art CS-based method in the accuracy of channel estimation for a given training duration T. That is, in order to obtain the same estimation accuracy, the training duration required by the embodiment of the present invention is less than the training duration required by the CS technology-based method in the prior art, that is, the channel estimation efficiency of the embodiment of the present invention is higher.
  • the receiving end obtains the received signal Y, which is obtained after the pilot signal X transmitted by the transmitting end is transmitted through the first channel, and the transmitting end includes M transmitting antennas, and then the receiving end Estimating the channel vector of the first channel using the atomic norm minimization ANM algorithm based on the pilot signal X, the received signal Y, and the two-layer Topplitz matrix T 2D (u) containing the exit angle array information and the arrival angle array information Therefore, the problem that the actual angle of arrival and the angle of departure information cannot be accurately estimated by mapping the angle of arrival and the angle of departure on the sparse grid is solved in the prior art, thereby improving the accuracy and estimation efficiency of the channel estimation.
  • a device such as a receiving device, in order to implement the above functions, includes hardware structures and/or software modules that perform respective functions.
  • a device such as a receiving device, in order to implement the above functions, includes hardware structures and/or software modules that perform respective functions.
  • the embodiments of the present invention can be implemented in a combination of hardware or hardware and computer software in conjunction with the apparatus and algorithm steps of the various examples described in the embodiments disclosed herein. Whether a function is implemented in hardware or computer software to drive hardware depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods to implement the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present application.
  • FIG. 8 is a schematic diagram showing a possible structure of the receiving end device involved in the foregoing embodiment.
  • the receiving end device 300 includes: an obtaining unit 301, and an estimating unit 302. .
  • the obtaining unit 301 is configured to perform step 201 in FIG. 3 and FIG. 4; the estimating unit 302 is configured to perform step 202 in FIG. 3 and FIG.
  • the receiving end device 300 may further include: a determining unit 303, configured to perform step 203 of FIG. All the related content of the steps involved in the foregoing method embodiments may be referred to the functional description of the corresponding functional modules, and details are not described herein again.
  • FIG. 9 is a schematic diagram showing a possible logical structure of a receiving end device 310 involved in the foregoing embodiment according to an embodiment of the present invention.
  • the receiving device 310 includes a processor 312, a communication interface 313, a memory 311, and a bus 314.
  • the processor 312, the communication interface 313, and the memory 311 are connected to one another via a bus 314.
  • the processor 312 is configured to perform control management on the actions of the receiving device 310.
  • the processor 312 is configured to perform steps 201-202 in FIG. 3 and FIG. 4, and step 203 in FIG. And/or other processes for the techniques described herein.
  • the communication interface 313 is for supporting the receiving device 310 to perform communication.
  • the memory 311 is configured to store program codes and data of the receiving device 310.
  • the processor 312 can be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It is possible to implement or carry out the various illustrative logical blocks, modules and circuits described in connection with the present disclosure.
  • the processor may also be a combination of computing functions, for example, including one or more microprocessor combinations, combinations of digital signal processors and microprocessors, and the like.
  • the bus 314 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus.
  • PCI peripheral component interconnect
  • EISA extended industry standard architecture
  • the bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one thick line is shown in Figure 9, but it does not mean that there is only one bus or one type of bus.
  • a computer readable storage medium is stored, where computer execution instructions are stored, when at least one processor of the device executes the computer to execute an instruction, The channel estimation method provided in FIG. 3 or FIG. 4 is performed.
  • a computer program product comprising computer executable instructions stored in a computer readable storage medium; at least one processor of the device may be Reading the storage medium reads the computer execution instructions, and the at least one processor executing the computer execution instructions causes the apparatus to implement the channel estimation method provided by FIG. 3 or FIG.
  • the receiving end obtains the received signal Y, which is obtained after the pilot signal X transmitted by the transmitting end is transmitted through the first channel, and the transmitting end includes M transmitting antennas, and then the receiving end is based on the guiding The frequency signal X, the received signal Y, and the two-layer Topplitz matrix T 2D (u) including the leaving angle array information and the arrival angle array information, using the atomic norm minimization ANM algorithm to estimate the channel vector h of the first channel, thereby The problem that the actual angle of arrival and the angle of departure information cannot be accurately estimated by mapping the angle of arrival and the angle of departure on the sparse grid is solved in the prior art, thereby improving the accuracy and estimation efficiency of the channel estimation.

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Abstract

本发明实施例提供一种信道估计方法及装置，涉及通信技术领域，解决了现有技术中信道估计准确率低的问题。所述方法应用于接收端，该接收端包括N个接收天线，N为大于或等于2的正整数，该方法包括：接收端获取接收信号Y，所述接收信号Y是由发射端发送的导频信号X经过第一信道传输后得到的，发射端包括M个发射天线；所述接收端基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计所述第一信道的信道向量h；其中，所述T2D(u)包括M*M个托普利兹矩阵，且每个所述托普利兹矩阵的维度为N*N，所述T2D(u)包含离开角阵列信息、以及达角阵列信息。

Description

一种信道估计方法及装置 技术领域
本申请涉及通信技术领域，尤其涉及一种信道估计方法及装置。
背景技术
随着通信技术的快速发展，出现了越来越多使用毫米波和大规模多输入输出(massive multi-input multi-output，massive MIMO)技术的通信系统，该通信系统也可以称为MIMO系统，具有更宽的带宽和更多的可利用频谱资源，其工作频率范围可以在几十吉到几百吉赫兹。massive MIMO技术也可以称为大天线阵列技术，是指利用大规模的天线阵列进行信号的接收和发送。为了采用MIMO系统进行高效通信，需要对MIMO系统的信道的信息状态信息进行估计。
在毫米波频段，massive MIMO的信道呈现稀疏特性，经空间散射体形成的有效传播路径的个数远远小于天线的个数，而传统的信道估计方法均是基于信道为密集多径的假设来实现的，因此无法用来高效准确地获取massive MIMO信道的信息状态信息。目前，在已知信道呈现稀疏特性的情况下，可通过压缩感知(comprehensive sensing，CS)技术来对massive MIMO信道进行估计。基于CS技术进行信道估计时，是基于信道在角度域的稀疏网格特性假设，即当天线阵列的数目足够大时，可以假设到达角(angle of arrival，AoA)和离开角(angle of department，AoD)的空间角度落在均匀间隔的网格上，然后通过二维空间傅里叶变换建立虚拟信道
Figure PCTCN2017078830-appb-000001
以及将虚拟信道
Figure PCTCN2017078830-appb-000002
向量化为
Figure PCTCN2017078830-appb-000003
之后利用
Figure PCTCN2017078830-appb-000004
的稀疏性，通过压缩感知算法进行恢复。在恢复
Figure PCTCN2017078830-appb-000005
后通过逆向量化得到虚拟信道
Figure PCTCN2017078830-appb-000006
从而再根据傅里叶逆变换得到信道矩阵H。
但是，上述方法是基于信道在角度域的稀疏网格特性假设，即假设所有的AoA和AoD落在均匀间隔的网格上，比如，假设AoA和AoD的角度范围为1°-30°，发射天线和接收天线的数目均为30，如图1所示，则该稀疏网格为一个30*30的网格，行代表接收天线，列代表发射天线，每个网格为1°，每条路径都对一个AoD和一个AoA，若其中一条路径的AoD为12°、到达角为15°，则该条路径就落在第15行第12列交接的格子上。但是，在实际应用中，AoD和AoA并不一定为整数，如图1所示的稀疏网格，若一条路径对应的AoD为12.35°、到达角为15.48°，则该条路径对应的点就会落在网格之外，而CS技术只假设在网格上的点才有意义，因此网格之外的点就会被忽略，从而只基于网格上的点使用CS技术进行信道估计时的准确度较低。
发明内容
本发明的实施例提供一种信道估计方法及装置，解决了现有技术中信道估计准确率低的问题。
为达到上述目的，本发明的实施例采用如下技术方案：
第一方面，提供一种信道估计方法，应用于接收端，该接收端包括N个接收 天线，N为大于或等于2的正整数，该方法包括：接收端通过第一信道获取接收信号Y，接收信号Y是由发射端发送的导频信号X经过第一信道传输后得到的，发射端包括M个发射天线，M为大于或等于2的正整数；接收端基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计第一信道的信道向量h；其中，T2D(u)包括M*M个托普利兹矩阵，每个托普利兹矩阵的维度为N*N，T2D(u)包含离开角阵列信息、以及到达角阵列信息。上述技术方案中，通过包含离开角阵列信息、以及到达角阵列信息的两层托普利兹矩阵T2D(u)、导频信号X和接收信号Y，使用原子范数最小化ANM算法估计第一信道的信道向量h，从而解决了现有技术中通过将到达角和离开角映射在理想稀疏网格上，而导致实际到达角和离开角信息无法准确估计的问题，从而提高了信道估计的准确度和估计效率。
结合第一方面，在第一方面的第一种可能的实现方式中，若第一信道中无噪声，则接收端基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计第一信道的信道向量h，包括：基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(1)，确定第一信道的信道向量h；式中，t为变量，trace是指矩阵的特征值的总和，hH为h的共轭转置，y为接收信号Y的向量化表示，XT为所述X的转置，I为单位矩阵，
Figure PCTCN2017078830-appb-000007
表示克罗内克积，
Figure PCTCN2017078830-appb-000008
表示半正定；
Figure PCTCN2017078830-appb-000009
上述可能的实现方式中，根据上述公式(1)可以估计出无噪声理想情况下第一信道的信道向量，与现有基于CS的方法相比，提高无噪声理想情况下信道估计的准确度和估计效率。
结合第一方面，在第一方面的第二种可能的实现方式中，若第一信道中有噪声，则接收端基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计第一信道的信道向量h，包括：基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(2)，确定第一信道的信道向量h；式中，v为与噪声方差、M、N以及信道多径数有关的权重，t为变量，trace是指矩阵的特征值的总和，||*||2表示*的2-范数，y为接收信号Y的向量化 表示，XT为X的转置，I为单位矩阵，
Figure PCTCN2017078830-appb-000010
表示克罗内克积，hH为所述h的共轭转置，
Figure PCTCN2017078830-appb-000011
表示半正定；
Figure PCTCN2017078830-appb-000012
上述可能的实现方式中，根据上述公式(2)可以估计出有噪声情况下第一信道的信道向量，与现有基于CS的方法相比，提高有噪声情况下信道估计的准确度和估计效率。
结合第一方面至第一方面的第二种可能的实现方式中的任一种，在第一方面的第三种可能的实现方式中，接收端基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计第一信道的信道向量h之后，该方法还包括：根据第一信道的信道向量h，确定第一信道的信道矩阵H。上述可能的实现方式中，可以根据第一信道的信道向量h，确定第一信道的信道矩阵H，从而基于该信道矩阵H进行发射机侧的预编码设计、以及接收机侧的检测设计时，可以提高MIMO系统的性能。
第二方面，提供一种接收端设备，该接收端设备包括N个接收天线，N为大于或等于2的正整数，该接收端设备包括：获取单元，用于获取接收信号Y，该接收信号Y是由发射端设备发送的导频信号X经过第一信道传输后得到的，发射端设备M个发射天线；估计单元，用于基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计第一信道的信道向量h；其中，T2D(u)包括M*M个托普利兹矩阵，且每个所述托普利兹矩阵的维度为N*N，T2D(u)包含离开角阵列信息、以及到达角阵列信息。
结合第二方面，在第二方面的第一种可能的实现方式中，若第一信道中无噪声，估计单元具体用于：基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(3)，确定第一信道的信道向量h；式中，t为变量，trace是指矩阵的特征值的总和，hH为h的共轭转置，y为接收信号Y的向量化表示，XT为所述X的转置，I为单位矩阵，
Figure PCTCN2017078830-appb-000013
表示克罗内克积，
Figure PCTCN2017078830-appb-000014
表示半正定；
Figure PCTCN2017078830-appb-000015
结合第二方面，在第二方面的第二种可能的实现方式中，若第一信道中有噪声，估计单元具体用于：基于导频信号X、接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(4)，确定第一信道的信道向量h；式中，v为与噪声方差、M、N以及信道多径数有关的权重，t为变量，trace是指矩阵的特征值的总和，||*||2表示*的2-范数，y为接收信号Y的向量化表示，XT为X的转置，I为单位矩阵，
Figure PCTCN2017078830-appb-000016
表示克罗内克积，hH为h的共轭转置，
Figure PCTCN2017078830-appb-000017
表示半正定；
Figure PCTCN2017078830-appb-000018
结合第二方面至第二方面的第二种可能的实现方式中的任一种，在第二方面的第三种可能的实现方式中，该装置还包括：确定单元，用于根据第一信道的信道向量h，确定第一信道的信道矩阵H。
第三方面，提供一种接收端设备，该接收端设备包括处理器和存储器，存储器中存储代码和数据，处理器运行存储器中的代码使得接收端设备执行第一方面至第一方面的第三种可能的实现方式中的任一项所提供的信道估计方法。
本申请的又一方面提供了一种计算机可读存储介质，所述计算机可读存储介质中存储有指令，当其在计算机上运行时，使得计算机执行上述各方面所述的方法。
本申请的又一方面提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行上述各方面所述的方法。
可以理解地，上述提供的任一种信道估计方法的接收端设备、计算机存储介质或者计算机程序产品均用于执行上文所提供的对应的方法，因此，其所能达到的有益效果可参考上文所提供的对应的方法中的有益效果，此处不再赘述。
附图说明
图1为一种稀疏网格的示意图；
图2为本发明实施例提供的一种MIMO系统的结构示意图；
图3为本发明实施例提供的一种信道估计方法的流程图；
图4为本发明实施例提供的另一种信道估计方法的流程图；
图5为本发明实施例提供的一种信道估计的流程示意图；
图6为本发明实施例提供的一种信道估计结果的比较示意图；
图7为本发明实施例提供的另一种信道估计结果的比较示意图；
图8为本发明实施例提供的一种接收端设备的结构示意图；
图9为本发明实施例提供的另一种接收端设备的结构示意图。
具体实施方式
移动通信中的多输入输出(multi-input multi-output，MIMO)技术是指利用多根发射天线和多根接收天线进行无线传输的技术，使用这种技术的无线通信系统即可 以称为MIMO系统，MIMO系统可以利用多天线来提高频谱效率也可用来抑制信道衰落。图2为本发明实施例所应用的MIMO系统的结构示意图，参见图2，该MIMO系统包括发射机和接收机，也可以成为发射端设备和接收端设备。在实际应用中，发射机可以为基站，接收机可以为用户设备。其中，发射机可以包含M个发射天线，接收机端可以包含N个接收天线，且M、N为大于或等于2的正整数，信号可以通过M个发射天线和N个天线接收进行传送和接收，信号传输过程中经过散射体后角度发生变化。massive MIMO系统是指M、N为较大值时的MIMO系统，即MIMO系统包括的发射天线和接收天线的数目较多。
在本发明实施例中，该MIMO系统可以是基于均匀直线阵列的通信系统，均匀直线阵列是指相邻天线之间的距离相同，且天线的排列呈直线。若该通信系统的有效传输路径为L，信道矩阵用H表示，则H是N*M阶矩阵，且可以通过如下公式(I)表示。
Figure PCTCN2017078830-appb-000019
式中，ρ为平均路径损耗，αl为第l条路径上的衰减系数，θr,l和θt,l分别表示第l条路径上的到达角AoA和离开角AoD，S为包含ρ和αl的矩阵，Ar(θr)为接收阵列响应矩阵，At(θt)为发射阵列响应矩阵，具体的θr,l和θt,l如图2中所示。
当发射机发送的导频信号为x∈CM(C表示有理数)时，则接收机接收到的接收信号y∈CN可表示为y＝Hx+w，式中w∈CN表示高斯噪声；若不考虑噪声的理想情况下，则y＝Hx。当训练序列时长为T时，该接收信号模型可以表示为Y＝HX+W，式中，Y＝[y1,y2,…,yT]∈CN×T,X＝[x1,x2,…,xT]∈CM×T，W＝[w1,w2,…,wT]∈CN×T；若不考虑噪声的理想情况下，则Y＝HX。信道估计即研究根据已知导频信号X从接收信号Y中如何准确地估计出信道矩阵H以便后续利用MIMO技术进行稳定高效信号传输。
图3为本发明实施例提供的一种信道估计方法的流程图，用于估计上述图2所示的MIMO系统的信道，且应用于接收端，该接收端包括N个接收天线，N为大于或等于2的正整数，参见图3，该方法包括以下步骤。
步骤201：接收端获取接收信号Y，该接收信号Y是由发射端发送的导频信号X经过第一信道传输后得到的，发射端包括M个发射天线，M为大于或等于2的正整数。
其中，导频信号也可以称为参考信号，一般是指通信系统发送以用于监测、评估、控制、均衡、或同步等目的单频率已知信号。导频信号可以包括多种信号，比如，在进行信道估计时，该导频信号可以为信道状态信息参考信号(channel state information reference signal，CSI-RS)，或者探测参考信号(sounding reference signal，SRS)。
具体的，当发射天线的数目为M，导频信号的训练序列时长为T时，则导频信号X的表达式可以表示为X＝[x1,x2,…,xT]∈CM×T。对应的，当接收天线的数目为N时， 该接收信号Y表达式可以表示为Y＝[y1,y2,…,yT]∈CN×T。
步骤202：接收端基于导频信号X、接收信号Y和两层托普利兹(Toeplitz)矩阵T2D(u)，使用原子范数最小化(atomic norm minimization，ANM)算法估计第一信道的信道向量h。
其中，两层托普利兹矩阵T2D(u)包括M*M个托普利兹矩阵，且每个托普利兹矩阵的维度为N*N，T2D(u)包含离开角阵列信息、以及到达角阵列信息。具体的，该离开角阵列信息可以是M个发射天线发送导频信号X时的离开角信息，到达角阵列信息可以是N个接收天线接收该接收信号Y时的到达角信息。可选的，该离开角阵列信息和达到角阵列信息可以是接收端在预设时间内统计或计算确定的信息。此处对接收端确定到达角阵列信息和离开角阵列信息的具体方式不做限定，可以参考本领域现有技术的方式确定。
其中，信道向量h是指信道矩阵H向量化以后的形式，若信道矩阵H的表达式为上述公式(i)，则对信道矩阵H的表达式进行向量化，得到该信道向量h的表达式如下公式(II)所示，该信道向量h可以用于指示第一信道的信道状态，因此，对信道矩阵H的估计，可以转换为对信道向量h的估计。
Figure PCTCN2017078830-appb-000020
式中，vec是指求向量化的符号，
Figure PCTCN2017078830-appb-000021
表示克罗内克积，[]*表示求共轭，θl＝(θr,l,θt,l)，
Figure PCTCN2017078830-appb-000022
是扩展的阵列响应向量，且可以用于表示包含到达角AoA信息和离开角AoD信息的阵列响应向量。
对于上述公式(II)表示的信道向量h，根据原子范数最小化ANM算法，可以定义原子集为：A:＝{a2D(θ),θ∈[0,2π)×[0,2π)}，从而可以得到信道向量h的原子范数表达式为如下公式(III)。式中，inf表示下确界。
||h||A＝inf{∑lsl||h＝∑lsla2D(θl),a2D(θl)∈A}(III)
由于原子集中原子数目是无限的，因此该优化问题一般无法直接求解。在本发明实例中，可以通过将ANM转换为半正定规划(semi-definite programming，SDP)进行求解。将ANM转换为SDP求解的方法，会引入一个含有离开角阵列信息和到达角阵列信息的两层托普利兹矩阵T2D(u)，该离开角阵列信息为M个发射天线发送导频信号X时的离开角阵列信息，该到达角阵列信息为N个接收天线接收接收信号Y时的到达角阵列信息。T2D(u)可以通过如下公式(IV)表示，u∈CN×M。
Figure PCTCN2017078830-appb-000023
其中，T2D(u)中包括的每个托普利兹矩阵Tb(0≤b≤M)是一个N*N的拓普利兹矩阵，且可以通过如下公式(V)表示。
Figure PCTCN2017078830-appb-000024
通过该两层拓普利兹矩阵T2D(u)可以将信道向量h的原子范数的表达式转换为如下公式(VI)，式中t为变量，trace是指矩阵的特征值的总和，hH为所述h的共轭转置，
Figure PCTCN2017078830-appb-000025
表示半正定。
Figure PCTCN2017078830-appb-000026
其中，在对第一信道进行估计时，接收端可以根据导频信号X、接收信号Y以及上述公式(VI)，确定第一信道的信道向量h，从而实现对第一信道的信道状态信息的估计。
具体的，在第一信道没有噪声的理想情况下，接收信号Y的模型可以表示为Y＝HX，则
Figure PCTCN2017078830-appb-000027
y为接收信号Y的向量化表示。因此，对第一信道的信道向量h进行恢复时，可以通过对如下公式(1)进行求解，以确定第一信道的信道向量h。式中，所述t为变量，trace是指矩阵特征值的总和，所述，所述y为所述接收信号Y的向量化表示，XT为X的转置，I为单位矩阵，即I为对角线元素为1、其他元素为0的矩阵，s.t.表示为限定条件，
Figure PCTCN2017078830-appb-000028
表示半正定。
Figure PCTCN2017078830-appb-000029
在第一信道存在噪声的情况下，接收信号Y的模型可以表示为Y＝HX+W，则
Figure PCTCN2017078830-appb-000030
因此，对第一信道的信道向量h进行恢复时，可以通过对如下公式(2)进行求解，以确定第一信道的信道向量h。式中，v为与噪声方差、M、N以及信道多径数L有关的权重，t为变量，trace是指矩阵特征值的总和，||*||2表示*的2-范数。
Figure PCTCN2017078830-appb-000031
其中，在通过上述公式(1)或(2)确定第一信道的信道向量h时，可以通过数学模型凸优化工具CVX tool进行求解，CVX是基于matlab的凸函数最优化问题的建立系统，可以将matlab语言转换为建模语言，CVX tool是其相应的工具箱。具体的求解过程可以参考相关技术，本发明实施例对此不做阐述。
进一步的，参见图4，在步骤202之后，该方法还可以包括：步骤203。
步骤203：接收端根据第一信道的信道向量h，确定第一信道的信道矩阵H。
其中，通常用于描述信道的信道状态信息的参数为信道矩阵，因此当接收端确定第一信道的信道向量h之后，可以根据信道矩阵H与信道向量h的关系，确定第一信道的信道矩阵H。在确定信道矩阵H之后，可以根据该信道矩阵H进行发射机侧的预编码设计以及接收机侧的检测设计，进而提高MIMO系统的性能。
具体的，上述信道估计在具体实现时可以为图5所示的示意图，即发送导频信号X，导频信号在第一信道中进行传输，且在传输过程可能会存在一定的噪声，传输结束后获取到接收信号Y。然后，将接收信号Y进行信号向量化，并利用ANM算法和两层托普利兹矩阵恢复向量化的信道向量h。最后，将信道向量h转换为信道矩阵H，即完成第一信道的信道估计。
通过将本发明实施例提供的信道估计方法和现有技术中基于CS技术进行信道估计的方法进行对比，得到的MIMO系统的性能如图6所示。图6表示的是在不同信噪比(signal to noise ratio，SNR)环境下，本发明实施例提供的方法与基于CS技术的方法估计得到的信道矩阵与实际信道矩阵之间的均方误差(mean square error，MSE)。由图6可知，本发明实施例提供的信道估计方法的准确度高于现有技术中基于CS技术的方法的准确度。
此外，通过比较不同训练时长T下两种方法所得到的估计信道与实际信道之间的均方误差，如图7所示。由图7中可知，在给定的训练时长T下，本发明实施例在信道估计的准确度方面始终优于现有技术中基于CS技术的方法。也即是，为取得相同估计准确性，本发明实施例需要的训练时长要少于现有技术中基于CS技术的方法所需的训练时长，即本发明实施例的信道估计效率更高。
在本发明实施例中，接收端通过获取接收信号Y，该接收信号Y是由发射端发送的导频信号X经过第一信道传输后得到的，该发射端包括M个发射天线，之后接收端基于导频信号X、接收信号Y和包含离开角阵列信息、以及到达角阵列信息的两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计第一信道的信道向量h，从而解决了现有技术中通过将到达角和离开角映射在稀疏网格上，而导致实际到达角和离开角信息无法准确估计的问题，从而提高了信道估计的准确度和估计效率。
上述主要从设备的角度对本发明实施例提供的方案进行了介绍。可以理解的 是，设备，例如接收端设备为了实现上述功能，其包含了执行各个功能相应的硬件结构和/或软件模块。本领域技术人员应该很容易意识到，结合本文中所公开的实施例描述的各示例的设备及算法步骤，本发明实施例能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
本发明的实施例可以根据上述方法示例对接收端设备进行功能模块的划分，例如，可以对应各个功能划分各个功能模块，也可以将两个或两个以上的功能集成在一个处理模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。需要说明的是，本发明的实施例中对模块的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
在采用对应各个功能划分各个功能模块的情况下，图8示出了上述实施例中所涉及的接收端设备的一种可能的结构示意图，接收端设备300包括：获取单元301、和估计单元302。其中，获取单元301用于执行图3和图4中的步骤201；估计单元302用于执行图3和图4中的步骤202。进一步的，接收端设备300还可以包括：确定单元303，确定单元303用于执行图4步骤203。上述方法实施例涉及的各步骤的所有相关内容均可以援引到对应功能模块的功能描述，在此不再赘述。
在硬件实现上，上述估计单元302和确定单元303可以为处理器，获取单元301可以为接收器，其与发送器可以构成通信接口。
图9所示，为本发明实施例提供的上述实施例中所涉及的接收端设备310的一种可能的逻辑结构示意图。接收端设备310包括：处理器312、通信接口313、存储器311以及总线314。处理器312、通信接口313以及存储器311通过总线314相互连接。在发明实施例中，处理器312用于对接收端设备310的动作进行控制管理，例如，处理器312用于执行图3和图4中的步骤201-步骤202、以及图4中的步骤203，和/或用于本文所描述的技术的其他过程。通信接口313用于支持接收端设备310进行通信。存储器311，用于存储接收端设备310的程序代码和数据。
其中，处理器312可以是中央处理器单元，通用处理器，数字信号处理器，专用集成电路，现场可编程门阵列或者其他可编程逻辑器件、晶体管逻辑器件、硬件部件或者其任意组合。其可以实现或执行结合本申请公开内容所描述的各种示例性的逻辑方框，模块和电路。所述处理器也可以是实现计算功能的组合，例如包含一个或多个微处理器组合，数字信号处理器和微处理器的组合等等。总线314可以是外设部件互连标准(peripheral component interconnect，PCI)总线或扩展工业标准结构(extended industry standard architecture，EISA)总线等。所述总线可以分为地址总线、数据总线、控制总线等。为便于表示，图9中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
在本发明的另一实施例中，还提供一种计算机可读存储介质，计算机可读存储介质中存储有计算机执行指令，当设备的至少一个处理器执行该计算机执行指令时，设 备执行图3或图4所提供的信道估计方法。
在本发明的另一实施例中，还提供一种计算机程序产品，该计算机程序产品包括计算机执行指令，该计算机执行指令存储在计算机可读存储介质中；设备的至少一个处理器可以从计算机可读存储介质读取该计算机执行指令，至少一个处理器执行该计算机执行指令使得设备实施图3或图4所提供的信道估计方法。
在本发明实例中，接收端通过获取接收信号Y，该接收信号Y是由发射端发送的导频信号X经过第一信道传输后得到的，发射端包括M个发射天线，之后接收端基于导频信号X、接收信号Y和包含离开角阵列信息、以及到达角阵列信息的两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计第一信道的信道向量h，从而解决了现有技术中通过将到达角和离开角映射在稀疏网格上，而导致实际到达角和离开角信息无法准确估计的问题，从而提高了信道估计的准确度和估计效率。
最后应说明的是：以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何在本申请揭露的技术范围内的变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (9)

  1. 一种信道估计方法，应用于接收端，所述接收端包括N个接收天线，所述N为大于或等于2的正整数，其特征在于，所述方法包括：
    所述接收端通过第一信道获取接收信号Y，所述接收信号Y是由发射端发送的导频信号X经过所述第一信道传输后得到的，所述发射端包括M个发射天线，所述M为大于或等于2的正整数；
    所述接收端基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计所述第一信道的信道向量h；其中，所述T2D(u)包括M*M个托普利兹矩阵，且每个所述托普利兹矩阵的维度为N*N，所述T2D(u)包含离开角阵列信息、以及到达角阵列信息。
  2. 根据权利要求1所述的方法，其特征在于，若所述第一信道中无噪声，所述接收端基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计所述第一信道的信道向量h，包括：
    基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(1)，确定所述第一信道的信道向量h，
    Figure PCTCN2017078830-appb-100001
    式中，所述t为变量，trace是指矩阵的特征值的总和，所述hH为所述h的共轭转置，所述y为所述接收信号Y的向量化表示，所述XT为所述X的转置，所述I为单位矩阵，所述
    Figure PCTCN2017078830-appb-100002
    表示克罗内克积，所述≥表示半正定。
  3. 根据权利要求1所述的方法，其特征在于，若所述第一信道中有噪声，所述接收端基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计所述第一信道的信道向量h，包括：
    基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(2)，确定所述第一信道的信道向量h，
    Figure PCTCN2017078830-appb-100003
    式中，所述v为与噪声方差所述M、所述N以及信道多径数有关的权重，所述t为变量，trace是指矩阵的特征值的总和，||*||2表示*的2-范数，所述y为所述接收信 号Y的向量化表示，所述XT为所述X的转置，所述I为单位矩阵，所述
    Figure PCTCN2017078830-appb-100004
    表示克罗内克积，所述hH为所述h的共轭转置，所述≥表示半正定。
  4. 根据权利要求1-3任一项所述的方法，其特征在于，所述接收端基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计所述第一信道的信道向量h之后，所述方法还包括：
    所述接收端根据所述第一信道的信道向量h，确定所述第一信道的信道矩阵H。
  5. 一种接收端设备，所述接收端设备包括N个接收天线，所述N为大于或等于2的正整数，其特征在于，所述接收端设备包括：
    获取单元，用于通过第一信道获取接收信号Y，所述接收信号Y是由发射端设备发送的导频信号X经过所述第一信道传输后得到的，所述发射端设备包括M个发射，所述M大于或等于2的正整数；
    估计单元，用于基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，使用原子范数最小化ANM算法估计所述第一信道的信道向量h；其中，所述T2D(u)包括M*M个托普利兹矩阵，且每个所述托普利兹矩阵的维度为N*N，所述T2D(u)包含离开角阵列信息、以及到达角阵列信息。
  6. 根据权利要求5所述的接收端设备，其特征在于，若所述第一信道中无噪声，所述估计单元，具体用于：
    基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(3)，确定所述第一信道的信道向量h，
    Figure PCTCN2017078830-appb-100005
    式中，所述t为变量，trace是指矩阵的特征值的总和，所述hH为所述h的共轭转置，所述y为所述接收信号Y的向量化表示，所述XT为所述X的转置，所述I为单位矩阵，所述
    Figure PCTCN2017078830-appb-100006
    表示克罗内克积，所述≥表示半正定。
  7. 根据权利要求5所述的接收端设备，其特征在于，若所述第一信道中有噪声，所述估计单元，具体用于：
    基于所述导频信号X、所述接收信号Y和两层托普利兹矩阵T2D(u)，通过如下公式(4)，确定所述第一信道的信道向量h，
    Figure PCTCN2017078830-appb-100007
    式中，所述v为与噪声方差、所述M、所述N以及信道多径数有关的权重，所述t为变量，trace是指矩阵的特征值的总和，||*||2表示*的2-范数，所述y为所述接收信号Y的向量化表示，所述XT为所述X的转置，所述I为单位矩阵，所述
    Figure PCTCN2017078830-appb-100008
    表示克罗内克积，所述hH为所述h的共轭转置，所述≥表示半正定。
  8. 根据权利要求5-7任一项所述的接收端设备，其特征在于，所述接收端设备还包括：
    确定单元，用于根据所述第一信道的信道向量h，确定所述第一信道的信道矩阵H。
  9. 一种接收端设备，其特征在于，所述接收端设备包括处理器和存储器，所述存储器中存储代码和数据，所述处理器运行所述存储器中的代码使得所述接收端设备执行权利要求1-4任一项所述的信道估计方法。
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