WO2017076371A1 - 一种波束赋形的方法及装置 - Google Patents
一种波束赋形的方法及装置 Download PDFInfo
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- WO2017076371A1 WO2017076371A1 PCT/CN2017/070124 CN2017070124W WO2017076371A1 WO 2017076371 A1 WO2017076371 A1 WO 2017076371A1 CN 2017070124 W CN2017070124 W CN 2017070124W WO 2017076371 A1 WO2017076371 A1 WO 2017076371A1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/02—Arrangements for detecting or preventing errors in the information received by diversity reception
- H04L1/06—Arrangements for detecting or preventing errors in the information received by diversity reception using space diversity
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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
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
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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
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
Definitions
- the present disclosure relates to the field of communications technologies, and in particular, to a method and apparatus for beamforming.
- LTE Long Term Evolution
- LTE-A Long Term Evolution-Advanced
- MIMO + OFDM Orthogonal Frequency Division Multiplexing
- the performance gain of MIMO technology comes from the spatial freedom that can be obtained with multi-antenna systems, and uses spatial degrees of freedom to achieve greater data transmission. Therefore, one of the most important evolution directions of MIMO technology in the standardization process is the expansion of dimensions.
- SRS Sounding Reference Signal
- the spatial channel information corresponding to 256 and 512 digital antenna ports is subjected to eigenvalue decomposition to obtain a beamforming shaped vector.
- eigenvalue decomposition to obtain a beamforming shaped vector.
- the feature decomposition of covariance matrices up to 128x128, 256x256, and 512x512 dimensions is extremely complex.
- a method of calculating a beamforming method using a feature decomposition method on a base station side generally adopts an overall three-dimensional spatial channel for feature decomposition.
- This method can obtain a complete channel feature vector.
- This complete channel feature vector includes not only the possible multiple streams in the vertical direction but also the possible multiple streams in the horizontal direction.
- the complexity of feature vector decomposition caused by this method Too large the base station is difficult to complete when the code is implemented.
- the purpose of the disclosure is to provide a method and a device for beamforming, which solves the problem that the feature vector decomposition caused by beamforming is too large in the prior art, and the base station is difficult to complete in code implementation. The problem.
- an embodiment of the present disclosure provides a method for beamforming, including: acquiring a channel matrix of an uplink channel of a user equipment that transmits a sounding reference signal; and acquiring a first dimension feature vector and a second dimension of the channel matrix, respectively. a feature vector; determining a three-dimensional feature vector according to the first dimension feature vector and the second dimension feature vector; and performing beamforming according to the three-dimensional feature vector.
- the step of acquiring the channel matrix of the uplink channel of the user equipment to transmit the sounding reference signal includes: receiving a sounding reference signal sent by the user equipment by using the uplink channel: acquiring a channel matrix of the uplink channel according to the sounding reference signal .
- the step of acquiring the first dimension feature vector and the second dimension feature vector of the channel matrix respectively includes: acquiring a first feature vector and a second feature vector of the first dimension of the channel matrix; A feature vector and the second feature vector acquire a third feature vector, a fourth feature vector, a fifth feature vector, and a sixth feature vector of the second dimension.
- the determining the three-dimensional feature vector according to the first dimension feature vector and the second dimension feature vector includes: comparing a third feature value corresponding to the third feature vector with a fourth feature corresponding to the fourth feature vector a value, a fifth feature value corresponding to the fifth feature vector, and a sixth feature value corresponding to the sixth feature vector, determining a maximum feature value of the second dimension and a second largest feature value of the second dimension; And determining, by the feature value and the second largest feature value of the second dimension, a first target feature vector of the first dimension corresponding to the maximum feature value of the second dimension, corresponding to the second largest feature value of the second dimension a second target feature vector of the first dimension, a third target feature vector of the second dimension corresponding to the maximum feature value of the second dimension, and a second dimension of the second dimension corresponding to the second largest feature value of the second dimension a fourth target feature vector; determining a first three-dimensional feature vector according to the third target feature vector and the first target feature vector; determining the first according to the fourth target feature vector and
- the step of performing beamforming according to the three-dimensional feature vector includes: performing single-flow beamforming according to the first three-dimensional feature vector; or, according to the first three-dimensional feature vector And the second three-dimensional feature vector performs dual-flow beamforming.
- the step of acquiring the first feature vector and the second feature vector of the first dimension of the channel matrix includes: acquiring a first dimension correlation matrix of the channel matrix; performing eigenvalues on the first dimension correlation matrix Decomposing, obtaining a first feature vector and a second feature vector, and a first feature value corresponding to the first feature vector and a second feature value corresponding to the second feature vector; wherein the first feature value The maximum eigenvalue of the first dimension, the second eigenvalue being the second largest eigenvalue of the first dimension.
- the step of acquiring the third feature vector, the fourth feature vector, the fifth feature vector, and the sixth feature vector of the second dimension according to the first feature vector and the second feature vector includes: according to the a first feature vector, constructing a first equivalent channel matrix of the second dimension; constructing a second equivalent channel matrix of the second dimension according to the second feature vector; acquiring a first equivalent channel matrix of the second dimension a first correlation matrix and a second correlation matrix of the second equivalent channel matrix of the second dimension; performing eigenvalue decomposition on the first correlation matrix to obtain a third eigenvector and a fourth eigenvector, and a third feature value corresponding to the third feature vector and a fourth feature value corresponding to the fourth feature vector; wherein the third feature value is a maximum feature value of the first equivalent channel matrix of the second dimension,
- the fourth characteristic value is a sub-maximum eigenvalue of the first equivalent channel matrix of the second dimension; performing eigenvalue decomposition on the second correlation matrix to obtain a fifth eigenvector and
- the embodiment of the present disclosure further provides a device for beamforming, comprising: a matrix acquiring module, configured to acquire a channel matrix of an uplink channel of a user equipment to transmit a sounding reference signal; and a vector acquiring module, configured to respectively acquire the channel matrix a first-dimensional feature vector and a second-dimensional feature vector; a determining module, configured to determine a three-dimensional feature vector according to the first-dimensional feature vector and the second-dimensional feature vector; and a shaping module configured to perform a beam according to the three-dimensional feature vector Forming.
- a matrix acquiring module configured to acquire a channel matrix of an uplink channel of a user equipment to transmit a sounding reference signal
- a vector acquiring module configured to respectively acquire the channel matrix a first-dimensional feature vector and a second-dimensional feature vector
- a determining module configured to determine a three-dimensional feature vector according to the first-dimensional feature vector and the second-dimensional feature vector
- a shaping module configured to perform a beam according to the three-dimensional feature vector Forming.
- the matrix obtaining module includes: a first matrix acquiring submodule, configured to receive a sounding reference signal sent by the user equipment by using the uplink channel: a second matrix acquiring submodule, configured to acquire the sound according to the sounding reference signal The channel matrix of the upstream channel.
- the vector obtaining module includes: a first vector acquiring submodule, configured to acquire the letter a first feature vector and a second feature vector of the first dimension of the track matrix; a second vector acquisition submodule configured to acquire a third feature vector of the second dimension according to the first feature vector and the second feature vector a fourth feature vector, a fifth feature vector, and a sixth feature vector.
- the determining module includes: a first determining submodule, configured to compare a third feature value corresponding to the third feature vector, a fourth feature value corresponding to the fourth feature vector, and a fifth feature corresponding to the fifth feature vector a value and a sixth feature value corresponding to the sixth feature vector, determining a maximum feature value of the second dimension and a second largest feature value of the second dimension; and a second determining submodule for using the maximum feature value of the second dimension a second largest eigenvalue of the second dimension, a first target eigenvector of the first dimension corresponding to the maximum eigenvalue of the second dimension, and a first dimension corresponding to the second largest eigenvalue of the second dimension a second target feature vector, a second target feature vector of the second dimension corresponding to the maximum feature value of the second dimension, and a fourth target feature of the second dimension corresponding to the second largest feature value of the second dimension a third determining submodule, configured to determine a first three-dimensional feature vector according to the third target feature vector and the
- the shaping module includes: a first shaping sub-module, configured to perform single-flow beamforming according to the first three-dimensional feature vector; or a second shaping sub-module according to the first three-dimensional The feature vector and the second three-dimensional feature vector perform dual-stream beamforming.
- the first vector obtaining sub-module includes: a first acquiring unit, configured to acquire a first-dimensional correlation matrix of the channel matrix; and a first decomposing unit, configured to perform eigenvalue decomposition on the first-dimensional correlation matrix Obtaining a first feature vector and a second feature vector, and a first feature value corresponding to the first feature vector and a second feature value corresponding to the second feature vector; wherein the first feature value is The largest eigenvalue of the first dimension, the second eigenvalue being the second largest eigenvalue of the first dimension.
- the second vector acquisition sub-module includes: a first construction unit, configured to construct a first equivalent channel matrix of the second dimension according to the first feature vector; and a second construction unit, configured to use, according to the first a second eigenvector, a second equivalent channel matrix of the second dimension; a second acquiring unit, configured to acquire a first correlation matrix of the first equivalent channel matrix of the second dimension and a second of the second dimension a second correlation matrix of the equivalent channel matrix; a second decomposition unit configured to perform eigenvalue decomposition on the first correlation matrix to obtain a third feature vector and a fourth feature vector, and corresponding to the third feature vector a third feature value and a fourth feature value corresponding to the fourth feature vector; wherein The third eigenvalue is a maximum eigenvalue of the first equivalent channel matrix of the second dimension, the fourth eigenvalue is a sub-maximum eigenvalue of the first equivalent channel matrix of the second dimension; and the third decomposing unit is configured to Performing eigenvalue decomposition on the
- An embodiment of the present disclosure further provides a beamforming device, including: a processor; and a memory connected to the processor through a bus interface, the memory being used to store a memory used by the processor when performing an operation
- Program and data when the processor calls and executes the program and data stored in the memory, implements the following functional module: a matrix acquisition module, configured to acquire a channel matrix of an uplink channel of the user equipment transmitting the sounding reference signal; a module, configured to respectively acquire a first-dimensional feature vector and a second-dimensional feature vector of the channel matrix; and a determining module, configured to determine a three-dimensional feature vector according to the first-dimensional feature vector and the second-dimensional feature vector; And a module, configured to perform beamforming according to the three-dimensional feature vector.
- the above technical solution of the present disclosure has at least the following beneficial effects: in the beamforming method and apparatus of the embodiment of the present disclosure, the beamforming vector including the complete three-dimensional channel information is obtained by two-level feature decomposition of the vertical dimension and the horizontal dimension, and the implementation is realized. More accurate 3D beam transmission; at the same time, it solves the complexity of feature decomposition of large-dimensional channel correlation matrix.
- FIG. 1 shows a basic flow chart of a method of beamforming provided by a first embodiment of the present disclosure
- FIG. 2 is a block diagram showing the structure of a beamforming device according to a second embodiment of the present disclosure.
- a first embodiment of the present disclosure provides a method for beamforming, including:
- Step 11 Obtain a channel matrix of an uplink channel that the user equipment transmits the sounding reference signal.
- Step 12 respectively acquiring a first dimension feature vector and a second dimension feature vector of the channel matrix
- Step 13 Determine a three-dimensional feature orientation according to the first dimension feature vector and the second dimension feature vector the amount
- Step 14 Perform beamforming according to the three-dimensional feature vector.
- the uplink channel is a channel for the user equipment UE to transmit a sounding reference signal (SRS signal), and the channel matrix of the uplink channel can be calculated by using the SRS signal.
- the direct feature decomposition of the channel matrix is complicated and difficult to implement. Therefore, in the first embodiment of the present disclosure, the channel matrix is analyzed from two dimensions to obtain a first-dimensional feature vector and a second-dimensional feature vector, thereby performing feature decomposition on the first-dimensional feature vector and the second-dimensional feature vector, respectively. Realize two-level feature decomposition and reduce the difficulty of feature decomposition. Furthermore, a three-dimensional feature vector is obtained, which is a beamforming vector including a guaranteed three-dimensional channel information, and finally a beamforming is performed according to the obtained three-dimensional feature vector, thereby realizing more accurate 3D beam transmission.
- SRS signal sounding reference signal
- the first dimension is a vertical dimension or a horizontal dimension
- the second dimension is a horizontal dimension or a vertical dimension.
- the first dimension is a vertical dimension
- the second dimension is a horizontal dimension or a vertical dimension.
- the beamforming method can not only utilize the horizontal dimension expansion, but also the vertical dimension expansion, flexible adaptation, and further solve all the antenna ports in the vertical direction.
- the coverage angle range is less than the problem of full 3D shaped transmission.
- step 11 in the first embodiment of the present disclosure includes: Step 111: Receive a sounding reference signal sent by the user equipment by using the uplink channel: Step 112, acquire a channel matrix of the uplink channel according to the sounding reference signal. .
- the user equipment UE transmits an SRS signal (probe reference signal), and the base station transmits a channel matrix of an uplink channel of the SRS signal according to the SRS signal.
- the number of SRS ports Base station antenna uplink channel on subcarriers Is an N V ⁇ N H matrix.
- the N V corresponds to the N V row of the large-scale antenna in the vertical direction
- the N H corresponds to the N H column of the large-scale antenna in the horizontal direction
- the N RB is the number of RBs (resource blocks) in the system bandwidth. Is the number of subcarriers in a resource block RB.
- step 12 in the first embodiment of the present disclosure includes: Step 121: acquiring a first feature vector and a second feature vector of the first dimension of the channel matrix; Step 122, according to the first feature vector and The second feature vector obtains a third feature vector, a fourth feature vector, a fifth feature vector, and a sixth feature vector of the second dimension.
- Step 121 includes: Step 1211: Acquire a first dimension correlation matrix of the channel matrix; Step 1212, perform eigenvalue decomposition on the first dimension correlation matrix to obtain a first feature vector and a second feature vector, and a first feature value corresponding to the first feature vector and a second feature value corresponding to the second feature vector; wherein the first feature value is a maximum feature value of the first dimension, and the second feature value is The second largest eigenvalue of the first dimension.
- the first dimension is a vertical dimension or a horizontal dimension
- the second dimension is a horizontal dimension or a vertical dimension.
- step 1211 the specific steps of acquiring the correlation matrix of the first dimension (for example, the first dimension is a vertical dimension) are:
- the specific step of performing eigenvalue decomposition on the vertical dimension correlation matrix in step 1212 is: calculating a vertical dimension feature vector: performing EVD (Eigenvalue Decomposition) decomposition Obtaining two vertical dimensional main feature vectors of the qth BfB shaped blocks of the kth SRS users, that is, the first feature vector And second eigenvector And corresponding two eigenvalues, namely the first eigenvalue And second eigenvalue First eigenvalue And second eigenvalue Corresponding to the two largest and the second largest eigenvalues respectively; then the first eigenvector And second eigenvector Is the two largest and the second largest eigenvalues with Corresponding feature vector. among them, with They are N V ⁇ 1 matrices.
- step 122 in the first embodiment of the present disclosure includes the following steps.
- Step 1221 Construct a first equivalent channel matrix of the second dimension according to the first feature vector.
- Step 1222 Construct a second equivalent channel matrix of the second dimension according to the second feature vector.
- Step 1223 Acquire a first correlation matrix of the first equivalent channel matrix of the second dimension and a second correlation matrix of the second equivalent channel matrix of the second dimension. That is, the correlation matrices of two horizontal dimensional equivalent channels are calculated separately. Calculation of user k SRS q BfB shaped blocks of the first level and a second level equivalent channel equivalent channel correlation matrix: the
- Step 1224 Perform eigenvalue decomposition on the first correlation matrix to obtain a third feature vector and a fourth feature vector, and a third feature value corresponding to the third feature vector and a fourth feature vector corresponding to the fourth feature vector.
- a fourth eigenvalue wherein the third eigenvalue is a maximum eigenvalue of a first equivalent channel matrix of the second dimension, and the fourth eigenvalue is a sub-maximum feature of the first equivalent channel matrix of the second dimension value.
- Step 1225 performing eigenvalue decomposition on the second correlation matrix to obtain a fifth eigenvector and a sixth eigenvector, and a fifth eigenvalue corresponding to the fifth eigenvector and corresponding to the sixth eigenvector.
- a sixth eigenvalue wherein the fifth eigenvalue is a maximum eigenvalue of a second equivalent channel matrix of the second dimension, and the sixth eigenvalue is a submaximum feature of the second equivalent channel matrix of the second dimension value.
- the horizontal dimension feature vector is calculated: EVD decomposition is performed on the first correlation matrix and the second correlation matrix, respectively, and EVD decomposition is performed.
- the step 13 includes the following steps.
- Step 131 Compare a third feature value corresponding to the third feature vector, a fourth feature value corresponding to the fourth feature vector, a fifth feature value corresponding to the fifth feature vector, and a sixth feature value corresponding to the sixth feature vector, The maximum eigenvalue of the second dimension and the second largest eigenvalue of the second dimension are determined.
- Step 132 Determine, according to the maximum feature value of the second dimension and the second largest feature value of the second dimension, a first target feature vector of the first dimension corresponding to the maximum feature value of the second dimension, and a second target feature vector of the first dimension corresponding to the second largest eigenvalue of the second dimension, a third target feature vector of the second dimension corresponding to the maximum eigenvalue of the second dimension, and the second dimension The fourth target feature vector of the second dimension corresponding to the next largest eigenvalue.
- Step 133 Determine a first three-dimensional feature vector according to the third target feature vector and the first target feature vector.
- Step 134 Determine a second three-dimensional feature vector according to the fourth target feature vector and the second target feature vector.
- step 13 the purpose of step 13 is to search for the two largest and second largest shaped feature vectors to determine the three-dimensional feature vector. Specific, comparison with Find the number of the two largest and the second largest eigenvalues corresponding to the largest and second largest eigenvalues with among them, with Number based on the two largest and the second largest eigenvalues with
- the two horizontal dimensional principal eigenvectors of the kth SRS user qth BfB shaped block can be obtained as the first target eigenvector And second target feature vector And passed with Find the third dimension feature vector of its vertical dimension feature vector And fourth target feature vector
- the two horizontal dimensional main feature vectors are respectively combined with the two vertical dimensional feature vectors to form a three-dimensional feature vector.
- the step 14 includes: step 141, performing single-flow beamforming according to the first three-dimensional feature vector; or, step 142, according to the first The three-dimensional feature vector and the second three-dimensional feature vector perform dual-flow beamforming.
- the stream number matching method can adopt the ZF method.
- the complexity of the feature decomposition of the large-dimension channel correlation matrix is solved by two-level feature decomposition, and the beamforming vector including the complete three-dimensional channel information is obtained; Since the channel matrix is analyzed from the horizontal dimension and the vertical dimension, the beamforming method can not only utilize horizontal dimension expansion, but also utilize vertical dimension expansion, and flexible adaptation, further solving all antenna ports in the vertical direction.
- the coverage angle range is less than the problem of full 3D shaped transmission, enabling more accurate 3D beam transmission.
- the second embodiment of the present disclosure provides a beamforming device, including: a matrix acquiring module 21, configured to acquire an uplink channel of a user equipment that transmits a sounding reference signal. a channel matrix; a vector obtaining module 22, configured to separately acquire the channel matrix a first dimension feature vector and a second dimension feature vector; a determining module 23, configured to determine a three-dimensional feature vector according to the first dimension feature vector and the second dimension feature vector; and a shaping module 24, configured to The three-dimensional feature vector performs beamforming.
- a matrix acquiring module 21 configured to acquire an uplink channel of a user equipment that transmits a sounding reference signal.
- a channel matrix configured to separately acquire the channel matrix a first dimension feature vector and a second dimension feature vector
- a determining module 23 configured to determine a three-dimensional feature vector according to the first dimension feature vector and the second dimension feature vector
- a shaping module 24 configured to The three-dimensional feature vector performs beamforming.
- the matrix obtaining module 21 includes: a first matrix acquiring submodule, configured to receive a sounding reference signal sent by the user equipment by using the uplink channel: a second matrix acquiring submodule, And configured to acquire a channel matrix of the uplink channel according to the sounding reference signal.
- the vector obtaining module 22 includes: a first vector obtaining sub-module, configured to acquire a first feature vector and a second feature vector of a titanium-plated dimension of the channel matrix; And a second vector acquisition submodule, configured to acquire a third feature vector, a fourth feature vector, a fifth feature vector, and a sixth feature vector of the second dimension according to the first feature vector and the second feature vector.
- the determining module 23 includes: a first determining submodule, configured to compare a third feature value corresponding to the third feature vector, and a fourth feature corresponding to the fourth feature vector a value, a fifth feature value corresponding to the fifth feature vector, and a sixth feature value corresponding to the sixth feature vector, determining a maximum feature value of the second dimension and a second largest feature value of the second dimension; and a second determining submodule, configured to: Determining, according to the maximum feature value of the second dimension and the second largest feature value of the second dimension, a first target feature vector of the first dimension corresponding to the maximum feature value of the second dimension, and the second a second target feature vector of the first dimension corresponding to the sub-maximum feature value of the dimension, a third target feature vector of the second dimension corresponding to the maximum feature value of the second dimension, and a sub-maximum feature of the second dimension a fourth target feature vector of the second dimension corresponding to the value; a third determining submodule, configured to compare a third feature value
- the shaping module 24 includes: a first shaping sub-module, configured to perform single-flow beamforming according to the first three-dimensional feature vector; or And a sub-module configured to perform dual-stream beamforming according to the first three-dimensional feature vector and the second three-dimensional feature vector.
- the first vector acquisition submodule includes: An acquiring unit, configured to acquire a first dimension correlation matrix of the channel matrix, and a first decomposition unit, configured to perform eigenvalue decomposition on the first dimension correlation matrix to obtain a first feature vector and a second feature vector, and a first feature value corresponding to the first feature vector and a second feature value corresponding to the second feature vector; wherein the first feature value is a maximum feature value of the first dimension, and the second feature value is Is the second largest eigenvalue of the first dimension.
- the second vector acquisition sub-module includes: a first construction unit, configured to construct a first equivalent channel matrix of the second dimension according to the first feature vector; a second constructing unit, configured to construct a second equivalent channel matrix of the second dimension according to the second feature vector, and a second acquiring unit, configured to acquire a first correlation of the first equivalent channel matrix of the second dimension a matrix and a second correlation matrix of the second equivalent channel matrix of the second dimension; a second decomposition unit configured to perform eigenvalue decomposition on the first correlation matrix to obtain a third eigenvector and a fourth eigenvector, And a third feature value corresponding to the third feature vector and a fourth feature value corresponding to the fourth feature vector; wherein the third feature value is a maximum of a first equivalent channel matrix of the second dimension An eigenvalue, the fourth eigenvalue is a sub-maximum eigenvalue of the first equivalent channel matrix of the second dimension; and a third decomposing unit is configured to
- the apparatus for beamforming provided by the second embodiment of the present disclosure is a device that applies the method for beamforming, and all embodiments of the beamforming method are applicable to the device, and both can be achieved. The same or similar benefits.
- a third embodiment of the present disclosure further provides a beamforming device, including: a processor; and a memory connected to the processor through a bus interface, the memory for storing
- the program and data used by the processor when performing the operation when the processor calls and executes the program and data stored in the memory, implements the following functional module: a matrix acquisition module, configured to acquire a user device to transmit a probe a channel matrix of an uplink channel of the reference signal; a vector obtaining module, configured to respectively acquire a first dimension feature vector and a second dimension feature vector of the channel matrix; and a determining module, configured to use the first dimension feature vector and the second Dimension feature vector, determine three a dimension feature vector; and a shaping module for performing beamforming based on the three-dimensional feature vector.
- a matrix acquisition module configured to acquire a user device to transmit a probe a channel matrix of an uplink channel of the reference signal
- a vector obtaining module configured to respectively acquire a first dimension feature vector and a second dimension feature vector of the channel matrix
- the apparatus for beamforming provided by the third embodiment of the present disclosure is a device that applies the method for beamforming, and all embodiments of the beamforming method are applicable to the device, and both can be achieved. The same or similar benefits.
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Abstract
本公开提供一种波束赋形的方法及装置。该波束赋形的方法包括:获取用户设备发射探测参考信号的上行信道的信道矩阵;分别获取所述信道矩阵的第一维特征向量和第二维特征向量;根据所述第一维特征向量和第二维特征向量,确定三维特征向量;根据所述三维特征向量进行波束赋形。
Description
相关申请的交叉引用
本申请主张在2015年11月5日在中国提交的中国专利申请号No.201510746940.X的优先权,其全部内容通过引用包含于此。
本公开涉及通信技术领域,特别涉及一种波束赋形的方法及装置。
鉴于多入多出技术(MIMO技术)对于提高峰值速率与系统频谱利用率的重要作用,LTE(Long Term Evolution,长期演进)/LTE-A(LTE-Advanced,LTE的演进)等无线接入技术标准都是以MIMO+OFDM(Orthogonal Frequency Division Multiplexing,正交频分复用)技术为基础构建起来的。MIMO技术的性能增益来自与多天线系统所能获得的空间自由度,利用空间自由度获得更大的数据传输。因此MIMO技术在标准化过程中的一个最重要的演进方向便是维度的扩展。
为了进一步提升MIMO技术,移动通信系统中引入大规模天线技术。对于全数字化的大规模天线有高达128、256、512个天线振子,以及高达128、256、512个收发信机,每个天线振子连接一个收发信机,具有高达128、256、512个数字天线端口。要充分利用高达128、256、512个数字天线端口的空间自由度。要使得基站在波束赋形时充分利用高达128、256、512个数字天线端口所对应的空间信道信息,对于时分双工TDD模式,则需要利用上行Sounding Reference Signal(SRS)信号测得高达128、256、512个数字天线端口所对应的空间信道信息,并进行特征值分解,以获取波束赋形的赋形向量。但是高达128x128、256x256、512x512维度的协方差矩阵的特征分解的复杂度极高。
目前对于大规模天线而言,在基站侧的利用特征分解的方法计算波束赋形的方法,通常采用整体三维空间信道进行特征分解的方法。此方法可以获得完整的信道特征向量,此完整的信道特征向量不仅包括垂直方向可能的多流,也包括了水平方向可能的多流。但此方法所带来的特征向量分解的复杂
度过大,基站在代码实现时难以完成。
发明内容
本公开的目的在于提供一种波束赋形的方法及装置,解决了现有技术中利用特征分解的方法计算波束赋形带来的特征向量分解的复杂度过大,基站在代码实现时难以完成的问题。
为了达到上述目的,本公开实施例提供一种波束赋形的方法,包括:获取用户设备发射探测参考信号的上行信道的信道矩阵;分别获取所述信道矩阵的第一维特征向量和第二维特征向量;根据所述第一维特征向量和第二维特征向量,确定三维特征向量;根据所述三维特征向量进行波束赋形。
其中,所述获取用户设备发射探测参考信号的上行信道的信道矩阵的步骤包括:接收用户设备通过所述上行信道发送的探测参考信号:根据所述探测参考信号,获取所述上行信道的信道矩阵。
其中,所述分别获取所述信道矩阵的第一维特征向量和第二维特征向量的步骤包括:获取所述信道矩阵的第一维的第一特征向量和第二特征向量;根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量。
其中,所述根据所述第一维特征向量和第二维特征向量,确定三维特征向量的步骤包括:比较所述第三特征向量对应的第三特征值、第四特征向量对应的第四特征值、第五特征向量对应的第五特征值以及第六特征向量对应的第六特征值,确定第二维的最大特征值和第二维的次最大特征值;根据所述第二维的最大特征值和所述第二维的次最大特征值,确定与所述第二维的最大特征值对应的第一维的第一目标特征向量,与所述第二维的次最大特征值对应的第一维的第二目标特征向量、与所述第二维的最大特征值对应的第二维的第三目标特征向量以及与所述第二维的次最大特征值对应的第二维的第四目标特征向量;根据所述第三目标特征向量和所述第一目标特征向量,确定第一三维特征向量;根据所述第四目标特征向量和所述第二目标特征向量,确定第二三维特征向量。
其中,所述根据所述三维特征向量进行波束赋形的步骤包括:根据所述第一三维特征向量,进行单流波束赋形;或者,根据所述第一三维特征向量
和所述第二三维特征向量,进行双流波束赋形。
其中,所述获取所述信道矩阵的第一维的第一特征向量和第二特征向量的步骤包括:获取所述信道矩阵的第一维相关矩阵;对所述第一维相关矩阵进行特征值分解,得到第一特征向量和第二特征向量,以及与所述第一特征向量对应的第一特征值和与所述第二特征向量对应的第二特征值;其中,所述第一特征值为第一维的最大特征值,所述第二特征值为第一维的次最大特征值。
其中,所述根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量的步骤包括:根据所述第一特征向量,构建第二维的第一等效信道矩阵;根据所述第二特征向量,构建第二维的第二等效信道矩阵;获取所述第二维的第一等效信道矩阵的第一相关矩阵以及所述第二维的第二等效信道矩阵的第二相关矩阵;对所述第一相关矩阵进行特征值分解,得到第三特征向量和第四特征向量,以及与所述第三特征向量对应的第三特征值和与所述第四特征向量对应的第四特征值;其中,所述第三特征值为第二维的第一等效信道矩阵的最大特征值,所述第四特征值为第二维的第一等效信道矩阵的次最大特征值;对所述第二相关矩阵进行特征值分解,得到第五特征向量和第六特征向量,以及与所述第五特征向量对应的第五特征值和与所述第六特征向量对应的第六特征值;其中,所述第五特征值为第二维的第二等效信道矩阵的最大特征值,所述第六特征值为第二维的第二等效信道矩阵的次最大特征值。
本公开实施例还提供一种波束赋形的装置,包括:矩阵获取模块,用于获取用户设备发射探测参考信号的上行信道的信道矩阵;向量获取模块,用于分别获取所述信道矩阵的第一维特征向量和第二维特征向量;确定模块,用于根据所述第一维特征向量和第二维特征向量,确定三维特征向量;赋形模块,用于根据所述三维特征向量进行波束赋形。
其中,所述矩阵获取模块包括:第一矩阵获取子模块,用于接收用户设备通过所述上行信道发送的探测参考信号:第二矩阵获取子模块,用于根据所述探测参考信号,获取所述上行信道的信道矩阵。
其中,所述向量获取模块包括:第一向量获取子模块,用于获取所述信
道矩阵的第一维的第一特征向量和第二特征向量;第二向量获取子模块,用于根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量。
其中,所述确定模块包括:第一确定子模块,用于比较所述第三特征向量对应的第三特征值、第四特征向量对应的第四特征值、第五特征向量对应的第五特征值以及第六特征向量对应的第六特征值,确定第二维的最大特征值和第二维的次最大特征值;第二确定子模块,用于根据所述第二维的最大特征值和所述第二维的次最大特征值,确定与所述第二维的最大特征值对应的第一维的第一目标特征向量,与所述第二维的次最大特征值对应的第一维的第二目标特征向量、与所述第二维的最大特征值对应的第二维的第三目标特征向量以及与所述第二维的次最大特征值对应的第二维的第四目标特征向量;第三确定子模块,用于根据所述第三目标特征向量和所述第一目标特征向量,确定第一三维特征向量;第四确定子模块,用于根据所述第四目标特征向量和所述第二目标特征向量,确定第二三维特征向量。
其中,所述赋形模块包括:第一赋形子模块,用于根据所述第一三维特征向量,进行单流波束赋形;或者第二赋形子模块,用于根据所述第一三维特征向量和所述第二三维特征向量,进行双流波束赋形。
其中,所述第一向量获取子模块包括:第一获取单元,用于获取所述信道矩阵的第一维相关矩阵;第一分解单元,用于对所述第一维相关矩阵进行特征值分解,得到第一特征向量和第二特征向量,以及与所述第一特征向量对应的第一特征值和与所述第二特征向量对应的第二特征值;其中,所述第一特征值为第一维的最大特征值,所述第二特征值为第一维的次最大特征值。
其中,所述第二向量获取子模块包括:第一构建单元,用于根据所述第一特征向量,构建第二维的第一等效信道矩阵;第二构建单元,用于根据所述第二特征向量,构建第二维的第二等效信道矩阵;第二获取单元,用于获取所述第二维的第一等效信道矩阵的第一相关矩阵以及所述第二维的第二等效信道矩阵的第二相关矩阵;第二分解单元,用于对所述第一相关矩阵进行特征值分解,得到第三特征向量和第四特征向量,以及与所述第三特征向量对应的第三特征值和与所述第四特征向量对应的第四特征值;其中,所述第
三特征值为第二维的第一等效信道矩阵的最大特征值,所述第四特征值为第二维的第一等效信道矩阵的次最大特征值;第三分解单元,用于对所述第二相关矩阵进行特征值分解,得到第五特征向量和第六特征向量,以及与所述第五特征向量对应的第五特征值和与所述第六特征向量对应的第六特征值;其中,所述第五特征值为第二维的第二等效信道矩阵的最大特征值,所述第六特征值为第二维的第二等效信道矩阵的次最大特征值。
本公开实施例还提供一种波束赋形的装置,包括:处理器;以及通过总线接口与所述处理器相连接的存储器,所述存储器用于存储所述处理器在执行操作时所使用的程序和数据,当处理器调用并执行所述存储器中所存储的程序和数据时,实现如下的功能模块:矩阵获取模块,用于获取用户设备发射探测参考信号的上行信道的信道矩阵;向量获取模块,用于分别获取所述信道矩阵的第一维特征向量和第二维特征向量;确定模块,用于根据所述第一维特征向量和第二维特征向量,确定三维特征向量;赋形模块,用于根据所述三维特征向量进行波束赋形。
本公开的上述技术方案至少具有如下有益效果:本公开实施例的波束赋形的方法及装置中,通过垂直维和水平维的两级特征分解,获得包括完整三维信道信息的波束赋形向量,实现更精确的3D波束传输;同时解决了大维度信道相关矩阵的特征分解的复杂度。
图1表示本公开的第一实施例提供的波束赋形的方法的基本流程图;
图2表示本公开的第二实施例提供的波束赋形的装置的结构示意图。
为使本公开要解决的技术问题、技术方案和优点更加清楚,下面将结合附图及具体实施例进行详细描述。
第一实施例
如图1所示,本公开的第一实施例提供一种波束赋形的方法,包括:
步骤11,获取用户设备发射探测参考信号的上行信道的信道矩阵;
步骤12,分别获取所述信道矩阵的第一维特征向量和第二维特征向量;
步骤13,根据所述第一维特征向量和第二维特征向量,确定三维特征向
量;
步骤14,根据所述三维特征向量进行波束赋形。
本公开的上述实施例中,上述上行信道为用户设备UE发射探测参考信号(SRS信号)的信道,则通过上述SRS信号能够计算得到该上行信道的信道矩阵。对该信道矩阵直接进行特征分解的复杂度较大,很难实现。故本公开的第一实施例中从两个维度对信道矩阵进行分析,得到第一维特征向量和第二维特征向量,从而分别对第一维特征向量和第二维特征向量进行特征分解,实现两级特征分解,降低特征分解的难度。进而得到三维特征向量,该三为特征向量为包括保证三维信道信息的波束赋形向量,最后根据得到的三维特征向量进行波束赋形,实现更精确的3D波束传输。
需要说明的是,本公开实施例中,第一维为垂直维或水平维,相应的,第二维为水平维或垂直维。为了更清楚的描述本申请的内容,以下以第一维为垂直维,第二维为水平维来进行具体描述。但是对于第一维为水平维,第二维为垂直维的场景仍属于本申请的保护范围。
同时由于从水平维和垂直维两方面对信道矩阵进行分析,使得该波束赋形的方法不仅可利用水平维角度扩展,也可利用垂直维角度扩展,灵活自适应,进一步解决了垂直方向所有天线端口的覆盖角度范围不足完整3D赋形传输的问题。
具体的,本公开的第一实施例中步骤11包括:步骤111,接收用户设备通过所述上行信道发送的探测参考信号:步骤112,根据所述探测参考信号,获取所述上行信道的信道矩阵。
用户设备UE发射SRS信号(探测参考信号),基站根据所述SRS信号发送该SRS信号的上行信道的信道矩阵。即假设基站接收KSRS个用户的PSRS个天线端口所发射的SRS信号,通过SRS信号计算出第kSRS(kSRS=0,…,KSRS-1)个用户的第pSRS(pSRS=0,…,PSRS-1)个SRS端口的第个子载波上的基站天线上行信道是一个NV×NH矩阵。其中,NV对应大规模天线在垂直方向的NV行,NH对应大规模天线在水平方向的NH列,NRB是系统带宽内的RB(resource block,资源块)数,是一个资源块RB内的子载波数。
进一步的,本公开的第一实施例中步骤12包括:步骤121,获取所述信道矩阵的第一维的第一特征向量和第二特征向量;步骤122,根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量。
且步骤121包括:步骤1211,获取所述信道矩阵的第一维相关矩阵;步骤1212,对所述第一维相关矩阵进行特征值分解,得到第一特征向量和第二特征向量,以及与所述第一特征向量对应的第一特征值和与所述第二特征向量对应的第二特征值;其中,所述第一特征值为第一维的最大特征值,所述第二特征值为第一维的次最大特征值。
需要说明的是,本公开实施例中,第一维为垂直维或水平维,相应的,第二维为水平维或垂直维。为了更清楚的描述本申请的内容,以下以第一维为垂直维,第二维为水平维来进行具体描述。但是对于第一维为水平维,第二维为垂直维的场景仍属于本申请的保护范围,以下不再进行重复说明。
具体的,步骤1211中,获取第一维(例如第一维为垂直维)相关矩阵的具体步骤为:
计算垂直维相关矩阵,为第qBfB(qBfB=0,1,…,QBfB-1)赋形块计算计算垂直维相关矩阵。其中,将系统带宽内个子载波分为QBfB个赋形块(BfB),每个赋形块内有个子载波,每个赋形块子载波采用个抽样子载波计算垂直维相关矩阵,则抽样的间隔为这时子载波编号n与第qBfB(qBfB=0,1,…,QBfB-1)个赋形块内第个抽样子载波之间关系表示为:
其中,
qBfB=0,1,…,QBfB-1
较佳的,步骤1212中对垂直维相关矩阵进行特征值分解的具体步骤为:计算垂直维特征向量:进行EVD(Eigenvalue Decomposition)分解得到计算第kSRS个用户的第qBfB个赋形块的的两个垂直维主特征向量,即第一特征向量和第二特征向量以及相应的两个特征值,即第一特征值和第二特征值其中第一特征值和第二特征值分别对应最大和次最大的两个特征值;则第一特征向量和第二特征向量是最大和次最大的两个特征值和所对应的特征向量。其中,和分别是NV×1矩阵。
在步骤121获得第一特征向量和第二特征向量的基础上,本公开的第一实施例中步骤122包括以下步骤。
步骤1221,根据所述第一特征向量,构建第二维的第一等效信道矩阵。
步骤1222,根据所述第二特征向量,构建第二维的第二等效信道矩阵。
其中,
qBfB=0,1,…,QBfB-1
步骤1223,获取所述第二维的第一等效信道矩阵的第一相关矩阵以及所述第二维的第二等效信道矩阵的第二相关矩阵。即分别计算两个水平维等效信道的相关矩阵。计算第kSRS个用户第qBfB赋形块的第一水平等效信道和第二
水平等效信道的相关矩阵:则
其中,
qBfB=0,1,…,QBfB-1
步骤1224,对所述第一相关矩阵进行特征值分解,得到第三特征向量和第四特征向量,以及与所述第三特征向量对应的第三特征值和与所述第四特征向量对应的第四特征值;其中,所述第三特征值为第二维的第一等效信道矩阵的最大特征值,所述第四特征值为第二维的第一等效信道矩阵的次最大特征值。
步骤1225,对所述第二相关矩阵进行特征值分解,得到第五特征向量和第六特征向量,以及与所述第五特征向量对应的第五特征值和与所述第六特征向量对应的第六特征值;其中,所述第五特征值为第二维的第二等效信道矩阵的最大特征值,所述第六特征值为第二维的第二等效信道矩阵的次最大特征值。
对进行特征分解,得到计算第kSRS个用户的第qBfB个赋形块的的两个垂直维主特征向量为第三特征向量和第四特征向量以及相应的两个特征值,第三特征值和第四特征值其中第三特征值和第四特征值分别对应最大和次最大的两个特征值。则第三特征向量
和第四特征向量是最大和次最大的两个特征值和所对应的特征向量。其中,第三特征向量和第四特征向量分别是NH×1矩阵。
对进行特征分解,得到计算第kSRS个用户的第qBfB个赋形块的的两个垂直维主特征向量为第五特征向量和第六特征向量以及相应的两个特征值,第五特征值和第六特征值其中第五特征值和第六特征值分别对应最大和次最大的两个特征值。第五特征向量和第六特征向量是最大和次最大的两个特征值和所对应的特征向量。其中,第五特征向量和第六特征向量分别是NH×1矩阵。
进一步的,本公开的第一实施例中确定第一特征向量至第六特征向量后,步骤13包括以下步骤。
步骤131,比较所述第三特征向量对应的第三特征值、第四特征向量对应的第四特征值、第五特征向量对应的第五特征值以及第六特征向量对应的第六特征值,确定第二维的最大特征值和第二维的次最大特征值。
步骤132,根据所述第二维的最大特征值和所述第二维的次最大特征值,确定与所述第二维的最大特征值对应的第一维的第一目标特征向量,与所述第二维的次最大特征值对应的第一维的第二目标特征向量、与所述第二维的最大特征值对应的第二维的第三目标特征向量以及与所述第二维的次最大特征值对应的第二维的第四目标特征向量。
步骤133,根据所述第三目标特征向量和所述第一目标特征向量,确定第一三维特征向量。
步骤134,根据所述第四目标特征向量和所述第二目标特征向量,确定第二三维特征向量。
即步骤13的目的为搜索最大和次最大的两个赋形特征向量,从而确定三维特征向量。具体的,比较和找到其中最大和次大特征值所对应的最大和次最大的两个特征值的编号和其中,和根据最大和次最大的两个特征值的编号和可以得到计算第kSRS个用户第qBfB赋形块的两个水平
维主特征向量为第一目标特征向量和第二目标特征向量并通过和找到其垂直维特征向量第三目标特征向量和第四目标特征向量将两个水平维主特征向量分别与两个垂直维特征向量合成三维特征向量。
进一步的,本公开的第一实施例中得到三维特征向量之后其步骤14包括:步骤141,根据所述第一三维特征向量,进行单流波束赋形;或者,步骤142,根据所述第一三维特征向量和所述第二三维特征向量,进行双流波束赋形。
综上,本公开实施例提供的波束赋形的方法中,通过两级特征分解,解决了大维度信道相关矩阵的特征分解的复杂度,获得了包括完整三维信道信息的波束赋形向量;同时由于从水平维和垂直维两方面对信道矩阵进行分析,使得该波束赋形的方法不仅可利用水平维角度扩展,也可利用垂直维角度扩展,灵活自适应,进一步解决了垂直方向所有天线端口的覆盖角度范围不足完整3D赋形传输的问题,实现更精确的3D波束传输。
为了更好的实现上述目的,如图2所示,本公开的第二实施例提供一种波束赋形的装置,包括:矩阵获取模块21,用于获取用户设备发射探测参考信号的上行信道的信道矩阵;向量获取模块22,用于分别获取所述信道矩阵
的第一维特征向量和第二维特征向量;确定模块23,用于根据所述第一维特征向量和第二维特征向量,确定三维特征向量;以及赋形模块24,用于根据所述三维特征向量进行波束赋形。
具体的,本公开的第二实施例中,所述矩阵获取模块21包括:第一矩阵获取子模块,用于接收用户设备通过所述上行信道发送的探测参考信号:第二矩阵获取子模块,用于根据所述探测参考信号,获取所述上行信道的信道矩阵。
具体的,本公开的第二实施例中,所述向量获取模块22包括:第一向量获取子模块,用于获取所述信道矩阵的镀钛维的第一特征向量和第二特征向量;第二向量获取子模块,用于根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量。
具体的,本公开的第二实施例中,所述确定模块23包括:第一确定子模块,用于比较所述第三特征向量对应的第三特征值、第四特征向量对应的第四特征值、第五特征向量对应的第五特征值以及第六特征向量对应的第六特征值,确定第二维的最大特征值和第二维的次最大特征值;第二确定子模块,用于根据所述第二维的最大特征值和所述第二维的次最大特征值,确定与所述第二维的最大特征值对应的第一维的第一目标特征向量,与所述第二维的次最大特征值对应的第一维的第二目标特征向量、与所述第二维的最大特征值对应的第二维的第三目标特征向量以及与所述第二维的次最大特征值对应的第二维的第四目标特征向量;第三确定子模块,用于根据所述第三目标特征向量和所述第一目标特征向量,确定第一三维特征向量;第四确定子模块,用于根据所述第四目标特征向量和所述第二目标特征向量,确定第二三维特征向量。
具体的,本公开的第二实施例中,所述赋形模块24包括:第一赋形子模块,用于根据所述第一三维特征向量,进行单流波束赋形;或者,第二赋形子模块,用于根据所述第一三维特征向量和所述第二三维特征向量,进行双流波束赋形。
具体的,本公开的第二实施例中,所述第一向量获取子模块包括:第一
获取单元,用于获取所述信道矩阵的第一维相关矩阵;第一分解单元,用于对所述第一维相关矩阵进行特征值分解,得到第一特征向量和第二特征向量,以及与所述第一特征向量对应的第一特征值和与所述第二特征向量对应的第二特征值;其中,所述第一特征值为第一维的最大特征值,所述第二特征值为第一维的次最大特征值。
具体的,本公开的第二实施例中,所述第二向量获取子模块包括:第一构建单元,用于根据所述第一特征向量,构建第二维的第一等效信道矩阵;第二构建单元,用于根据所述第二特征向量,构建第二维的第二等效信道矩阵;第二获取单元,用于获取所述第二维的第一等效信道矩阵的第一相关矩阵以及所述第二维的第二等效信道矩阵的第二相关矩阵;第二分解单元,用于对所述第一相关矩阵进行特征值分解,得到第三特征向量和第四特征向量,以及与所述第三特征向量对应的第三特征值和与所述第四特征向量对应的第四特征值;其中,所述第三特征值为第二维的第一等效信道矩阵的最大特征值,所述第四特征值为第二维的第一等效信道矩阵的次最大特征值;第三分解单元,用于对所述第二相关矩阵进行特征值分解,得到第五特征向量和第六特征向量,以及与所述第五特征向量对应的第五特征值和与所述第六特征向量对应的第六特征值;其中,所述第五特征值为第二维的第二等效信道矩阵的最大特征值,所述第六特征值为第二维的第二等效信道矩阵的次最大特征值。
需要说明的是,本公开第二实施例提供的波束赋形的装置是应用上述波束赋形的方法的装置,则上述波束赋形的方法的所有实施例均适用于该装置,且均能达到相同或相似的有益效果。
为了更好的实现上述目的,本公开的第三实施例还提供一种波束赋形的装置,包括:处理器;以及通过总线接口与所述处理器相连接的存储器,所述存储器用于存储所述处理器在执行操作时所使用的程序和数据,当处理器调用并执行所述存储器中所存储的程序和数据时,实现如下的功能模块:矩阵获取模块,用于获取用户设备发射探测参考信号的上行信道的信道矩阵;向量获取模块,用于分别获取所述信道矩阵的第一维特征向量和第二维特征向量;确定模块,用于根据所述第一维特征向量和第二维特征向量,确定三
维特征向量;以及赋形模块,用于根据所述三维特征向量进行波束赋形。
需要说明的是,本公开第三实施例提供的波束赋形的装置是应用上述波束赋形的方法的装置,则上述波束赋形的方法的所有实施例均适用于该装置,且均能达到相同或相似的有益效果。
以上所述是本公开的可选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本公开所述原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本公开的保护范围。
Claims (15)
- 一种波束赋形的方法,包括:获取用户设备发射探测参考信号的上行信道的信道矩阵;分别获取所述信道矩阵的第一维特征向量和第二维特征向量;根据所述第一维特征向量和第二维特征向量,确定三维特征向量;根据所述三维特征向量进行波束赋形。
- 根据权利要求1所述的波束赋形的方法,其中,所述获取用户设备发射探测参考信号的上行信道的信道矩阵的步骤包括:接收用户设备通过所述上行信道发送的探测参考信号:根据所述探测参考信号,获取所述上行信道的信道矩阵。
- 根据权利要求1所述的波束赋形的方法,其中,所述分别获取所述信道矩阵的第一维特征向量和第二维特征向量的步骤包括:获取所述信道矩阵的第一维的第一特征向量和第二特征向量;根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量。
- 根据权利要求3所述的波束赋形的方法,其中,所述根据所述第一维特征向量和第二维特征向量,确定三维特征向量的步骤包括:比较所述第三特征向量对应的第三特征值、第四特征向量对应的第四特征值、第五特征向量对应的第五特征值以及第六特征向量对应的第六特征值,确定第二维的最大特征值和第二维的次最大特征值;根据所述第二维的最大特征值和所述第二维的次最大特征值,确定与所述第二维的最大特征值对应的第一维的第一目标特征向量,与所述第二维的次最大特征值对应的第一维的第二目标特征向量、与所述第二维的最大特征值对应的第二维的第三目标特征向量以及与所述第二维的次最大特征值对应的第二维的第四目标特征向量;根据所述第三目标特征向量和所述第一目标特征向量,确定第一三维特征向量;根据所述第四目标特征向量和所述第二目标特征向量,确定第二三维特 征向量。
- 根据权利要求4所述的波束赋形的方法,其中,所述根据所述三维特征向量进行波束赋形的步骤包括:根据所述第一三维特征向量,进行单流波束赋形;或者根据所述第一三维特征向量和所述第二三维特征向量,进行双流波束赋形。
- 根据权利要求3或5所述的波束赋形的方法,其中,所述获取所述信道矩阵的第一维的第一特征向量和第二特征向量的步骤包括:获取所述信道矩阵的第一维相关矩阵;对所述第一维相关矩阵进行特征值分解,得到第一特征向量和第二特征向量,以及与所述第一特征向量对应的第一特征值和与所述第二特征向量对应的第二特征值;其中,所述第一特征值为第一维的最大特征值,所述第二特征值为第一维的次最大特征值。
- 根据权利要求6所述的波束赋形的方法,其中,所述根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量的步骤包括:根据所述第一特征向量,构建第二维的第一等效信道矩阵;根据所述第二特征向量,构建第二维的第二等效信道矩阵;获取所述第二维的第一等效信道矩阵的第一相关矩阵以及所述第二维的第二等效信道矩阵的第二相关矩阵;对所述第一相关矩阵进行特征值分解,得到第三特征向量和第四特征向量,以及与所述第三特征向量对应的第三特征值和与所述第四特征向量对应的第四特征值;其中,所述第三特征值为第二维的第一等效信道矩阵的最大特征值,所述第四特征值为第二维的第一等效信道矩阵的次最大特征值;对所述第二相关矩阵进行特征值分解,得到第五特征向量和第六特征向量,以及与所述第五特征向量对应的第五特征值和与所述第六特征向量对应的第六特征值;其中,所述第五特征值为第二维的第二等效信道矩阵的最大特征值,所述第六特征值为第二维的第二等效信道矩阵的次最大特征值。
- 一种波束赋形的装置,包括:矩阵获取模块,用于获取用户设备发射探测参考信号的上行信道的信道矩阵;向量获取模块,用于分别获取所述信道矩阵的第一维特征向量和第二维特征向量;确定模块,用于根据所述第一维特征向量和第二维特征向量,确定三维特征向量;赋形模块,用于根据所述三维特征向量进行波束赋形。
- 根据权利要求8所述的波束赋形的装置,其中,所述矩阵获取模块包括:第一矩阵获取子模块,用于接收用户设备通过所述上行信道发送的探测参考信号:第二矩阵获取子模块,用于根据所述探测参考信号,获取所述上行信道的信道矩阵。
- 根据权利要求8所述的波束赋形的装置,其中,所述向量获取模块包括:第一向量获取子模块,用于获取所述信道矩阵的第一维的第一特征向量和第二特征向量;第二向量获取子模块,用于根据所述第一特征向量和所述第二特征向量,获取第二维的第三特征向量、第四特征向量、第五特征向量以及第六特征向量。
- 根据权利要求10所述的波束赋形的装置,其中,所述确定模块包括:第一确定子模块,用于比较所述第三特征向量对应的第三特征值、第四特征向量对应的第四特征值、第五特征向量对应的第五特征值以及第六特征向量对应的第六特征值,确定第二维的最大特征值和第二维的次最大特征值;第二确定子模块,用于根据所述第二维的最大特征值和所述第二维的次最大特征值,确定与所述第二维的最大特征值对应的第一维的第一目标特征向量,与所述第二维的次最大特征值对应的第一维的第二目标特征向量、与所述第二维的最大特征值对应的第二维的第三目标特征向量以及与所述第二维的次最大特征值对应的第二维的第四目标特征向量;第三确定子模块,用于根据所述第三目标特征向量和所述第一目标特征向量,确定第一三维特征向量;第四确定子模块,用于根据所述第四目标特征向量和所述第二目标特征向量,确定第二三维特征向量。
- 根据权利要求11所述的波束赋形的装置,其中,所述赋形模块包括:第一赋形子模块,用于根据所述第一三维特征向量,进行单流波束赋形;或者第二赋形子模块,用于根据所述第一三维特征向量和所述第二三维特征向量,进行双流波束赋形。
- 根据权利要求10或12所述的波束赋形的装置,其中,所述第一向量获取子模块包括:第一获取单元,用于获取所述信道矩阵的第一维相关矩阵;第一分解单元,用于对所述第一维相关矩阵进行特征值分解,得到第一特征向量和第二特征向量,以及与所述第一特征向量对应的第一特征值和与所述第二特征向量对应的第二特征值;其中,所述第一特征值为第一维的最大特征值,所述第二特征值为第一维的次最大特征值。
- 根据权利要求13所述的波束赋形的装置,其中,所述第二向量获取子模块包括:第一构建单元,用于根据所述第一特征向量,构建第二维的第一等效信道矩阵;第二构建单元,用于根据所述第二特征向量,构建第二维的第二等效信道矩阵;第二获取单元,用于获取所述第二维的第一等效信道矩阵的第一相关矩阵以及所述第二维的第二等效信道矩阵的第二相关矩阵;第二分解单元,用于对所述第一相关矩阵进行特征值分解,得到第三特征向量和第四特征向量,以及与所述第三特征向量对应的第三特征值和与所述第四特征向量对应的第四特征值;其中,所述第三特征值为第二维的第一等效信道矩阵的最大特征值,所述第四特征值为第二维的第一等效信道矩阵的次最大特征值;第三分解单元,用于对所述第二相关矩阵进行特征值分解,得到第五特征向量和第六特征向量,以及与所述第五特征向量对应的第五特征值和与所述第六特征向量对应的第六特征值;其中,所述第五特征值为第二维的第二等效信道矩阵的最大特征值,所述第六特征值为第二维的第二等效信道矩阵的次最大特征值。
- 一种波束赋形的装置,包括:处理器;以及通过总线接口与所述处理器相连接的存储器,所述存储器用于存储所述处理器在执行操作时所使用的程序和数据,当处理器调用并执行所述存储器中所存储的程序和数据时,实现如下的功能模块:矩阵获取模块,用于获取用户设备发射探测参考信号的上行信道的信道矩阵;向量获取模块,用于分别获取所述信道矩阵的第一维特征向量和第二维特征向量;确定模块,用于根据所述第一维特征向量和第二维特征向量,确定三维特征向量;赋形模块,用于根据所述三维特征向量进行波束赋形。
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US20060217075A1 (en) * | 2005-03-25 | 2006-09-28 | Kyocera Corporation | Wireless communication method, wireless communication system, and wireless communication device |
CN101640583A (zh) * | 2008-07-31 | 2010-02-03 | 鼎桥通信技术有限公司 | 一种发射预处理的方法 |
CN104283631A (zh) * | 2013-07-05 | 2015-01-14 | 株式会社Ntt都科摩 | 生成用于三维mimo系统的预编码矩阵的方法和装置以及发射机 |
Family Cites Families (8)
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CN102412885B (zh) * | 2011-11-25 | 2015-05-06 | 西安电子科技大学 | Lte中的三维波束赋形方法 |
CN103152085B (zh) * | 2011-12-06 | 2018-02-02 | 中兴通讯股份有限公司 | 三维波束赋形的权值获取方法和装置 |
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US9042476B2 (en) * | 2013-07-26 | 2015-05-26 | Google Technology Holdings LLC | Methods and a device for multi-resolution precoding matrix indicator feedback |
CN103825678B (zh) * | 2014-03-06 | 2017-03-08 | 重庆邮电大学 | 一种基于Khatri‑Rao积3D MU‑MIMO的预编码方法 |
CN104184690B (zh) * | 2014-09-03 | 2017-04-12 | 西安电子科技大学 | 一种适用于3d mimo系统的双层预编码方法 |
-
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Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060217075A1 (en) * | 2005-03-25 | 2006-09-28 | Kyocera Corporation | Wireless communication method, wireless communication system, and wireless communication device |
CN101640583A (zh) * | 2008-07-31 | 2010-02-03 | 鼎桥通信技术有限公司 | 一种发射预处理的方法 |
CN104283631A (zh) * | 2013-07-05 | 2015-01-14 | 株式会社Ntt都科摩 | 生成用于三维mimo系统的预编码矩阵的方法和装置以及发射机 |
Non-Patent Citations (1)
Title |
---|
TD TECH.: "Text Proposal to TR 25.824 on MIMO Conclusions", 3GPP TSG-RAN WG1 MEETING #53, R1-081938, 9 May 2008 (2008-05-09), XP050110294 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115987346A (zh) * | 2022-12-15 | 2023-04-18 | 华工未来通信(江苏)有限公司 | 一种智能反射面被动波束赋型方法、系统及存储介质 |
CN115987346B (zh) * | 2022-12-15 | 2024-02-02 | 华工未来通信(江苏)有限公司 | 一种智能反射面被动波束赋型方法、系统及存储介质 |
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