WO2017219739A1 - 一种确定波束赋型向量的方法及装置 - Google Patents
一种确定波束赋型向量的方法及装置 Download PDFInfo
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- 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
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- the present application relates to the field of wireless communication technologies, and in particular, to a method and apparatus for determining a beamforming vector.
- duplex modes In the 3GPP standard 4G standard, two duplex modes are defined: FDD (Frequency Division Duplex)-LTE (Long Term Evolution) and TDD (Time Division Duplex)-LTE.
- the TDD-LTE uplink and downlink links adopt the same carrier frequency point, and the uplink and downlink channels can be considered to have reciprocity, which also provides the possibility that the beamforming theory is applied in LTE.
- Massive MIMO Multiple Input Multiple Output
- MU Multiple Users
- Beamforming generally calculates the downlink beamforming vector of the current user based on the uplink channel information detected by the base station.
- the shortcoming of the prior art is that although the vertical and horizontal dimensions of the beamforming scheme are widely used in the industry, and the eigenvector decomposition of the 128-order is directly performed, the complexity is significantly reduced, but compared with the conventional beam assignment. The performance of the type algorithm will be reduced.
- the present application provides a method and apparatus for determining a beamforming vector to provide a high performance scheme for reducing the complexity of beamforming operations.
- a method for determining a beamforming vector including:
- Nr represents the number of receiving antennas of the base station
- Nt represents the number of transmitting antennas of the terminal
- Correlation matrix among them Is a reduced order correlation matrix, and the dimension is the number of terminal antennas;
- the beamforming vector is determined from the average channel information and the output eigenvector matrix U.
- the correlation matrix The averaging process is performed, and the average processed correlation matrix satisfies:
- N is the number of subcarriers in the physical resource block PRB.
- the eigenvector method EBB or the singular value decomposition method SVD pair is adopted.
- Do feature vector decomposition where The shaping to give the number of streams U get, where U is a front GET U of M columns, M being the number of streams excipient.
- the beamforming vector is determined according to the average channel information and the output feature vector matrix U, including:
- the method further comprises:
- the beamforming vector is normalized according to the stream power allocation such that the shaping vector W is normalized to each column.
- An apparatus for determining a beamforming vector is provided in an embodiment of the present application, including
- a listening module configured to detect a channel matrix on the kth subcarrier according to the SRS
- Nr represents the number of receiving antennas of the base station
- Nt represents the number of transmitting antennas of the terminal
- Correlation matrix module for finding correlation matrix among them Is a reduced order correlation matrix, and the dimension is the number of terminal antennas;
- Eigenvector decomposition module for pairing Correlation matrix obtained by averaging Perform eigenvector decomposition to obtain correlation matrix eigenvectors;
- An output matrix module configured to determine an output feature vector matrix U according to the number of streams of the shaping, wherein the number of columns of U is a stream number;
- a beamforming vector module is configured to determine a beamforming vector from the average channel information and the output feature vector matrix U.
- the correlation matrix module is further used for the correlation matrix
- the averaging process is performed, and the average processed correlation matrix satisfies:
- N is the number of subcarriers in the physical resource block PRB.
- the feature vector decomposition module is further used to adopt an EBB or SVD pair Do feature vector decomposition, where The shaping to give the number of streams U get, where U is a front GET U of M columns, M being the number of streams excipient.
- the beamforming vector module is further configured to: when determining the beamforming vector from the average channel information and the output feature vector matrix U:
- the method further comprises:
- a normalization module is used to normalize the beamforming vector according to the flow power allocation such that the shaping vector W is normalized to each column.
- the embodiment of the present application further provides a base station, where the base station includes a processor, a memory, a transceiver, a bus, and a bus interface, wherein the processor, the memory, and the transceiver are connected by a bus, and the bus interface is in a bus and Providing an interface between the transceivers;
- a transceiver for transmitting data under the control of a processor performing the following processes:
- Nr represents the number of receiving antennas of the base station
- Nt represents the number of transmitting antennas of the terminal
- a processor for reading a program in the memory performing the following process:
- Correlation matrix among them Is a reduced order correlation matrix, and the dimension is the number of terminal antennas;
- the beamforming vector is determined from the average channel information and the output eigenvector matrix U.
- the correlation matrix The averaging process is performed, and the average processed correlation matrix satisfies:
- N is the number of subcarriers in the physical resource block PRB.
- the eigenvector method EBB or the singular value decomposition method SVD pair is adopted.
- Do feature vector decomposition where
- the processor determines the wave according to the average channel information and the output feature vector matrix U in the following manner Beam type vector:
- the processor is further configured to:
- the beamforming vector is normalized according to the stream power allocation such that the shaping vector W is normalized to each column.
- the deterministic beamforming scheme proposed in the embodiment of the present application decomposes the traditional high-dimensional eigenvector by the matrix theory, and is equivalent to the low-dimensional eigenvector decomposition, which reduces the complexity of the beamforming operation.
- the communication device can shape the traditional large-grain beam and refine it into a small-grained beamforming, which can reduce the complexity and significantly improve the performance compared with the traditional method.
- 1 is a schematic diagram showing relationship between antenna numbers and actual channel matrices formed in the embodiment of the present application;
- FIG. 2 is a schematic flowchart of a method for implementing a method for determining a beamforming vector in an embodiment of the present application
- FIG. 3 is a schematic structural diagram of an apparatus for determining a beamforming vector in an embodiment of the present application
- FIG. 4 is a schematic structural diagram of a base station in an embodiment of the present application.
- Beamforming generally calculates the downlink beamforming vector of the current user based on the uplink channel information detected by the base station.
- the beamforming algorithms commonly used in the industry are as follows:
- the terminal transmits SRS (Sounding Reference Signals) to listen to the pilot signals in different antennas at different times. After receiving by the base station, the SRS signal is used for channel estimation, and the channels of the different antennas of the terminal to the base station are combined. Since the TDD-LTE uplink and downlink channels are reciprocal, the uplink channel combination is also equivalent to the downlink channel combination. Assuming base station 128 antenna, terminal 4 antenna, the channel combination is expressed as:
- H 4 ⁇ 128 is a 4 ⁇ 128-dimensional matrix
- h 1,k represents the channel from the first antenna of the terminal to the k-th antenna of the base station
- h 2,k represents the second antenna of the terminal to the k-th antenna of the base station. The channel on the other, and so on.
- the base station obtains the correlation matrix of the channel.
- the beamforming vector is represented by W. Then, the shaping vector satisfies (H 4 ⁇ 128 * W) '* (H 4 ⁇ 128 * W) maximum.
- W is the eigenvector of R. Therefore, it is sufficient to perform eigenvector decomposition on R in the implementation.
- R k the correlation matrix
- the PRB (physical resource block) covered by consecutive N subcarriers is called The granularity of beamforming.
- EKB Eigenvalue Based Beamforming
- SVD Single Value Decompostion
- the base station needs to do the eigenvalue decomposition of the 128 ⁇ 128 large matrix. From the engineering point of view, it is difficult to implement itself. In fact, current equipment manufacturers have been conducting extensive research on this issue. The current practice is to implement the above traditional methods step by step.
- FIG. 1 is a schematic diagram of relationship between antenna numbers and actual channel matrices formed by them. For details, refer to FIG. 1 and related related materials.
- the base station antenna is the transmit antenna
- the terminal antenna is the receive antenna
- N H N V N P is the number of base station antennas
- N R is the number of terminal antennas
- the massive MIMO system is N H N V N P ⁇ N R .
- Method 1 Calculate the horizontal direction feature vector u H and the vertical direction feature vector u V respectively , and finally combine the two into a 3D massive MIMO channel vector.
- Step 1 Calculate the vertical direction feature vector within each PU.
- Step 1.1 Calculate the vertical average transmission correlation matrix of the base station antenna to the terminal antenna in the same polarization direction of each column in the PU on all sampled subcarriers:
- Step 1.2 Calculate the vertical dimension average transmission correlation matrix of all base station antenna columns in the PU:
- Step 1.3 Perform eigenvalue decomposition on the vertical dimension average correlation correlation matrix R V in the PU to obtain a plurality of vertical dimensional feature vectors u V,1 , u V,2 ,..., where u V,1 is the main eigenvector, u V , 2 is the secondary feature vector, and so on.
- the corresponding eigenvalues are ⁇ V,1 , ⁇ V,2 ,...
- Step 2 Calculate the horizontal direction feature vector within each PU.
- Step 2.1 Calculate the horizontal average transmission correlation matrix of each row of base station antennas to terminal antennas in the PU on all sampled subcarriers:
- Step 2.2 Calculate the horizontal dimension average transmission correlation matrix of all base station antenna rows in the PU:
- Step 2.3 Perform eigenvalue decomposition on the horizontal dimension average transmission correlation matrix R H in the PU to obtain a plurality of horizontal dimensional feature vectors u H,1 , u H,2 ,..., where u H,1 is the main eigenvector, u H , 2 is the secondary feature vector, and so on.
- the corresponding feature vectors are ⁇ H,1 , ⁇ H,2 ,...
- Step 3 Synthesize a 3D precoding matrix or channel vector.
- Each feature vector in the horizontal direction and the vertical direction constitutes a 3D feature vector, that is, a precoding vector.
- Rank L can be determined by a 3D feature vector corresponding to a maximum of L 3D feature values, such as a 3D precoding matrix of rank 4:
- the corresponding 3D channel matrix is:
- Method 2 Calculate the vertical direction feature vector u V , form an equivalent horizontal channel, calculate the eigenvector u H of the equivalent horizontal channel, and finally combine the two into a 3D massive MIMO channel vector.
- Step 1 Calculate the vertical direction feature vector within each PU.
- Step 1.1 Calculate the vertical average transmission correlation matrix of the base station antenna to the terminal antenna in the same polarization direction of each column in the PU on all sampled subcarriers:
- Step 1.2 Calculate the vertical dimension precoding matrix within the PU.
- All antenna columns of the base station use the same vertical dimension precoding vector.
- Step 1.2.1.1 Calculate the vertical dimension average transmission correlation matrix of all antenna columns of the base station in the PU.
- Step 1.2.1.2 Perform eigenvalue decomposition on the vertical dimension average correlation correlation matrix R V in the PU to obtain a vertical dimension main eigenvector u V,1 , that is, a precoding vector used by the vertical dimension column antennas.
- Step 1.2.1.3 Form an equivalent horizontal dimension channel for each sampled subcarrier of the PU, subcarrier n.
- Step 3 Synthesize the 3D channel vector.
- the feature vectors in the horizontal direction and the vertical direction constitute a 3D feature vector, which is a precoding vector.
- Rank L may be determined by a 3D feature vector corresponding to a maximum of L 3D feature values, such as a 3D precoding matrix matrix of rank 2:
- the inventors obtained the results of Table 1 by comparing the throughput simulation performance of 128 antenna and MU user beamforming:
- Simulation conditions 3D-UMa, AMC on, 10 USER, fixed 2 streams per user, unit Gbps, terminal 2 antenna
- the beamforming vector is a solution that satisfies the maximum solution of W H *R HH *W, that is, the eigenvector of R HH .
- the base station 128 antenna and the terminal 4 antenna will be described as an example for convenience.
- R HH (U ⁇ D H ) H *U ⁇ D H
- ⁇ is a diagonal matrix
- D is a right ⁇ matrix
- the derived vector W is the first few columns of vectors in the right-hand matrix:
- the algorithm using the above idea only needs a 4-dimensional SVD decomposition to find the shape vector.
- an 8th-order feature is needed.
- FIG. 2 is a schematic flowchart of a method for determining a beamforming vector, as shown in the figure, which may include:
- Step 201 Listening to a channel matrix on the kth subcarrier according to the SRS Wherein, Nr represents the number of receiving antennas of the base station, and Nt represents the number of transmitting antennas of the terminal;
- Step 202 Find a correlation matrix among them, Is a reduced order correlation matrix, and the dimension is the number of terminal antennas;
- Step 203 pair Correlation matrix obtained by averaging Perform eigenvector decomposition to obtain correlation matrix eigenvectors;
- Step 204 Determine, according to the number of streams to be shaped, an output feature vector matrix U, where the number of columns of U is a stream number;
- Step 205 Determine a beamforming vector according to the average channel information and the output feature vector matrix U.
- Correlation matrix The correlation matrix after averaging can be expressed as:
- EBB or SVD pairs can be used.
- Do feature vector decomposition where The shaping to give the number of streams U get, where U is a front GET U of M columns, M being the number of streams excipient.
- a type factor can be given to each resource in the shaped granularity, and the shaping factor is averaged in a shaped granularity to obtain average channel information, so as to achieve the effect of suppressing noise,
- the beamforming vector is determined according to the average channel information and the output feature vector matrix U, including:
- the average channel information is:
- it may further include:
- the beamforming vector is normalized according to the stream power allocation such that the shaping vector W is normalized to each column.
- the base station detects the channel matrix on the kth subcarrier according to the SRS Nr represents the number of receiving antennas of the base station 128 or 64, or other possible antenna numbers, Nt represents the number of transmitting antennas of the terminal, 2, 4, 8 and other possible antenna numbers.
- the average processed correlation matrix can be expressed as:
- EBB or SVD can be used for decomposition, and of course, it is not limited to other matrix algorithms.
- the feature vector decomposition does not need to be completely decomposed, but is related to the number of streams of the shaping. The smaller the number of streams, the lower the computational complexity at the time of decomposition, and the final result is the U matrix.
- N is the number of estimated channels within the committed granularity.
- averaging operation for the resulting channel is the averaging operation for the resulting channel.
- the obtained vector can be:
- the upper right corner of H represents the conjugate transpose transformation.
- the deterministic beamforming scheme proposed in the embodiment of the present invention decomposes the traditional high-dimensional eigenvector by the matrix theory, and is equivalent to the low-dimensional eigenvector decomposition, which reduces the complexity of the beamforming operation. Due to the reduction of complexity, the communication device can shape the traditional large-grain beam and refine it into a small-grained beamforming, which can reduce the complexity and significantly improve the performance compared with the traditional method.
- the inventors obtained the results of the simulation results of the throughput simulation of the 128-antenna and MU user beamforming.
- the proposed scheme in the embodiment of the present application has the performance of the most traditional algorithm.
- the performance loss is also small. See Table 2 for details.
- Table 2 Comparison of throughput performance of beamforming for 128 antennas and MU users
- Simulation conditions 3D-UMa, AMC on, 10 USER, fixed 2 streams per user, unit Gbps, terminal 2 antenna
- the simplified EBB column is the result obtained by adopting the solution proposed in the embodiment of the present application.
- an apparatus for determining a beamforming vector is also provided in the embodiment of the present application. Since the principle of solving the problem is similar to the method for determining a beamforming vector, the implementation of the device can be referred to the method. The implementation, repetitions will not be repeated.
- FIG. 3 is a schematic structural diagram of an apparatus for determining a beamforming vector, as shown in the figure, which may include:
- the intercepting module 301 is configured to detect a channel matrix on the kth subcarrier according to the SRS Wherein, Nr represents the number of receiving antennas of the base station, and Nt represents the number of transmitting antennas of the terminal;
- Correlation matrix module 302 for finding correlation matrix among them Is a reduced order correlation matrix, and the dimension is the number of terminal antennas;
- Feature vector decomposition module 303 for Correlation matrix after averaging Perform eigenvector decomposition to obtain correlation matrix eigenvectors;
- An output matrix module 304 configured to determine an output feature vector matrix U according to the number of streams of the shaping, wherein the number of columns of U is a stream number;
- the beamforming vector module 305 is configured to determine a beamforming vector according to the average channel information and the output feature vector matrix U.
- the correlation matrix module is further used for the correlation matrix
- the averaging process is performed, and the average processed correlation matrix satisfies:
- N is the number of subcarriers in the physical resource block PRB.
- the eigenvector decomposition module is further used to adopt an EBB or SVD pair Do feature vector decomposition, where The shaping to give the number of streams U get, where U is a front GET U of M columns, M being the number of streams excipient.
- the beamforming vector module is further configured to determine a beamforming vector based on the average channel information and the output feature vector matrix U:
- it further includes:
- a normalization module is used to normalize the beamforming vector according to the flow power allocation such that the shaping vector W is normalized to each column.
- the base station includes a processor 400, a transceiver 410, and a memory 420, where:
- the processor 400 is configured to read a program in the memory 420 and perform the following process:
- Correlation matrix among them Is a reduced order correlation matrix, and the dimension is the number of terminal antennas;
- the beamforming vector is determined from the average channel information and the output eigenvector matrix U.
- the transceiver 410 is configured to send data under the control of the processor 400, and performs the following processes:
- Listening to the channel matrix on the kth subcarrier according to SRS Nr represents the number of receiving antennas of the base station, and Nt represents the number of transmitting antennas of the terminal.
- Correlation matrix The averaging process is performed, and the average processed correlation matrix satisfies:
- N is the number of subcarriers in the physical resource block PRB.
- the eigenvector method EBB or the singular value decomposition method SVD pair is adopted.
- Do feature vector decomposition where The shaping to give the number of streams U get, where U is a front GET U of M columns, M being the number of streams excipient.
- the beamforming vector is determined according to the average channel information and the output feature vector matrix U, including:
- it further includes:
- the beamforming vector is normalized according to the stream power allocation such that the shaping vector W is normalized to each column.
- the bus architecture may include any number of interconnected buses and bridges, specifically by the processor 400.
- the various circuits of the memory represented by one or more processors and memory 420 are linked together.
- the bus architecture can also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and, therefore, will not be further described herein.
- the bus interface provides an interface.
- Transceiver 410 can be a plurality of components, including a transmitter and a transceiver, providing means for communicating with various other devices on a transmission medium.
- the processor 400 is responsible for managing the bus architecture and general processing, and the memory 420 can store data used by the processor 400 when performing operations.
- embodiments of the present application can be provided as a method, system, or computer program product.
- the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
- the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.
- the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
- the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
- These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
- the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
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Abstract
本申请公开了一种确定波束赋型向量的方法及装置,包括:根据信道探测参考信号侦听到第k个子载波上信道矩阵Η^χ*·,其中,Nr表示基站接收天线数,M表示终端的发送天线数;求相关矩阵Λ^ζ^^χ*"1^"^)77,其中,Λ««是降阶的相关矩阵,维度是终端天线数;对"冊平均的相关矩阵做特征向量分解得到相关矩阵特征向量;根据赋型的流数,确定输出特征向量矩阵^7,其中,^的列数是流数;根据平均信道信息以及输出特征向量矩阵确定波束赋型向量。釆用本申请,通信设备可以极大降低波束赋型的复杂度,同时比现有方法性能有较大优势。
Description
本申请要求在2016年6月24日提交中国专利局、申请号为201610471909.4、发明名称为“一种确定波束赋型向量的方法及装置”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
本申请涉及无线通信技术领域,特别涉及一种确定波束赋型向量的方法及装置。
3GPP规范4G标准时,定义了两种双工方式:FDD(Frequency Division Duplex,频分双工)-LTE(Long Term Evolution,长期演进)和TDD(Time Division Duplex,时分双工)-LTE。其中TDD-LTE上下行链路采用相同的载波频点,上下行信道可认为具备互易性,这也为波束赋型理论在LTE中应用提供了可能。随着4.5G以及5G的行业发展,大规模天线Massive MIMO(Multiple Input Multiple Output,大规模多入多出)应用得到业界的广泛关注,已有相关文献已经证明,大阵列天线波束赋型宽度窄,能增加更多MU(MultipleUsers,多用户)复用的机会,提升小区吞吐量。
波束赋型,一般是根据基站侦听到的上行信道信息去计算当前用户的下行波束赋型向量。但现有技术的不足在于:尽管业界广泛采用垂直维度和水平维度分别进行波束赋型的方案,并且相比直接做128阶的特征向量分解,复杂度有明显降低,但是相比传统的波束赋型算法性能会降低。
发明内容
本申请提供了一种确定波束赋型向量的方法及装置,用以提供一种高性能的能降低波束赋型运算复杂度的方案。
本申请实施例中提供了一种确定波束赋型向量的方法,包括:
根据赋型的流数,确定输出特征向量矩阵U,其中,U的列数是流数;
根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量。
较佳地,根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量,包括:
较佳地,进一步包括:
根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
本申请实施例中提供了一种确定波束赋型向量的装置,包括
输出矩阵模块,用于根据赋型的流数,确定输出特征向量矩阵U,其中,U的列数是流数;
波束赋型向量模块,用于根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量。
较佳地,波束赋型向量模块进一步用于在根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量时:
较佳地,进一步包括:
归一化模块,用于根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
本申请实施例还提供一种基站,该基站包括处理器、存储器、收发机、总线及总线接口,其中,所述处理器、所述存储器和所述收发机通过总线连接,总线接口在总线和收发机之间提供接口;
收发机,用于在处理器的控制下发送数据,执行下列过程:
处理器,用于读取存储器中的程序,执行下列过程:
根据赋型的流数,确定输出特征向量矩阵U,其中,U的列数是流数;
根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量。
根据赋型流数得到Uget,其中Uget是U的前M列,M为赋型流数。
较佳的,所述处理器采用如下方式根据平均信道信息以及输出特征向量矩阵U确定波
束赋型向量:
较佳的,所述处理器还用于:
根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
本申请有益效果如下:
本申请实施例中提出的确定波束赋型方案,通过矩阵理论,把传统高维度的特征向量分解,等效为低维度的特征向量分解,降低了波束赋型运算的复杂度。
由于复杂度的减少,通信设备可以把传统大粒度波束赋型,细化为小粒度的波束赋型,在减少复杂度的同时,还能相比传统方法明显提升性能。
此处所说明的附图用来提供对本申请的进一步理解,构成本申请的一部分,本申请的示意性实施例及其说明用于解释本申请,并不构成对本申请的不当限定。在附图中:
图1为本申请实施例中天线序号以及它们构成的实际信道矩阵的关系示意图;
图2为本申请实施例中确定波束赋型向量的方法实施流程示意图;
图3为本申请实施例中确定波束赋型向量的装置结构示意图;
图4为本申请实施例中基站结构示意图。
发明人在发明过程中注意到:
波束赋型一般是根据基站侦听到的上行信道信息去计算当前用户的下行波束赋型向量。业界常用的波束赋型算法如下:
1)终端在不同时刻,轮流在不同天线上发送SRS(Sounding Reference signals,信道探测参考信号)侦听导频信号。基站接收后,利用SRS信号进行信道估计,并把终端不同天线到达基站的信道组合。由于TDD-LTE上下行信道互易,因此该上行信道组合也等价于下行信道组合。假设基站128天线,终端4天线,则信道组合表示为:
2)基站求信道的相关矩阵。
假设波束赋型向量用W表示。则赋型矢量满足(H4×128*W)'*(H4×128*W)最大。
(H4×128*W)'*(H4×128*W)=W'*H'4×128*H4×128*W=W'*R*W。
根据矩阵Rayleigh熵理论,W即为R的特征向量。因此在实现中对R做特征向量分解即可。实现中,我们会假设连续N个子载波上的信道分布平坦,会对N个子载波上的相关矩阵Rk求平均,其中连续N个子载波覆盖的PRB(physical resource block,物理资源块),称为波束赋型的粒度。
3)对相关矩阵做EBB(Eigenvalue Based Beamforming,特征向量法)或者SVD(Singular Value Decompostion,奇异值分解法)特征向量分解得到其中eig表示EBB分解,W表示所求的赋型粒度上共用的波束赋型向量。
从上面的公式推导看,基站需要做128×128这个大矩阵的特征值分解,从工程上讲,本身是很难是实现的。事实上,当前各设备厂商一直针对这个问题进行广泛研究,目前已有的做法是对上面的传统做法进行分步实现。
假设二维天线面阵垂直地面放置时,可分为水平方向H和垂直方向V。天线阵元的序号按照相同极化方向优先按垂直方向的方式排列,水平方向共有NH列相同极化方向的天线阵列,垂直方向共有NV根相同极化方向的天线阵元,极化方向数为NP=1,2。图1为天线序号以及它们构成的实际信道矩阵的关系示意图,具体关系请参见图1及相关现有资料。
对于下行MIMO信道,基站天线为发射天线,终端天线为接收天线,NHNVNP为基站天线数,NR为终端天线数,massive MIMO系统NHNVNP×NR。设为基站通过上行SRS信道估计得到的子载波n的下行MIMO信道矩阵,为第c列基站天线阵元到终端所有天线在子载波n上的信道矩阵,为第r行基站天线阵元到终端所有天线在子载波n上的信道矩阵。Sn为该PU(PerUser,每用户)上的抽样子载波集合,Sc={0,1,…,NHNP-1}为基站天线列的集合,Sr={0,1,…,NV}为基站天线行的集合,N(S)
表示集合S中元素的个数。
方法一:分别计算水平方向特征向量uH和垂直方向特征向量uV,最后将二者合成3D的massive MIMO信道向量。
步骤1:计算每个PU内的垂直方向特征向量。
步骤1.1:计算PU内每列同极化方向的基站天线到终端天线在所有抽样子载波上的垂直维平均发送相关矩阵:
步骤1.2:计算PU内所有基站天线列的垂直维平均发送相关矩阵:
步骤1.3:对PU内的垂直维平均发送相关矩阵RV进行特征值分解得到多个垂直维特征向量uV,1,uV,2,…,其中uV,1为主特征向量,uV,2为次特征向量,依次类推。对应的特征值为λV,1,λV,2,…。
步骤2:计算每个PU内的水平方向特征向量。
步骤2.1:计算PU内每行基站天线到终端天线在所有抽样子载波上的水平维平均发送相关矩阵:
步骤2.2:计算PU内所有基站天线行的水平维平均发送相关矩阵:
步骤2.3:对PU内的水平维平均发送相关矩阵RH进行特征值分解得到多个水平维特征向量uH,1,uH,2,…,其中uH,1为主特征向量,uH,2为次特征向量,依次类推。对应的特征向量为λH,1,λH,2,…。
步骤3:合成3D预编码矩阵或信道向量。
水平方向和垂直方向的各特征向量构成3D特征向量即为预编码向量。Rank L可由最大L个3D特征值对应的3D特征向量确定,例如rank 4的3D预编码矩阵:
相应的3D信道矩阵为:
方法二:计算垂直方向特征向量uV,形成等效水平信道,再计算等效水平信道的特征向量uH,最后将二者合成3D的massive MIMO信道向量。
步骤1:计算每个PU内的垂直方向特征向量。
步骤1.1:计算PU内每列同极化方向的基站天线到终端天线在所有抽样子载波上的垂直维平均发送相关矩阵:
步骤1.2:计算PU内的垂直维预编码矩阵。
基站所有天线列采用相同垂直维预编码向量。
步骤1.2.1.1:计算PU内基站所有天线列的垂直维平均发送相关矩阵。
步骤1.2.1.2:对PU内的垂直维平均发送相关矩阵RV进行特征值分解得到垂直维主特征向量uV,1,即垂直维各列天线使用的预编码向量。
步骤1.2.1.3:形成PU每个抽样子载波的等效水平维信道,子载波n。
步骤3:合成3D信道向量。
水平方向和垂直方向的特征向量构成3D特征向量即为预编码向量。Rank L可由最大L个3D特征值对应的3D特征向量确定,例如rank 2的3D预编码矩阵矩阵:
如果每个PRB上都做这样的赋型计算,则复杂度依然很大,因此工程上一般用4个PRB的粒度做一次赋型计算。由于粒度变粗,赋型的精确度下降,下行性能也会降低。
发明人通过对128天线、MU用户波束赋型的吞吐量仿真性能对比得到如表1的结果:
表1:128天线,MU用户波束赋型的吞吐量性能对比
仿真条件:3D-UMa,AMC on,10个USER,每个用户固定2流,单位Gbps,终端2天线
由此可见,尽管业界广泛采用这种垂直维度和水平维度分别波束赋型的方案,并且相比直接做128阶的特征向量分解,复杂度有明显降低,但是正是因为垂直维度和水平维度没有联合起来考虑,相比传统的波束赋型算法性能会降低。
基于这种改进方案依然存在运算量复杂度大,性能下降等问题,本申请实施例中将提出一种更简单的波束赋型算法,不仅低于现有的优化方案复杂度,并且与最传统的算法相
比,性能损失也很小。
在提供具体的技术方案前,先对本申请实施例中提出的技术方案进行解释,以使本领域技术人员更容易理解本方案。
发明人注意到,波束赋型向量就是求满足WH*RHH*W最大的解,即RHH的特征向量。下面为了方便以基站128天线,终端4天线为例进行说明。
先对H4×128做SVD分解,H4×128=UΛDH,则
RHH=(UΛDH)H*UΛDH
=D*ΛH*UH*UΛDH
=D*Λ2*DH,其中U为左酉矩阵,
Λ为对角矩阵,D为右酉矩阵(酉矩阵特性:酉矩阵与自己的共轭转置矩阵相乘为单位阵)。
事实上,求的赋型向量W就是右酉矩阵里的前几列向量:
WH*RHH*W=DH*RHH*D=Λ2,其中,Λ2就是特征向量对应的特征值,物理意义表示对应的数据流上的功率。因此波束赋型关心的是如何求出D。
现在用另外一种方式求矩阵D。
由于Λ为一实数对角矩阵(副对角元素为0),赋型向量乘上实数不改变空间特性,因此D*Λ就是所要的赋型向量W。
综上,采用上述思路的算法,只需求一次4维度的SVD分解就可以求出赋型向量,相比传统的128阶的特性向量分解,以及目前比较流行的简化算法,需要做一次8阶特征向量分解,再加一次8阶或者16阶的特性向量分解,实现复杂度都明显降低。
下面结合附图对本申请的具体实施方式进行说明。
图2为确定波束赋型向量的方法实施流程示意图,如图所示,可以包括:
步骤204、根据赋型的流数,确定输出特征向量矩阵U,其中,U的列数是流数;
步骤205、根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量。
实施中,可对每一个赋形颗粒度内的资源给出一个赋型因子,并在一个赋形颗粒度内对该赋型因子进行平均处理得到平均信道信息,以实现抑制噪声的作用,一种可能的实施方式中根据平均信道信息以及输出特征向量矩阵U,确定波束赋型向量,包括:
实施中,还可以进一步包括:
根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
下面对具体的实施进行进一步说明。
2)求相关矩阵:
4)根据赋型的流数,确定特征向量分解的计算量以及输出特征向量矩阵U。其中U的列数就是流数,例如赋型流数为2,则Uget=U(:,[1:2])。
具体实施中,特征向量分解不需要完全分解,而是与赋型的流数有关。流数越少,分解时计算复杂度就低,最终求出来的就是U矩阵。
则所求赋型向量可为:
6)最后还可以进一步的对W根据流功率分配归一化。
由上述可见,本申请实施例中提出的确定波束赋型方案,通过矩阵理论,把传统高维度的特征向量分解,等效为低维度的特征向量分解,降低了波束赋型运算的复杂度。由于复杂度的减少,通信设备可以把传统大粒度波束赋型,细化为小粒度的波束赋型,在减少复杂度的同时,还能相比传统方法明显提升性能。
进一步的,发明人通过对128天线、MU用户波束赋型的吞吐量仿真性能对比得到如表2的结果,通过仿真对比,可以看出本申请实施例中提出的方案与最传统的算法性能相比,性能损失也很小。具体请见表2
表2:128天线,MU用户波束赋型的吞吐量性能对比
仿真条件:3D-UMa,AMC on,10个USER,每个用户固定2流,单位Gbps,终端2天线
其中,简化EBB一栏即为采用本申请实施例中提出的方案得到的结果。
基于同一发明构思,本申请实施例中还提供了一种确定波束赋型向量的装置,由于该装置解决问题的原理与一种确定波束赋型向量的方法相似,因此该装置的实施可以参见方法的实施,重复之处不再赘述。
图3为确定波束赋型向量的装置结构示意图,如图所示,可以包括:
输出矩阵模块304,用于根据赋型的流数,确定输出特征向量矩阵U,其中,U的列数是流数;
波束赋型向量模块305,用于根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量。
实施中,波束赋型向量模块进一步用于在根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量时:
实施中,进一步包括:
归一化模块,用于根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
为了描述的方便,以上所述装置的各部分以功能分为各种模块或单元分别描述。当然,在实施本申请时可以把各模块或单元的功能在同一个或多个软件或硬件中实现。
在实施本申请实施例提供的技术方案时,可以按如下方式实施。
图4为基站结构示意图,如图所示,基站中包括处理器400、收发机410和存储器420,其中:
处理器400,用于读取存储器420中的程序,执行下列过程:
根据赋型的流数,确定输出特征向量矩阵U,其中,U的列数是流数;
根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量。
收发机410,用于在处理器400的控制下发送数据,执行下列过程:
实施中,根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量,包括:
实施中,进一步包括:
根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
其中,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400
代表的一个或多个处理器和存储器420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外围设备、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410可以是多个元件,即包括发送机和收发机,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400负责管理总线架构和通常的处理,存储器420可以存储处理器400在执行操作时所使用的数据。
本领域内的技术人员应明白,本申请的实施例可提供为方法、系统、或计算机程序产品。因此,本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且,本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器和光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器,使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中,使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品,该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上,使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理,从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
显然,本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样,倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内,则本申请也意图包含这些改动和变型在内。
Claims (15)
- 如权利要求1至4任一所述的方法,其特征在于,进一步包括:根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
- 如权利要求6至9任一所述的装置,其特征在于,进一步包括:归一化模块,用于根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
- 一种基站,其特征在于,包括处理器、存储器、收发机、总线及总线接口,其中,所述处理器、所述存储器和所述收发机通过总线连接,总线接口在总线和收发机之间提供接口;收发机,用于在处理器的控制下发送数据,执行下列过程:处理器,用于读取存储器中的程序,执行下列过程:根据赋型的流数,确定输出特征向量矩阵U,其中,U的列数是流数;根据平均信道信息以及输出特征向量矩阵U确定波束赋型向量。
- 如权利要求11至14任一所述的基站,其特征在于,所述处理器还用于:根据流功率分配对波束赋型向量归一化,以使赋型向量W每列归一。
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CN110166092A (zh) * | 2018-02-14 | 2019-08-23 | 上海华为技术有限公司 | 数据端口到天线的映射向量的生成方法及装置 |
WO2022222074A1 (en) * | 2021-04-21 | 2022-10-27 | Nokia Shanghai Bell Co., Ltd. | Beamforming solution for fdd mimo communication |
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 |
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101800582A (zh) * | 2009-02-09 | 2010-08-11 | 中兴通讯股份有限公司 | 一种多用户波束成形方法及装置 |
CN102404028A (zh) * | 2010-09-07 | 2012-04-04 | 普天信息技术研究院有限公司 | 一种波束赋形方法 |
CN103281110A (zh) * | 2013-04-26 | 2013-09-04 | 大唐移动通信设备有限公司 | 波束赋形方法和设备 |
CN103457647A (zh) * | 2012-06-04 | 2013-12-18 | 普天信息技术研究院有限公司 | 一种双流波束赋形方法及装置 |
CN105207708A (zh) * | 2015-09-06 | 2015-12-30 | 北京北方烽火科技有限公司 | 一种波束赋形权向量的生成方法及装置 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104980206B (zh) * | 2014-04-11 | 2018-06-15 | 普天信息技术有限公司 | 一种波束赋形方法 |
-
2016
- 2016-06-24 CN CN201610471909.4A patent/CN107547117A/zh not_active Withdrawn
-
2017
- 2017-04-14 WO PCT/CN2017/080605 patent/WO2017219739A1/zh active Application Filing
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101800582A (zh) * | 2009-02-09 | 2010-08-11 | 中兴通讯股份有限公司 | 一种多用户波束成形方法及装置 |
CN102404028A (zh) * | 2010-09-07 | 2012-04-04 | 普天信息技术研究院有限公司 | 一种波束赋形方法 |
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