WO2016169168A1 - 一种数据处理方法、装置及计算机存储介质 - Google Patents

一种数据处理方法、装置及计算机存储介质 Download PDF

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WO2016169168A1
WO2016169168A1 PCT/CN2015/087543 CN2015087543W WO2016169168A1 WO 2016169168 A1 WO2016169168 A1 WO 2016169168A1 CN 2015087543 W CN2015087543 W CN 2015087543W WO 2016169168 A1 WO2016169168 A1 WO 2016169168A1
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channel estimation
estimation matrix
performance
detected layer
matrix
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PCT/CN2015/087543
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French (fr)
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易立强
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深圳市中兴微电子技术有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • 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

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  • the present invention relates to multiple-input multiple-output (MIMO) technology, and more particularly to a data processing method, apparatus, and computer storage medium.
  • MIMO multiple-input multiple-output
  • MIMO technology is the main method for improving the spectrum efficiency of current wireless communication.
  • MIMO technologies have been adopted for new generation high-throughput wireless communication protocols such as 802.11n/ac and LTE/LTE-A.
  • 802.11n/ac new generation high-throughput wireless communication protocols
  • LTE/LTE-A new generation high-throughput wireless communication protocols
  • the physical layer abstraction can reflect the instantaneous state of the wireless channel to the high-level module of the system through one or several simple quantities, thereby realizing the interaction of the cross-layer information and optimizing the overall performance of the system, such as link adaptation and hybrid automatic retransmission. And other technologies.
  • the physical layer abstraction is based on Signal-to-Noise Ratio (SNR), such as the equivalent exponential effective SINR mapping (EESM) algorithm, which can accurately achieve the minimum MIMO.
  • SNR Signal-to-Noise Ratio
  • EESM equivalent exponential effective SINR mapping
  • MLD Maximum Likelihood Detector
  • MLD is a nonlinear detection compared to MMSE detection, and it is difficult to directly obtain an accurate MLD abstract model.
  • the traditional MIMO MLD-based performance abstraction method normalizes the channel matrix by using the channel noise variance to obtain a normalized channel matrix, and performs QR decomposition on the normalized channel matrix or column permutation deformation.
  • R of the layer based on the pre-built parameter-performance table, multi-dimensional linear interpolation is performed using each non-zero element in the matrix R as an interpolation parameter to determine the layer performance of the MIMO system.
  • R is used to construct the parameter-performance table
  • the table size will increase exponentially as the range of elements in the matrix R increases, resulting in a very large consumption of system storage space and simulation time.
  • the whole calculation is very complicated and inefficient.
  • Embodiments of the present invention provide a data processing method, apparatus, and computer storage medium, which can effectively implement performance abstraction based on MIMO MLD.
  • An embodiment of the present invention provides a data processing method, where the method includes:
  • determining the performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set including:
  • the determining, according to the information interaction value and the main diagonal element in the second channel estimation matrix, the performance of the detected layer including:
  • a two-dimensional linear interpolation method is used to determine the performance of the detected layer based on the information interaction value and the main diagonal element in the second channel estimation matrix.
  • the first channel estimation matrix corresponding to the detected layer in the system space is a channel estimation matrix of the detected layer.
  • the first channel estimation matrix corresponding to the detected layer in the system space is a column permutation matrix of the channel estimation matrix different from the detected layer in the system space.
  • the embodiment of the present invention further provides a computer storage medium, where the computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the data processing method according to the embodiment of the present invention.
  • An embodiment of the present invention further provides a data processing apparatus, where the apparatus includes an obtaining module, a first determining module, and a second determining module;
  • the acquiring module is configured to acquire a first channel estimation matrix and a noise mean square error corresponding to the detected layer in the system space;
  • the first determining module is configured to determine a normalized second channel estimation matrix by using the first channel estimation matrix and a noise mean square error;
  • the second determining module is configured to determine a performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set.
  • the second determining module includes an index determining unit, an information interaction value determining unit, and a performance determining unit;
  • the index determining unit is configured to determine a performance base index according to a primary diagonal element in the second channel estimation matrix
  • the information interaction value determining unit is configured to combine the preset performance relationship curve set, the determined performance base index, and the non-primary diagonal element in the second channel estimation matrix to obtain a corresponding detection layer Information interaction value;
  • the performance determining unit is configured to determine a performance of the detected layer according to the information interaction value and a main diagonal element in the second channel estimation matrix.
  • the performance determining unit is configured to: according to the information interaction value and the The main diagonal elements in the second channel estimation matrix are determined by two-dimensional linear interpolation to determine the performance of the detected layer.
  • the first channel estimation matrix corresponding to the detected layer in the system space is a channel estimation matrix of the detected layer.
  • the first channel estimation matrix corresponding to the detected layer in the system space is a column permutation matrix of the channel estimation matrix different from the detected layer in the system space.
  • the data processing method and device and the computer storage medium provided by the embodiments of the present invention acquire a first channel estimation matrix and a noise mean square error corresponding to the detected layer in the system space; and determine the first channel estimation matrix and the noise mean square error a normalized second channel estimation matrix; determining performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set.
  • performance abstraction based on MIMO MLD can be realized, and the performance of the MIMO system can be effectively saved to determine the storage space and simulation time consumption on the basis of simplifying the entire computational performance complexity and ensuring the calculation accuracy.
  • FIG. 1 is a schematic flowchart of an implementation process of a data processing method according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of the two detection layers in the embodiment of the present invention using 16QAM modulation Related performance relationship diagram
  • FIG. 3 is a schematic flowchart of an implementation process for determining performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set according to an embodiment of the present invention
  • FIG. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a second determining module according to an embodiment of the present invention.
  • acquiring a first channel estimation matrix and a noise mean square error corresponding to the detected layer in the system space determining normalization by using the first channel estimation matrix and the noise mean square error a second channel estimation matrix; determining performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set.
  • FIG. 1 is a schematic flowchart of an implementation of a data processing method according to an embodiment of the present invention. As shown in FIG. 1 , a data processing method according to an embodiment of the present invention includes:
  • Step S101 Acquire a first channel estimation matrix and a noise mean square error corresponding to the detected layer in the system space;
  • the received signal can be represented by the following expression:
  • N [n 0 , n 1 ] T is an additive white Gaussian noise (AWGN) vector, and ⁇ is the noise mean square error.
  • AWGN additive white Gaussian noise
  • a data processing apparatus acquires a first channel estimation matrix and a noise mean square error corresponding to a detected layer in a system space from a device of a previous stage connected to itself.
  • the first channel estimation matrix corresponding to the detected layer in the system space may be a channel estimation matrix of the detected layer, that is, The first channel estimation matrix corresponding to the detected layer in the system space may also be a channel estimation matrix different from the detected layer in the system space.
  • the detection methods of the respective detection layers are the same, the following description will be made only by taking the detected layer where s 1 is located as an example.
  • the first channel estimation matrix and the noise mean square difference of the detected layer are respectively recorded as And ⁇ .
  • Step S102 Determine a normalized second channel estimation matrix by using the first channel estimation matrix and the noise mean square error
  • a data processing apparatus determines a normalized second channel estimation matrix by using the first channel estimation matrix and a noise mean square error Including: through QR decomposition To determine the normalized second channel estimation matrix Where Q is an orthogonal matrix, For the upper triangular matrix.
  • Step S103 Determine performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set.
  • the data processing apparatus first determines a set of performance relationship curves based on a large number of layer detections. Specifically, it is necessary to calculate a log-likelihood ratio (LLR) value in MIMO maximum likelihood ML detection, which is assumed to be represented by L( ⁇ ).
  • LLR log-likelihood ratio
  • the max-log approximation is generally used to calculate the expression as follows:
  • d ML is the shortest Euclidean distance obtained by the search
  • s ML is the transmission vector corresponding to d ML
  • It is the shortest Euclidean distance obtained by fixing the bit of d ML to (j, b) and then fixing it.
  • the second channel estimation matrix In the case of good conditions, it can be approximated that the same
  • different main diagonal elements with The determined performance base index may be the same, that is, corresponding to the same performance relationship curve, which can reduce the number of performance curves.
  • the performance basis index is determined to be treated differently according to the layer modulation mode combination.
  • both detection layers are given in the case of 16QAM modulation in Quadrature Amplitude Modulation (QAM).
  • QAM Quadrature Amplitude Modulation
  • Related performance diagrams MI value is set in the main diagonal element combination In case, with The value shows a smooth trend, and the performance relationship is easy to express in a functional relationship. Performance graphs are obtained using off-line simulation. Simulation models for different The matrix ML demodulation maps out the MI value.
  • the matrix element takes a value.
  • Some features can be used to selectively select matrix element values without the need for exhaustive methods to reduce simulation time. If different modulation modes are used to combine different matrix element ranges, in general, the higher the modulation order, the larger the value range; QR decomposition uniqueness
  • the main diagonal elements of the matrix elements are positive; and the non-primary diagonal elements have a conjugate relationship in a specific case, and the phase rotations are +/- ⁇ /2, +/- ⁇ , +/- 3 ⁇ /2 have equal MI values and the like.
  • the value step can be equally spaced or non-equal interval.
  • the data processing apparatus is based on the second channel estimation matrix based on the performance relationship curve set as shown in Table 1 determined by the data processing apparatus according to a large number of layer detections. And a preset performance relationship curve set determines the performance of the detected layer, as shown in FIG. 3, including:
  • Step S1031 Determine a performance basic index according to a main diagonal element in the second channel estimation matrix
  • the modulo value of the diagonal element of the master To determine the performance base index.
  • Step S1032 Combine the preset performance relationship curve set, the determined performance base index, and the non-primary diagonal element in the second channel estimation matrix to obtain an information interaction value corresponding to the detected layer;
  • Step S1033 Determine the performance of the detected layer according to the information interaction value and the main diagonal element in the second channel estimation matrix.
  • the data processing apparatus may use two-dimensional linear interpolation to determine the performance of the detected layer, and may also adopt other An interpolation method is used to determine the performance of the detected layer.
  • the data processing method can implement the performance abstraction based on the MIMO MLD, and can effectively save the performance of the MIMO system and determine the storage space and the simulation time on the basis of simplifying the entire computational performance complexity and ensuring the calculation accuracy. Consumption.
  • the embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the data processing method according to the first embodiment of the invention.
  • the apparatus includes an obtaining module 401, a first determining module 402, and a second determining module 403;
  • the acquiring module 401 is configured to acquire a first channel estimation matrix and a noise mean square error corresponding to the detected layer in the system space;
  • the first channel estimation matrix corresponding to the detected layer in the system space may be a channel estimation matrix of the detected layer, or may be a channel estimation matrix different from the detected layer in the system space.
  • Column permutation matrix may be a channel estimation matrix of the detected layer, or may be a channel estimation matrix different from the detected layer in the system space.
  • the first determining module 402 is configured to determine a normalized second channel estimation matrix by using the first channel estimation matrix and a noise mean square error;
  • the second determining module 403 is configured to determine performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set.
  • the second determining module 403 includes an index determining unit 4031, an information interaction value determining unit 4032, and a performance determining unit 4033;
  • the index determining unit 4031 is configured to determine a performance base index according to a primary diagonal element in the second channel estimation matrix
  • the information interaction value determining unit 4032 is configured to combine the preset performance relationship curve set, the determined performance base index, and the non-primary diagonal element in the second channel estimation matrix to obtain a corresponding detection.
  • Layer information interaction value
  • the performance determining unit 4033 is configured to determine a performance of the detected layer according to the information interaction value and a main diagonal element in the second channel estimation matrix.
  • the performance determining unit 4033 determines the performance of the detected layer by using a two-dimensional linear interpolation method according to the information interaction value and the main diagonal element in the second channel estimation matrix.
  • each module in the data processing apparatus and each unit included in each module of the embodiment of the present invention may be implemented by a processor in the data processing apparatus, or may be implemented by a specific logic circuit;
  • the central processing unit CPU
  • the microprocessor Micro Processor Unit, MPU
  • the digital signal processor DSP
  • FPGA Field Programmable Gate Array
  • embodiments of the present invention can be provided as a method, system, Or a computer program product. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware. Moreover, the invention 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.
  • the data processing method acquires a first channel estimation matrix and a noise mean square error corresponding to a detected layer in a system space; and determines a normalized second channel by using the first channel estimation matrix and a noise mean square error An estimation matrix; determining performance of the detected layer according to the second channel estimation matrix and a preset performance relationship curve set.

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Abstract

本发明实施例提供数据处理方法、装置及计算机存储介质，获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。

Description

一种数据处理方法、装置及计算机存储介质 技术领域
本发明涉及多输入多输出(Multiple-Input Multiple-Output，MIMO)技术，尤其涉及一种数据处理方法、装置及计算机存储介质。
背景技术
MIMO技术是当前无线通信提高频谱效率的主要方法。目前，802.11n/ac、LTE/LTE-A等新一代高吞吐量无线通信协议均已采用MIMO技术。随着对无线通信系统的传输速率，服务质量以及广泛的接入等需求的提高，MIMO技术的快速跨层调度受到极大的关注。物理层抽象可以将无线信道即时的状态通过一个或几个简单的量反映给系统的高层模块，从而实现跨层信息的交互，达到系统整体性能的优化，如链路自适应、混合自动重传等技术。
目前，有关物理层抽象均是基于信噪比(Signal-to-Noise Ratio，SNR)进行的，如等效指数信噪比映射(Exponential Effective SINR Mapping，EESM)算法，能够准确实现对MIMO的最小均方误差(Minimum Mean Square Error，MMSE)检测算法的性能抽象。然而，在MIMO系统中，相对于MMSE检测，最大似然检测(Maximum Likelihood Detector，MLD)作为一种非线性检测，难以直接得到精确的MLD抽象模型。
相关技术中，传统的基于MIMO MLD的性能抽象方法，利用信道噪声方差对信道矩阵进行归一化，获得归一化信道矩阵，对该归一化信道矩阵或列置换得到的变形进行QR分解获得对于该层的上三角矩阵R；基于预先构建的参数-性能表格，利用所述矩阵R中的各个非零元素作为内插参数执行多维线性插值，确定出MIMO系统的该层性能。但是，在采用枚举矩阵 R的方式来构建参数-性能表格时，随着矩阵R中元素范围的增加表格大小将呈指数级增加，导致对系统存储空间和仿真时间的消耗非常大；同时，由于所述方法存在三个维度以上的线性插值，整个计算非常复杂，效率不高。
发明内容
本发明实施例提供一种数据处理方法、装置及计算机存储介质，能够有效实现基于MIMO MLD的性能抽象。
本发明实施例的技术方案是这样实现的：
本发明实施例提供一种数据处理方法，所述方法包括：
获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；
利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；
根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。
上述方案中，所述根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能，包括：
根据所述第二信道估计矩阵中的主对角线元素确定性能基础索引；
结合所述预置的性能关系曲线集、所确定的性能基础索引和所述第二信道估计矩阵中的非主对角线元素，得到对应于被检测层的信息交互值；
根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能。
上述方案中，所述根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能，包括：
根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素，采用二维线性插值法来确定所述被检测层的性能。
上述方案中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述被检测层的信道估计矩阵。
上述方案中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述系统空间中区别于所述被检测层的信道估计矩阵的列置换矩阵。
本发明实施例还提供一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令用于执行本发明实施例所述数据处理方法。
本发明实施例还提供一种数据处理装置，所述装置包括获取模块、第一确定模块和第二确定模块；
所述获取模块，配置为获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；
所述第一确定模块，配置为利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；
所述第二确定模块，配置为根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。
上述方案中，所述第二确定模块包括索引确定单元、信息交互值确定单元和性能确定单元；其中，
所述索引确定单元，配置为根据所述第二信道估计矩阵中的主对角线元素确定性能基础索引；
所述信息交互值确定单元，配置为结合所述预置的性能关系曲线集、所确定的性能基础索引和所述第二信道估计矩阵中的非主对角线元素，得到对应于被检测层的信息交互值；
所述性能确定单元，配置为根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能。
上述方案中，所述性能确定单元，配置为根据所述信息交互值和所述 第二信道估计矩阵中的主对角线元素，采用二维线性插值法来确定所述被检测层的性能。
上述方案中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述被检测层的信道估计矩阵。
上述方案中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述系统空间中区别于所述被检测层的信道估计矩阵的列置换矩阵。
本发明实施例所提供的数据处理方法、装置及计算机存储介质，获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。如此，能够实现基于MIMO MLD的性能抽象，在简化整个计算性能复杂度和保证计算精度的基础上，有效节省MIMO系统性能确定对存储空间和仿真时间的消耗。
附图说明
图1为本发明实施例数据处理方法的实现流程示意图；
图2为本发明实施例两个检测层均采用16QAM调制情况下与
Figure PCTCN2015087543-appb-000001
相关的性能关系图；
图3为本发明实施例根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能的实现流程示意图；
图4为本发明实施例数据处理装置的组成结构示意图；
图5为本发明实施例所述第二确定模块的组成结构示意图。
具体实施方式
在本发明实施例中，获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；利用所述第一信道估计矩阵和噪声均方差确定归一化 的第二信道估计矩阵；根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。
下面结合附图及具体实施例对本发明再作进一步详细的说明。
图1为本发明实施例数据处理方法的实现流程示意图，如图1所示，本发明实施例数据处理方法包括：
步骤S101：获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；
这里，首先对2X2MIMO系统的信号输入输出情况进行简要描述。对于2X2MIMO系统来讲，接收信号可以由下述表达式表示：
y＝Hs+N；
其中，s＝[s0,s1]为发送信号向量，s中每个元素包含Ω比特，si∈θ，θ为星座点集；y＝[y0,y1]T为接收信号向量；
Figure PCTCN2015087543-appb-000002
为信道估计矩阵；N＝[n0,n1]T是加性高斯白噪声(Additive White Gaussian Noise，AWGN)向量，σ为噪声均方差。
具体地，在MIMO系统中，数据处理装置从与自身相连接的前一级的装置中获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差。
在实际应用中，所述对应于系统空间中被检测层的第一信道估计矩阵可以为所述被检测层的信道估计矩阵，即
Figure PCTCN2015087543-appb-000003
所述对应于系统空间中被检测层的第一信道估计矩阵也可以为所述系统空间中区别于所述被检测层的信道估计矩阵
Figure PCTCN2015087543-appb-000004
的列置换矩阵，即
Figure PCTCN2015087543-appb-000005
这里，由于各检测层求取方法相同，下面仅以求取s1所在被检测层为例进行说明。其中，所述被检测层的第一信道估计矩阵和噪声均方差分别记 为
Figure PCTCN2015087543-appb-000006
和σ。
步骤S102：利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；
具体地，在MIMO系统中，数据处理装置利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵
Figure PCTCN2015087543-appb-000007
包括：通过QR分解式
Figure PCTCN2015087543-appb-000008
来确定归一化的第二信道估计矩阵
Figure PCTCN2015087543-appb-000009
其中，Q为正交矩阵，
Figure PCTCN2015087543-appb-000010
为上三角矩阵。
步骤S103：根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。
这里，在MIMO系统中，数据处理装置首先会根据大量的层检测确定出性能关系曲线集。具体地，在MIMO最大似然ML检测中需要计算对数似然比(Log-likelihood ratio，LLR)值，假定用L(·)表示。为了简化计算复杂度，一般采用max-log近似计算表达式如下：
Figure PCTCN2015087543-appb-000011
这里，
Figure PCTCN2015087543-appb-000012
和
Figure PCTCN2015087543-appb-000013
分别表示传输的符号j的第b个比特值为0和1组成的符号向量集合，j＝0,1，0≤b<Ω。
结合QR分解H＝QR，若
Figure PCTCN2015087543-appb-000014
则max-log近似计算表达式进一步转换为下述表达式：
Figure PCTCN2015087543-appb-000015
上述表达式可以等效转换为：
Figure PCTCN2015087543-appb-000016
其中，dML为搜索得到的最短欧式距离，sML为dML对应的发送向量；
Figure PCTCN2015087543-appb-000017
是将dML的比特为(j,b)取反后固定不变得到的最短欧式距离。
上述等效转换后的表达式中的欧式距离计算方法为：
Figure PCTCN2015087543-appb-000018
这样，
Figure PCTCN2015087543-appb-000019
结合上述表达式可以确定s1所在检测层中，
Figure PCTCN2015087543-appb-000020
Figure PCTCN2015087543-appb-000021
且
Figure PCTCN2015087543-appb-000022
仅为实数或者虚数。在dML与
Figure PCTCN2015087543-appb-000023
不相关的基础上，仿真表明，R01相位旋转+/-π/2，+/-π，+/-3π/2，求取的LLR具有相同的分布，也即相同的信息交互值MI。
这里，为了简化处理，在第二信道估计矩阵
Figure PCTCN2015087543-appb-000024
为良好条件的情况下，可以近似认为相同的|R01|具有相同的MI值。这样，在计算性能关系因素R01时仅考虑|R01|的影响。
便于说明，这里采用
Figure PCTCN2015087543-appb-000025
表示第二信道估计矩阵
Figure PCTCN2015087543-appb-000026
中主对角线元素的求模运算值，
Figure PCTCN2015087543-appb-000027
表示第二信道估计矩阵
Figure PCTCN2015087543-appb-000028
中非主对角线元素的求模运算值。由
Figure PCTCN2015087543-appb-000029
来确定性能基础索引，再根据
Figure PCTCN2015087543-appb-000030
得到对应的信息交互值MI。
这里，在实际应用中，不同的主对角元素
Figure PCTCN2015087543-appb-000031
和
Figure PCTCN2015087543-appb-000032
所确定的性能基础索引可能是相同，也即对应相同的性能关系曲线，这样可以缩减性能曲线的条数。然而，由于不同的层调制模式组合可能具有不同性能关系曲线，确定性能基础索引需按照层调制模式组合区别对待。如图2所示，给出了 两个检测层均采用正交振幅调制(Quadrature Amplitude Modulation，QAM)中的16QAM调制情况下与
Figure PCTCN2015087543-appb-000033
相关的性能关系图。MI值在设置主对角元素组合
Figure PCTCN2015087543-appb-000034
情况下，随
Figure PCTCN2015087543-appb-000035
取值呈平滑的变化趋势，性能关系易于用函数关系式表达。性能关系图采用离线仿真得到。仿真模型针对不同的
Figure PCTCN2015087543-appb-000036
矩阵ML解调映射出MI值。先确定好调制方式组合，再选择
Figure PCTCN2015087543-appb-000037
矩阵元素取值。可利用一些特性进行选择性选取矩阵元素取值，不需要进行穷举方法，以缩减仿真时间。如利用不同调制方式组合采用不同的矩阵元素范围，一般说来，调制阶数越高，取值范围越大；QR分解唯一性中
Figure PCTCN2015087543-appb-000038
矩阵元素主对角元素为正；以及非主对角元素特定情况下共轭关系，相位旋转+/-π/2，+/-π，+/-3π/2具有相等MI值等性质。元素取值方面，取值步长可以采用等间隔，也可采用非等间隔方式。
以下述表1为例，给出了与
Figure PCTCN2015087543-appb-000039
相关的性能关系曲线示意，其中的性能关系曲线是采用多项式拟合方法得到的。这里，需要补充说明的是，在实际应用中，所述性能关系曲线也可以通过其他关系函数拟合得到。
Figure PCTCN2015087543-appb-000040
表1
基于数据处理装置根据大量的层检测确定出的如表1所示的性能关系曲线集，数据处理装置根据所述第二信道估计矩阵
Figure PCTCN2015087543-appb-000041
和预置的性能关系曲线集确定所述被检测层的性能，如图3所示，包括：
步骤S1031：根据所述第二信道估计矩阵中的主对角线元素确定性能基础索引；
具体地，根据所述第二信道估计矩阵
Figure PCTCN2015087543-appb-000042
中主对角线元素的求模运算值
Figure PCTCN2015087543-appb-000043
来确定性能基础索引。
步骤S1032：结合所述预置的性能关系曲线集、所确定的性能基础索引和所述第二信道估计矩阵中的非主对角线元素，得到对应于被检测层的信息交互值；
具体地，结合所述预置的性能关系曲线集、所确定的性能基础索引、及第二信道估计矩阵
Figure PCTCN2015087543-appb-000044
中非主对角线元素的求模运算值
Figure PCTCN2015087543-appb-000045
得到对应的信息交互值MI。
步骤S1033：根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能。
具体地，根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素，数据处理装置可以采用二维线性插值法来确定所述被检测层的性能，也可以采用其他的插值方法来确定所述被检测层的性能。
如此，通过本发明实施例所述数据处理方法，能够实现基于MIMO MLD的性能抽象，在简化整个计算性能复杂度和保证计算精度的基础上，有效节省MIMO系统性能确定对存储空间和仿真时间的消耗。
本发明实施例还提供一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令用于执行本发明实施例一所述数据处理方法。
图4为本发明实施例数据处理装置的组成结构示意图，如图4所示，所述装置包括获取模块401、第一确定模块402和第二确定模块403；
所述获取模块401，配置为获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；
其中，所述对应于系统空间中被检测层的第一信道估计矩阵可以为所述被检测层的信道估计矩阵，也可以为所述系统空间中区别于所述被检测层的信道估计矩阵的列置换矩阵。
所述第一确定模块402，配置为利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；
所述第二确定模块403，配置为根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。
在一实施例中，如图5所示，所述第二确定模块403包括索引确定单元4031、信息交互值确定单元4032和性能确定单元4033；其中，
所述索引确定单元4031，配置为根据所述第二信道估计矩阵中的主对角线元素确定性能基础索引；
所述信息交互值确定单元4032，配置为结合所述预置的性能关系曲线集、所确定的性能基础索引和所述第二信道估计矩阵中的非主对角线元素，得到对应于被检测层的信息交互值；
所述性能确定单元4033，配置为根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能。
具体地，所述性能确定单元4033根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素，采用二维线性插值法来确定所述被检测层的性能。
在实际应用中，本发明实施例所述数据处理装置中的各模块及其各模块所包括的各单元均可以通过所述数据处理装置中的处理器实现，也可以通过具体的逻辑电路实现；比如，在实际应用中，可由位于数据处理装置的中央处理器(Central Processing Unit，CPU)、微处理器(Micro Processor Unit，MPU)、数字信号处理器(Digital Signal Processor，DSP)、或现场可编程门阵列(Field Programmable Gate Array，FPGA)实现。
本发明实施例所记载的技术方案之间，在不冲突的情况下，可以任意组合。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、 或计算机程序产品。因此，本发明可采用硬件实施例、软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明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。
本发明是参照根据本发明实施例的方法、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。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。
工业实用性
本发明实施例所述数据处理方法，获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。如此，能够实现基于MIMO MLD的性能抽象，在简化整个计算性能复杂度和保证计算精度的基础上，有效节省MIMO系统性能确定对存储空间和仿真时间的消耗。

Claims (11)

  1. 一种数据处理方法，所述方法包括：
    获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；
    利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；
    根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。
  2. 根据权利要求1所述的方法，其中，所述根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能，包括：
    根据所述第二信道估计矩阵中的主对角线元素确定性能基础索引；
    结合所述预置的性能关系曲线集、所确定的性能基础索引和所述第二信道估计矩阵中的非主对角线元素，得到对应于被检测层的信息交互值；
    根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能。
  3. 根据权利要求2所述的方法，其中，所述根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能，包括：
    根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素，采用二维线性插值法来确定所述被检测层的性能。
  4. 根据权利要求1所述的方法，其中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述被检测层的信道估计矩阵。
  5. 根据权利要求1所述的方法，其中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述系统空间中区别于所述被检测层的信道估计矩阵的列置换矩阵。
  6. 一种数据处理装置，所述装置包括获取模块、第一确定模块和第二确定模块；
    所述获取模块，配置为获取对应于系统空间中被检测层的第一信道估计矩阵和噪声均方差；
    所述第一确定模块，配置为利用所述第一信道估计矩阵和噪声均方差确定归一化的第二信道估计矩阵；
    所述第二确定模块，配置为根据所述第二信道估计矩阵和预置的性能关系曲线集确定所述被检测层的性能。
  7. 根据权利要求6所述的装置，其中，所述第二确定模块包括索引确定单元、信息交互值确定单元和性能确定单元；
    所述索引确定单元，配置为根据所述第二信道估计矩阵中的主对角线元素确定性能基础索引；
    所述信息交互值确定单元，配置为结合所述预置的性能关系曲线集、所确定的性能基础索引和所述第二信道估计矩阵中的非主对角线元素，得到对应于被检测层的信息交互值；
    所述性能确定单元，配置为根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素确定所述被检测层的性能。
  8. 根据权利要求7所述的装置，其中，
    所述性能确定单元，配置为根据所述信息交互值和所述第二信道估计矩阵中的主对角线元素，采用二维线性插值法来确定所述被检测层的性能。
  9. 根据权利要求6所述的装置，其中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述被检测层的信道估计矩阵。
  10. 根据权利要求6所述的装置，其中，所述对应于系统空间中被检测层的第一信道估计矩阵为所述系统空间中区别于所述被检测层的信道估计矩阵的列置换矩阵。
  11. 一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令用于执行权利要求1至5任一项所述数据 处理方法。
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