WO2018153178A1 - 一种基于mimo系统的信号检测方法及装置、存储介质 - Google Patents

一种基于mimo系统的信号检测方法及装置、存储介质 Download PDF

Info

Publication number
WO2018153178A1
WO2018153178A1 PCT/CN2018/072586 CN2018072586W WO2018153178A1 WO 2018153178 A1 WO2018153178 A1 WO 2018153178A1 CN 2018072586 W CN2018072586 W CN 2018072586W WO 2018153178 A1 WO2018153178 A1 WO 2018153178A1
Authority
WO
WIPO (PCT)
Prior art keywords
covariance matrix
whitening
matrix
scaling
scaling factor
Prior art date
Application number
PCT/CN2018/072586
Other languages
English (en)
French (fr)
Inventor
董雪涛
Original Assignee
深圳市中兴微电子技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳市中兴微电子技术有限公司 filed Critical 深圳市中兴微电子技术有限公司
Priority to US16/487,866 priority Critical patent/US10707932B2/en
Publication of WO2018153178A1 publication Critical patent/WO2018153178A1/zh

Links

Images

Classifications

    • 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
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/046—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account
    • 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
    • H04B7/0413—MIMO systems
    • 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
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • 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
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0046—Code rate detection or code type detection
    • 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
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0047—Decoding adapted to other signal detection operation
    • 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
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0054—Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms
    • 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
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0055—MAP-decoding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03012—Arrangements for removing intersymbol interference operating in the time domain
    • H04L25/03019—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception
    • H04L25/03057—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception with a recursive structure
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03178—Arrangements involving sequence estimation techniques
    • H04L25/03305—Joint sequence estimation and interference removal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/0335—Arrangements for removing intersymbol interference characterised by the type of transmission
    • H04L2025/03426—Arrangements for removing intersymbol interference characterised by the type of transmission transmission using multiple-input and multiple-output channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03611—Iterative algorithms
    • H04L2025/03617—Time recursive algorithms
    • H04L2025/03624—Zero-forcing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03611—Iterative algorithms
    • H04L2025/03636—Algorithms using least mean square [LMS]

Definitions

  • the present invention relates to a signal detection technology, and in particular to a signal detection method and apparatus based on a multiple-input multiple-output (MIMO) system, and a computer storage medium.
  • MIMO multiple-input multiple-output
  • LTE-A Long Term Evolution-Advanced
  • R3/R13 3rd Generation Partnership Project
  • UE User Equipment
  • the terminal is required to support the network assistant interference cancellation and suppression (NAICS, Network Assistant Interference Cancelation and Suppression) receiving algorithm.
  • NAICS Network Assistant Interference Cancelation and Suppression
  • a typical NAICS receiver can only solve one strong interference neighboring area, and other neighboring areas are Seen as random interference.
  • a UE detects a transmit signal in a MIMO system by using a received signal, a channel estimate, and an interference noise covariance matrix.
  • the channel and transmit symbols of one strong interfering neighbor that are resolved are equivalently included in H and X, while the interference of other cells is reflected in N.
  • the UE can estimate R by various methods.
  • the parameter value is the interference noise covariance matrix.
  • FIG. 1 is a schematic diagram of an implementation process of signal detection in the prior art; as shown in FIG. 1:
  • Input k, Y, and H estimates of X detected by algorithms such as ZF, MMSE, and R-ML (including SD) in the detection unit
  • the corresponding parameter estimation of the neighboring area is obtained.
  • R w can have k.
  • Form (c) is a common method in the industry because the lower triangular matrix is more convenient to reverse.
  • FIG. 2 is a schematic diagram of an implementation process of signal detection by a whitening matrix in the prior art; as shown in FIG. 2:
  • the whitening matrix W is obtained by the covariance matrix R, and then the whitening matrix W and the Y and H in the system equation are used as the input of the whitening unit, and the whitening calculation is performed on Y and H to obtain the whitened Y W and H W , and then Estimation of X obtained by whitening Y W and H W detected by algorithms such as ZF, MMSE and R-ML (including SD) in the detection unit In the NAICS blind detection parameter process, the corresponding parameter estimation of the neighboring area is obtained.
  • Figure 2 shows that k is always a fixed constant "1", which means that no matter how much interference noise, it is eventually equivalent to the white noise level of "1".
  • the interference noise power itself is small, whitening will cause Y and H to be numerically amplified many times into Y W and H W ;
  • the interference noise power itself Large, whitening will make Y and H numerically shrink many times into Y W and H W , so Y W and H W vary widely, requiring a large bit width to represent, which will greatly increase the detection unit.
  • the area of signal detection by ZF, MMSE, and R-ML algorithms including SD.
  • the numerical range of R is directly caused to be large, so that the process area for calculating the whitening matrix W by using the covariance matrix R is also relatively large.
  • embodiments of the present invention are expected to provide a signal detection method and apparatus based on a MIMO system, and a computer storage medium, which can reduce the bit width and the area of a digital model used for whitening operations.
  • a signal detection method based on a MIMO system comprising: pairing the first covariance matrix according to a first principal diagonal element in the first covariance matrix Performing a scaling calculation to obtain a second covariance matrix;
  • the transmission signal in the MIMO system is detected according to the operation result, and the detection result is obtained.
  • the first covariance matrix is scaled according to the first main diagonal element in the first covariance matrix to obtain a second covariance matrix, including:
  • the first scaling factor can also be directly used, and it is not necessary to quantize the second scaling factor for reuse, that is, the direct use is not excluded. The possibility of the first scaling factor.
  • the first scaling factor is a mean value, a maximum value, or a minimum value of the first main diagonal element.
  • the detecting, according to the operation result, the detection signal in the MIMO system, and obtaining the detection result including:
  • the transmission signal in the MIMO system is detected and calculated by the zero-forcing ZF algorithm, the minimum mean square error MMSE algorithm or the simplified maximum likelihood method R-ML algorithm, and the detection result is obtained.
  • a signal detection apparatus based on a MIMO system comprising:
  • the scaling unit is configured to perform scaling calculation on the first covariance matrix according to a first main diagonal element in the first covariance matrix to obtain a second covariance matrix;
  • the whitening unit is configured to obtain a whitening matrix according to the second covariance matrix obtained by the scaling unit; input the whitening matrix, the received signal vector and the channel matrix as input parameters, and input a mathematical model for whitening operation The calculation result is obtained after performing the whitening calculation;
  • the detecting unit is configured to detect a transmit signal in the MIMO system according to the operation result obtained by the whitening unit, to obtain a detection result.
  • the scaling unit is further configured to obtain a first scaling factor according to the first main diagonal element in the first covariance matrix, and quantize the first scaling factor into a power form of 2.
  • the first scaling factor can also be directly used, and it is not necessary to quantize the second scaling factor for reuse, that is, the direct use is not excluded. The possibility of the first scaling factor.
  • the first scaling factor is a mean value, a maximum value, or a minimum value of the first main diagonal element.
  • the detecting unit is further configured to perform a detection calculation on the transmit signal in the MIMO system by using a ZF algorithm, an MMSE algorithm, or an R-ML algorithm according to the operation result, to obtain a detection result.
  • the scaling unit, the whitening unit, and the detecting unit may use a central processing unit (CPU), a digital signal processor (DSP, Digital Singnal Processor), or a programmable logic array (FPGA) when performing processing. , Field-Programmable Gate Array) implementation.
  • CPU central processing unit
  • DSP digital signal processor
  • FPGA programmable logic array
  • Embodiments of the present invention also provide a computer storage medium in which computer executable instructions are stored, the computer executable instructions configured to perform the above MIMO system based signal detection method.
  • An embodiment of the present invention provides a signal detection method and apparatus based on a MIMO system, and performing scaling calculation on the first covariance matrix according to a first main diagonal element in the first covariance matrix to obtain a second association.
  • a variance matrix obtaining a whitening matrix according to the second covariance matrix; taking the whitening matrix, the vector of the received signal, and the channel matrix as input parameters, inputting a mathematical model for whitening operation, performing a whitening calculation to obtain an operation result;
  • the operation result detects the transmitted signal in the MIMO system, and obtains the detection result.
  • the covariance matrix is scaled and calculated by the main diagonal elements in the covariance matrix, and the covariance matrix is normalized, so that the numerical concentration in the scaled covariance matrix is no longer diverged, and the reduction is achieved.
  • the bit width and the area of the digital model used for the whitening operation are reduced; in addition, by converting the first scaling factor into a power of 2, the division operation becomes a shift operation, further saving resources.
  • FIG. 1 is a schematic diagram of an implementation process in signal detection in the prior art
  • FIG. 2 is a schematic diagram of an implementation process of signal detection by a whitening matrix in the prior art
  • FIG. 3 is a flowchart of implementing a signal detection method based on a MIMO system according to an embodiment of the present invention
  • FIG. 4 is a schematic flowchart of an implementation process of detecting a transmit signal in a MIMO system according to an embodiment of the present invention
  • FIG. 5 is a schematic diagram of a specific implementation process of scaling calculation of R by using a scaling unit according to an embodiment of the present invention
  • FIG. 6 is a schematic structural diagram of a signal detecting apparatus based on a MIMO system according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of implementing a signal detection method based on a MIMO system according to an embodiment of the present invention; as shown in FIG. 3, the method includes:
  • Step 301 Acquire a first covariance matrix of interference noise, and perform scaling calculation on the first covariance matrix according to a first main diagonal element in the first covariance matrix to obtain a second covariance matrix.
  • the method is mainly implemented in a MIMO system, where the first covariance matrix R is a positive definite Hermitian matrix, according to the mean, maximum or minimum value of the first main diagonal element in the first covariance matrix.
  • the first covariance matrix is scaled to obtain a second covariance matrix, which can implement normalization processing on the first covariance matrix, and the value set of the obtained second covariance matrix is no longer diverged, thereby reducing The bit width.
  • Step 302 Obtain a whitening matrix according to the second covariance matrix; use the whitening matrix, the vector of the received signal, and the channel matrix as input parameters, input the mathematical model used for the whitening operation, perform a whitening calculation, and obtain an operation result;
  • the whitening matrix W is obtained according to the second covariance matrix, and then the whitening matrix W, the vector Y of the received signal, and the channel matrix H are used as input parameters, and are input into a digital model for whitening operation to perform whitening calculation. After that, Y W and H W after whitening are obtained.
  • step 303 the transmit signal in the MIMO system is detected according to the operation result, and the detection result is obtained.
  • the whitened Y W and H W are passed through a Zero Forcing (ZF) algorithm, a Minimum Mean Square Error (MMSE) algorithm, or a simplified Maximum Likelihood (R-ML, Reduced Maximum Likelihood).
  • ZF Zero Forcing
  • MMSE Minimum Mean Square Error
  • R-ML Reduced Maximum Likelihood
  • FIG. 4 is a schematic flowchart of implementing detection of a transmit signal in a MIMO system according to an embodiment of the present invention. As shown in FIG. 4:
  • an appropriate first scaling factor k value is selected, and then the whitening matrix W, the vector Y of the received signal, and the channel matrix H are used as input parameters of the whitening unit, and are input for the whitening operation.
  • the calculation result is obtained after the whitening calculation is performed in the mathematical model; and the detection unit calculates and calculates the transmission signal in the MIMO system by using the ZF algorithm, the MMSE algorithm or the R-ML algorithm according to the operation result, and obtains the detection result.
  • the value of k selected according to the main diagonal element of R is as shown in FIG.
  • FIG. 5 is a schematic diagram of a specific implementation process of scaling calculation of R by using a scaling unit according to an embodiment of the present invention. As shown in FIG. 5:
  • the main diagonal element r ii of R is used as an input for calculating the first scaling factor, for example, the first scaling factor k is the mean, maximum or minimum value of the R main diagonal element, but is not limited to this method, including all A method for obtaining a first scaling factor k value based on a main diagonal element, such as proper scaling, etc., thereby normalizing R, so that the resulting R 0 value set is no longer diverged, which reduces the bit width, thereby reducing the calculation The area of the whitened matrix module.
  • the first covariance matrix is scaled according to the first main diagonal element in the first covariance matrix to obtain a second covariance matrix, including:
  • the first scaling factor k may be quantized. Specifically, the first scaling factor may be the mean value, the maximum value or the minimum value of the first main diagonal element R, and other values, and then adopt the closest 2
  • the range of W has shrunk. It should be noted that the whitening unit for calculating W is actually implemented by the circuit. Since the range of R 0 is concentrated, the process bit width of the calculation W is also greatly reduced, thereby reducing the area of the whitening unit of the calculation W.
  • the whitening matrix W, the vector Y of the received signal, and the channel matrix H are used as input parameters, and are input into a mathematical model for whitening operation for whitening calculation:
  • the range of W has shrunk. It should be noted that the whitening unit for calculating W is actually implemented by the circuit. Since the range of R 0 is concentrated, the process bit width of the whitening unit to calculate W is also greatly reduced, thereby reducing the realized area of the whitening unit calculation W.
  • FIG. 6 is a schematic structural diagram of a signal detecting apparatus based on a MIMO system according to an embodiment of the present invention; as shown in FIG. 6, the apparatus includes: a scaling unit 601, a whitening unit 602, and a detecting unit 603;
  • the scaling unit 601 is configured to perform scaling calculation on the first covariance matrix according to a first main diagonal element in the first covariance matrix to obtain a second covariance matrix;
  • the whitening unit 602 is configured to obtain a whitening matrix according to the second covariance matrix obtained by the scaling unit 601; and input the whitening matrix, the vector of the received signal, and the channel matrix as input parameters, and input the whitening operation
  • the calculation result is obtained after the whitening calculation in the mathematical model;
  • the detecting unit 603 is configured to detect a transmit signal in the MIMO system according to the operation result obtained by the whitening unit 602, to obtain a detection result.
  • the first covariance matrix of the interference noise is defined by the scaling unit 601.
  • the first covariance matrix R is a positive definite Hermitian matrix according to the mean and maximum of the first main diagonal elements in the first covariance matrix.
  • the value or the minimum value is scaled to the first covariance matrix to obtain a second covariance matrix.
  • the normalization process of the first covariance matrix can be realized, and the numerical concentration of the obtained second covariance matrix is no longer diverged, thereby reducing the bit width.
  • the whitening unit W obtains the whitening matrix W according to the second covariance matrix, and then inputs the whitening matrix W, the vector Y of the received signal, and the channel matrix H as input parameters, and inputs a digital model for whitening operation.
  • Y W and H W after whitening are obtained.
  • the scaling unit 601 is further configured to obtain a first scaling factor according to a first primary diagonal element in the first covariance matrix, and quantize the first scaling factor to 2 a power factor form, obtaining a second scaling factor; performing scaling calculation on the first covariance matrix according to the second scaling factor to obtain a second covariance matrix.
  • a specific implementation flow of performing scaling calculation on R by the scaling unit 601 is described with reference to FIG. 5. This reduces resources by replacing the division in the covariance matrix with a shift operation.
  • embodiments of the present invention can be provided as a method, system, or 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 first covariance matrix is scaled according to the first main diagonal element in the first covariance matrix to obtain a second covariance matrix; according to the second covariance matrix Obtaining a whitening matrix; taking the whitening matrix, the vector of the received signal, and the channel matrix as input parameters, inputting a mathematical model for whitening operation, performing a whitening calculation to obtain an operation result; and transmitting a signal according to the operation result to the MIMO system
  • the test is performed and the test result is obtained.
  • the covariance matrix is scaled and calculated by the main diagonal elements in the covariance matrix, and the covariance matrix is normalized, so that the numerical concentration in the scaled covariance matrix is no longer diverged, and the reduction is achieved.
  • the bit width and the area of the digital model used for the whitening operation are reduced; in addition, by converting the first scaling factor into a power of 2, the division operation becomes a shift operation, saving resources.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Radio Transmission System (AREA)

Abstract

本发明公开了一种基于MIMO系统的信号检测方法，所述方法包括：根据第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；根据所述第二协方差矩阵得到白化矩阵；将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；根据所述运算结果对多输入多输出(MIMO)系统中的发射信号进行检测，得到检测结果。本发明还同时公开了一种基于MIMO系统的信号检测装置、计算机存储介质。

Description

一种基于MIMO系统的信号检测方法及装置、存储介质
相关申请的交叉引用
本申请基于申请号为201710098805.8、申请日为2017年02月23日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本发明涉及信号检测技术，具体涉及一种基于多输入多输出(MIMO，Multiple-Input Multiple-Output)系统的信号检测方法及装置、计算机存储介质。
背景技术
在长期演进技术升级版(LTE-A，Long Term Evolution-Advanced)系统中，尤其是在第三代合作伙伴计划(3GPP，3rd Generation Partnership Project)R12/R13规范中异构网越来越重要、微小区部署越来越密集。这种越来越密集的微小区部署导致用户设备(UE，User Equipment)在微小区边缘面临越来越强的宏小区或微小区干扰。从3GPP R12规范开始，要求终端支持网络辅助干扰消除和抑制(NAICS，Network Assistant Interference Cancelation and Suppression)的接收算法，一个典型的NAICS接收机只能解决一个强干扰邻区，其他的邻区则被看作是随机干扰。
现有技术中，UE通过接收信号、信道估计和干扰噪声协方差矩阵对MIMO系统中的发射信号进行检测。
具体地，接收信号的系统方程为：Y＝HX+N；其中Y是接收信号向量，H是信道矩阵，X是发射符号向量，N是干扰噪声向量，这里，干扰噪声向量包含干扰和白噪声两部分。对于NAICS系统，被解决的1个强干扰邻区的信道和发射符号等效包含在H和X中，而其他小区的干扰则体现在N 中。
定义R为干扰噪声协方差矩阵，即R ＝E(NN H)，其中，R为正定Hermitian矩阵，NN H中的H表示复共轭转置矩阵，这里，UE可以通过多种方法估计出R的参数值。
现有技术中，UE的信号检测以及NAICS中检测邻区参数时，通常用ZF、MMSE、ML以及R-ML(包括SD)等算法进行，而这些算法都只适用于R＝k﹒I的情况，其中，k为实常系数，代表白噪声功率，I为单位矩阵。具体如图1所示。
图1为现有技术中信号检测的实现过程示意图；如图1所示：
输入k、Y和H，通过检测单元中的ZF、MMSE以及R-ML(包括SD)等算法检测得到的X的估计
Figure PCTCN2018072586-appb-000001
，在NAICS盲检参数中则得到邻区对应的参数估计。
当有邻区干扰时，R就不再满足k﹒I的性质，对于MMSE算法，可以用MMSE-IRC干扰抑制合并(IRC，Interference Rejection Combining)检测，而其他算法则无法进行有效的干扰抑制。于是需要利用白化矩阵W，对系统方程进行白化处理，即WY＝WHX+WN，那么系统方程等效变形为：Y w＝H wX+N w，其中Y W＝WY，H W＝WH，而白化后的协方差矩阵R w为：
Figure PCTCN2018072586-appb-000002
适当选取W，使得R w具有k﹒I的形式。例如：
(a)取
Figure PCTCN2018072586-appb-000003
(b)R -1进行cholesky分解，R -1＝U H·U，其中U为上三角矩阵，取W＝U；
(c)R进行cholesky分解，R＝L·L H，其中L为下三角矩阵，取W＝L -1。
利用上述(a)、(b)、(c)三种W(不限于这三种形式)，均能使R w具有k﹒I的形式。其中形式(c)为业内常用方式，因为下三角矩阵更方便求逆。下面以形式(c)为例进行证明：
W＝L -1代入白化后的协方差矩阵R w，则R w＝WRW H＝L -1RL -H＝L -1L·L HL -H＝I＝1·I；
该证明说明了白化后R w不但有k﹒I的形式，而且k还为固定常数“1”，同理可证(a)和(b)也有一样的结果。因此UE只需要增加一个白化单元就可以使用原来的各种检测算法，具体如图2所示。
图2为现有技术中通过白化矩阵进行信号检测的实现过程示意图；如图2所示：
通过协方差矩阵R得到白化矩阵W，然后再将白化矩阵W和系统方程中的Y和H作为白化单元的输入，对Y和H进行白化计算，得到白化后的Y W和H W，再将白化后的Y W和H W通过检测单元中的ZF、MMSE以及R-ML(包括SD)等算法检测得到的X的估计
Figure PCTCN2018072586-appb-000004
，在NAICS盲检参数过程中则得到邻区对应的参数估计。
图2显示k总为固定常数“1”，他的含义是无论干扰噪声多大，最终都被等效到“1”的白噪声水平上。当UE处于很低干扰噪声环境时，干扰噪声功率本身很小，白化会使Y和H在数值上放大很多倍变成Y W和H W；当UE处于高干扰噪声环境时，干扰噪声功率本身很大，白化会使Y和H在数值上缩小很多倍变成Y W和H W，从而Y W和H W的变化范围非常大，需要很大的位宽才能表示，这样会大大增加检测单元通过ZF、MMSE以及R-ML算法(包括SD)进行信号检测的面积。同时，由于干扰环境时常变化，直接导致R的数值范围也很大，使得利用协方差矩阵R计算白化矩阵W的过程面积也相对较大。
发明内容
为解决现有存在的技术问题，本发明实施例期望提供一种基于MIMO系统的信号检测方法及装置、计算机存储介质，能够降低位宽和用于白化运算的数字模型的面积。
本发明实施例的技术方案是这样实现的：
根据本发明实施例的一方面，提供一种基于MIMO系统的信号检测方法，所述方法包括：根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；
根据所述第二协方差矩阵得到白化矩阵；
将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；
根据所述运算结果对多输入多输出MIMO系统中的发射信号进行检 测，得到检测结果。
上述方案中，所述根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵，包括：
根据所述第一协方差矩阵中的第一主对角线元素得到第一缩放因子，并将所述第一缩放因子量化为2的幂次形式，得到第二缩放因子；
根据所述第一缩放因子或第二缩放因子对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。需要指出的是：除了将第一缩放因子量化后得到的第二缩放因子，该第一缩放因子也可以直接拿来用，不一定非得量化成为第二缩放因子再用，也就是不排除直接用第一缩放因子的可能。
上述方案中，所述第一缩放因子为第一主对角线元素的均值、最大值或最小值。
上述方案中，所述根据所述运算结果对MIMO系统中的发射信号进行检测，得到检测结果，包括：
根据所述运算结果通过迫零ZF算法、最小均方误差MMSE算法或简化的最大似然法R-ML算法对所述MIMO系统中的发射信号进行检测计算，得到检测结果。
根据本发明实施例的另一方面，提供一种基于MIMO系统的信号检测装置，所述装置包括：
缩放单元、白化单元和检测单元；其中，
所述缩放单元，配置为根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；
所述白化单元，配置为根据所述缩放单元得到的所述第二协方差矩阵得到白化矩阵；将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；
所述检测单元，配置为根据所述白化单元得到的所述运算结果，对MIMO系统中的发射信号进行检测，得到检测结果。
上述方案中，所述缩放单元，还配置为根据所述第一协方差矩阵中的第一主对角线元素得到第一缩放因子，并将所述第一缩放因子量化为2的 幂次形式，得到第二缩放因子；根据所述第一缩放因子或所述第二缩放因子对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。需要指出的是：除了将第一缩放因子量化后得到的第二缩放因子，该第一缩放因子也可以直接拿来用，不一定非得量化成为第二缩放因子再用，也就是不排除直接用第一缩放因子的可能。
上述方案中，所述第一缩放因子为第一主对角线元素的均值、最大值或最小值。
上述方案中，所述检测单元，还配置为根据所述运算结果通过ZF算法、MMSE算法或R-ML算法对所述MIMO系统中的发射信号进行检测计算，得到检测结果。
所述缩放单元、所述白化单元和所述检测单元在执行处理时，可以采用中央处理器(CPU，Central Processing Unit)、数字信号处理器(DSP，Digital Singnal Processor)或可编程逻辑阵列(FPGA，Field－Programmable Gate Array)实现。
本发明实施例还提供一种计算机存储介质，其中存储有计算机可执行指令，该计算机可执行指令配置执行上述基于MIMO系统的信号检测方法。
本发明实施例提供一种基于MIMO系统的信号检测方法及装置，根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；根据所述第二协方差矩阵得到白化矩阵；将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；根据所述运算结果对MIMO系统中的发射信号进行检测，得到检测结果。如此，通过协方差矩阵中的主对角线元素对协方差矩阵进行缩放计算，实现对协方差矩阵进行归一化处理，从而使得缩放后的协方差矩阵中的数值集中不再发散，达到降低位宽和降低用于白化运算的数字模型的面积；另外，通过对第一缩放因子转换成2的幂次形式，使得除法运算变为移位运算，进一步节省了资源。
附图说明
图1为现有技术中信号检测时的实现过程示意图；
图2为现有技术中通过白化矩阵进行信号检测的实现过程示意图；
图3为本发明实施例一种基于MIMO系统的信号检测方法的实现流程图；
图4为本发明实施例中对MIMO系统中的发射信号进行检测的实现流程示意图；
图5为本发明实施例中利用缩放单元对R进行缩放计算的具体实现流程示意图；
图6为本发明实施例一种基于MIMO系统的信号检测装置的组成结构示意图。
具体实施方式
下面结合附图对本发明的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本发明，并不用于限制本发明。
图3为本发明实施例一种基于MIMO系统的信号检测方法的实现流程图；如图3所示，所述方法包括：
步骤301、获取干扰噪声的第一协方差矩阵，根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。
这里，所述方法主要应用于MIMO系统中实现，第一协方差矩阵R为正定Hermitian矩阵，根据所述第一协方差矩阵中第一主对角线元素的均值、最大值或最小值对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵，能够实现对第一协方差矩阵的归一化处理，并使所得到的第二协方差矩阵的数值集中不再发散，从而降低了位宽。
步骤302、根据所述第二协方差矩阵得到白化矩阵；将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型 中进行白化计算后得到运算结果；
这里，具体根据所述第二协方差矩阵得到白化矩阵W，然后将所述白化矩阵W、接收信号的向量Y及信道矩阵H作为输入参数，输入用于白化运算的数字模型中，进行白化计算后，得到白化后的Y W和H W。
这步骤303、根据所述运算结果对MIMO系统中的发射信号进行检测，得到检测结果。
这里，将白化后的Y W和H W通过迫零(ZF，Zero Forcing)算法、最小均方误差(MMSE，Minimum Mean Square Error)算法或简化的最大似然法(R-ML，Reduced Maximum Likelihood)算法对所述MIMO系统中的发射信号进行检测计算，得到检测结果。
图4为本发明实施例中对MIMO系统中的发射信号进行检测的实现流程示意图；如图4所示：
以对R进行cholesky分解，R＝L·L H，其中L为下三角矩阵，取W＝L -1为例，通过缩放单元对R进行缩放计算，得到R 0，其中，
Figure PCTCN2018072586-appb-000005
然后，用R 0得到白化矩阵W，
Figure PCTCN2018072586-appb-000006
令 则
Figure PCTCN2018072586-appb-000008
之后，依据R的主对角线元素选择合适的第一缩放因子k值，再将所述白化矩阵W、接收信号的向量Y及信道矩阵H作为白化单元的输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；再由检测单元根据所述运算结果通过ZF算法、MMSE算法或R-ML算法对所述MIMO系统中的发射信号进行检测计算，得到检测结果。具体地，依据R的主对角线元素选择k值如图5所示。
图5为本发明实施例中利用缩放单元对R进行缩放计算的具体实现流程示意图；如图5所示：
使用R的主对角元素r ii作为计算第一缩放因子的输入，例如令第一缩放因子k为R主对角线元素的均值，最大值或最小值，但不限于此种方法，包含所有基于主对角元素获取第一缩放因子k值的方法，比如适当缩放等，从而对R有归一化的作用，使所得R 0数值集中不再发散，这就降低了位宽，从而降低计算白化矩阵模块的面积。同时，k不再是固定值“1”，与本来的 干扰噪声在数值大小上变化不大，从而Y w和H w的数值范围变化也不大，使得后续通过ZF、MMSE、R-ML(包括SD)单元也不必增加位宽，优化了信号检测的实现面积。
在本发明实施例中，所述根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵，包括：
根据所述第一协方差矩阵中的第一主对角线元素得到第一缩放因子，并将所述第一缩放因子量化为2的幂次形式，得到第二缩放因子；
根据所述第二缩放因子对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。
这里，可以对第一缩放因子k进行量化，具体地，所述第一缩放因子可以为第一主对角线元素R的为均值、最大值或最小值以及其他值，然后采用最接近的2的幂次形式的数值作为第二缩放因子k，即k＝2 m，其中m为整数。这样可以将
Figure PCTCN2018072586-appb-000009
中的除法用移位运算代替，而移位运算不耗费资源，即R 0＝2 -m·R。例如：
Figure PCTCN2018072586-appb-000010
Figure PCTCN2018072586-appb-000011
(1)如果不对R进行缩放计算，而是直接进行白化计算，则对R进行cholesky分解后，R＝L·L H，则：
Figure PCTCN2018072586-appb-000012
得到白化矩阵：
Figure PCTCN2018072586-appb-000013
从上式可以看出，W的数值非常大，需要较多整数位，使得位宽变大。
Y和H通过白化处理后，得到：
Figure PCTCN2018072586-appb-000014
Figure PCTCN2018072586-appb-000015
可以看出，Y w和H w和原本的Y和H比较，变大了很多，这就需要更多的整数位来表示，因此增加了位宽，也就增加了检测单元的实现面积。
(2)利用R主对角元素的均值对R进行缩放，
k＝(0.013+0.03185)/2＝0.0215
Figure PCTCN2018072586-appb-000016
对R 0进行cholesky分解，
Figure PCTCN2018072586-appb-000017
Figure PCTCN2018072586-appb-000018
得到白化矩阵
Figure PCTCN2018072586-appb-000019
可见W的范围已经收缩了。需要指出的是，计算W的白化单元实际也是电路实现的，由于R 0的范围集中，使得计算W的过程位宽也大大减小，从而减小了计算W的白化单元面积。
Figure PCTCN2018072586-appb-000020
Figure PCTCN2018072586-appb-000021
可见，R缩放和不缩放比较，Y w和H w没有变得异常大，并且范围和原来的Y和H基本相同，使得后续检测单元通过ZF、MMSE和R-ML(包括SD)等算法进行信号检测时，基本不需要进行位宽调整就可以使用。
本发明实施例一实施方式中，将第一缩放因子k量化为2的幂次形式得到第二缩放因子，k＝0.0215，则最接近的2的幂次形式的数值为2 -6＝0.015625，因此实际可以取k＝2 -6＝0.015625来进一步简化R的缩放过程，通过移位运算代替除法运算，大大降低检测单元的实现面积。
Figure PCTCN2018072586-appb-000022
对R 0进行cholesky分解，
Figure PCTCN2018072586-appb-000023
Figure PCTCN2018072586-appb-000024
白化矩阵
Figure PCTCN2018072586-appb-000025
将所述白化矩阵W、接收信号的向量Y及信道矩阵H作为输入参数，输入用于白化运算的数学模型中进行白化计算：
Figure PCTCN2018072586-appb-000026
Figure PCTCN2018072586-appb-000027
可见W的范围已经收缩了。需要指出的是，计算W的白化单元实际也是电路实现的，由于R 0的范围集中，使得白化单元计算W的过程位宽也大大减小，从而减小了白化单元计算W的实现面积。
图6为本发明实施例一种基于MIMO系统的信号检测装置的组成结构示意图；如图6所示，所述装置包括：缩放单元601、白化单元602和检测单元603；
其中，所述缩放单元601，配置为根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；
所述白化单元602，配置为根据所述缩放单元601得到的所述第二协方差矩阵得到白化矩阵；将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；
所述检测单元603，配置为根据所述白化单元602得到的所述运算结果，对MIMO系统中的发射信号进行检测，得到检测结果。
这里，具体通过所述缩放单元601定义干扰噪声的第一协方差矩阵，第一协方差矩阵R为正定Hermitian矩阵，根据所述第一协方差矩阵中第一主对角线元素的均值、最大值或最小值对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。能够实现对第一协方差矩阵的归一化处理，并使所得到的第二协方差矩阵的数值集中不再发散，从而降低了位宽。
然后，由所述白化单元602根据所述第二协方差矩阵得到白化矩阵W，然后将所述白化矩阵W、接收信号的向量Y及信道矩阵H作为输入参数， 输入用于白化运算的数字模型中，进行白化计算后，得到白化后的Y W和H W。
然后，由所述检测单元603将白化后的Y W和H W通过ZF算法、MMSE算法或R-ML算法对所述MIMO系统中的发射信号进行检测计算，得到检测结果。具体地，对MIMO系统中的发射信号进行检测的实现流程参照图4描述。
在本发明实施例中，所述缩放单元601，还配置为根据所述第一协方差矩阵中的第一主对角线元素得到第一缩放因子，并将所述第一缩放因子量化为2的幂次形式，得到第二缩放因子；根据所述第二缩放因子对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。具体地，利用缩放单元601对R进行缩放计算的具体实现流程参照图5描述。如此通过将协方差矩阵中的除法用移位运算代替，降低了资源。
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，仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。
工业实用性
采用本发明实施例，根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；根据所述第二协方差矩阵得到白化矩阵；将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；根据所述运算结果对MIMO系统中的发射信号进行检测，得到检测结果。如此，通过协方差矩阵中的主对角线元素对协方差矩阵进行缩放计算，实现对协方差矩阵进行归一化处理，从而使得缩放后的协方差矩阵中的数值集中不再发散，达到降低位宽和降低用于白化运算的数字模型的面积；另外，通过对第一缩放因子转换成2的幂次形式，使得除法运算变为移位运算，节省了资源。

Claims (9)

  1. 一种基于MIMO系统的信号检测方法，所述方法包括：
    根据第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；
    根据所述第二协方差矩阵得到白化矩阵；
    将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；
    根据所述运算结果对多输入多输出MIMO系统中的发射信号进行检测，得到检测结果。
  2. 根据权利要求1所述的方法，其中，所述根据所述第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵，包括：
    根据所述第一协方差矩阵中的第一主对角线元素得到第一缩放因子，并将所述第一缩放因子量化为2的幂次形式，得到第二缩放因子；
    根据所述第一缩放因子或第二缩放因子对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。
  3. 根据权利要求2所述的方法，其中，所述第一缩放因子为第一主对角线元素的均值、最大值或最小值。
  4. 根据权利要求1所述的方法，其中，所述根据所述运算结果对MIMO系统中的发射信号进行检测，得到检测结果，包括：
    根据所述运算结果通过迫零ZF算法、最小均方误差MMSE算法或简化的最大似然法R-ML算法对所述MIMO系统中的发射信号进行检测计算，得到检测结果。
  5. 一种基于MIMO系统的信号检测装置，所述装置包括：
    缩放单元、白化单元和检测单元；其中，
    所述缩放单元，配置为根据第一协方差矩阵中的第一主对角线元素对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵；
    所述白化单元，配置为根据所述缩放单元得到的所述第二协方差矩阵 得到白化矩阵；将所述白化矩阵、接收信号的向量及信道矩阵作为输入参数，输入用于白化运算的数学模型中进行白化计算后得到运算结果；
    所述检测单元，配置为根据所述白化单元得到的所述运算结果，对MIMO系统中的发射信号进行检测，得到检测结果。
  6. 根据权利要求5所述的装置，其中，所述缩放单元，具体配置为根据所述第一协方差矩阵中的第一主对角线元素得到第一缩放因子，并将所述第一缩放因子量化为2的幂次形式，得到第二缩放因子；根据所述第一缩放因子或所述第二缩放因子对所述第一协方差矩阵进行缩放计算，得到第二协方差矩阵。
  7. 根据权利要求6所述的装置，其中，所述第一缩放因子为所述第一主对角线元素的均值、最大值或最小值。
  8. 根据权利要求5所述的装置，其中，所述检测单元，具体配置为根据所述运算结果通过ZF算法、MMSE算法或R-ML算法对所述MIMO系统中的发射信号进行检测计算，得到检测结果。
  9. 一种计算机存储介质，其中存储有计算机可执行指令，该计算机可执行指令配置执行所述权利要求1-4中任一项的基于MIMO系统的信号检测方法。
PCT/CN2018/072586 2017-02-23 2018-01-15 一种基于mimo系统的信号检测方法及装置、存储介质 WO2018153178A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US16/487,866 US10707932B2 (en) 2017-02-23 2018-01-15 MIMO system-based signal detection method and device, and storage medium

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710098805.8 2017-02-23
CN201710098805.8A CN108471323A (zh) 2017-02-23 2017-02-23 一种基于mimo系统的信号检测方法及装置

Publications (1)

Publication Number Publication Date
WO2018153178A1 true WO2018153178A1 (zh) 2018-08-30

Family

ID=63253114

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/072586 WO2018153178A1 (zh) 2017-02-23 2018-01-15 一种基于mimo系统的信号检测方法及装置、存储介质

Country Status (3)

Country Link
US (1) US10707932B2 (zh)
CN (1) CN108471323A (zh)
WO (1) WO2018153178A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114172596A (zh) * 2021-12-01 2022-03-11 哲库科技(北京)有限公司 信道噪声检测方法及相关装置
CN116055003A (zh) * 2023-01-05 2023-05-02 湖南大学 数据最优传输方法、装置、计算机设备和存储介质

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3958476A3 (en) * 2020-08-21 2022-03-02 Nokia Technologies Oy Regularization of covariance matrix and eigenvalue decomposition in a mimo system
US11569873B1 (en) 2021-12-23 2023-01-31 Industrial Technology Research Institute MIMO signal symbol detection and search method, decoding circuit and receiving antenna system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130102256A1 (en) * 2011-10-19 2013-04-25 Marvell World Trade Ltd. Systems and methods for suppressing interference in a signal received by a device having two or more antennas
CN103997473A (zh) * 2014-05-13 2014-08-20 华为技术有限公司 一种信号干扰的滤波方法及相关装置
CN104683282A (zh) * 2015-02-16 2015-06-03 中兴通讯股份有限公司 一种支持发射分集的干扰抑制合并方法和装置
CN105763493A (zh) * 2014-12-17 2016-07-13 深圳市中兴微电子技术有限公司 一种信号干扰抑制方法和装置

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7200631B2 (en) * 2003-01-10 2007-04-03 Lucent Technologies Inc. Method and apparatus for determining an inverse square root of a given positive-definite hermitian matrix
EP2260579B1 (en) * 2008-02-25 2011-11-23 Telefonaktiebolaget L M Ericsson (publ) A method of and a device for precoding transmit data signals in a wireless mimo communication system
CN102215072B (zh) * 2010-04-09 2014-06-11 华为技术有限公司 多天线通信系统中信号检测的方法和接收机
EP2640022B1 (en) * 2012-03-14 2016-07-20 Telefonaktiebolaget LM Ericsson (publ) Technique for generating a filter for data reception
US9160383B2 (en) * 2013-11-12 2015-10-13 Huawei Technologies Co., Ltd. Method for estimating covariance matrices and use thereof
US20170324462A1 (en) * 2014-11-28 2017-11-09 ZTE Canada Inc. Unified interference rejection combining
US9729221B2 (en) * 2015-06-26 2017-08-08 Intel IP Corporation Method for feedback reporting and a mobile communications device

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130102256A1 (en) * 2011-10-19 2013-04-25 Marvell World Trade Ltd. Systems and methods for suppressing interference in a signal received by a device having two or more antennas
CN103997473A (zh) * 2014-05-13 2014-08-20 华为技术有限公司 一种信号干扰的滤波方法及相关装置
CN105763493A (zh) * 2014-12-17 2016-07-13 深圳市中兴微电子技术有限公司 一种信号干扰抑制方法和装置
CN104683282A (zh) * 2015-02-16 2015-06-03 中兴通讯股份有限公司 一种支持发射分集的干扰抑制合并方法和装置

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"PDSCH Demodulation Performance of Enhanced SU -MIMO Receiver UE", 3GPP TSG-RAN WG4 #81, R4-1609709, 18 November 2016 (2016-11-18), XP051179934 *
ZHANG, BILING: "A RIS-based Subspace Channel Estimation for MIMO Vir- tual Carrier OFDM Systems", TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), 2016 39TH INTERNATIONAL CON- FERENCE ON, 1 November 2016 (2016-11-01), XP055535092 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114172596A (zh) * 2021-12-01 2022-03-11 哲库科技(北京)有限公司 信道噪声检测方法及相关装置
CN114172596B (zh) * 2021-12-01 2024-04-30 哲库科技(北京)有限公司 信道噪声检测方法及相关装置
CN116055003A (zh) * 2023-01-05 2023-05-02 湖南大学 数据最优传输方法、装置、计算机设备和存储介质

Also Published As

Publication number Publication date
US20200059274A1 (en) 2020-02-20
CN108471323A (zh) 2018-08-31
US10707932B2 (en) 2020-07-07

Similar Documents

Publication Publication Date Title
WO2018153178A1 (zh) 一种基于mimo系统的信号检测方法及装置、存储介质
Shariati et al. Low-complexity polynomial channel estimation in large-scale MIMO with arbitrary statistics
Kang Intelligent reflecting surface: Joint optimal training sequence and refection pattern
Gismalla et al. Performance analysis of the periodogram-based energy detector in fading channels
WO2016131342A1 (zh) 干扰抑制合并方法、装置和存储介质
US8861651B2 (en) Process for performing a QR decomposition of a channel matrix in a MIMO wireless communication system, and receiver for doing the same
Chen et al. A robust diffusion estimation algorithm for asynchronous networks in IoT
Ghods et al. Optimally-tuned nonparametric linear equalization for massive MU-MIMO systems
TWI530116B (zh) 用於無線網路中通道估計的方法與設備
Georgiadis et al. Adaptive Bayesian decision feedback equaliser for alpha-stable noise environments
EP3468080A1 (en) System and method for selecting transmission parameters for rank two mimo transmission
Liu et al. Distributed blind estimation over sensor networks
US20150358089A1 (en) Joint spatial processing for space frequency block coding and/or non space frequency block coding channels
Gabet et al. Effective channel order estimation based on nullspace structure and exponential fit
Meng et al. Efficient co-channel interference suppression in MIMO-OFDM systems
Kim et al. Communication equalizer algorithms with decision feedback based on error probability
TWI810146B (zh) 在無線通訊系統中干擾之預先編碼的矩陣索引的盲蔽偵測的裝置與方法
Faraji et al. Fixed-point implementation of interpolation-based MMSE MIMO detector in joint transmission scenario for LTE-A wireless standard
RU2632417C2 (ru) Способ, система и устройство предварительного кодирования
Kim et al. MMSE-based lattice-reduction-aided fixed-complexity sphere decoder for low-complexity near-ML MIMO detection
Elnakeeb et al. On training sequence optimization for leaked MIMO OFDM channels
KR102541870B1 (ko) 무선 통신 시스템에서 간섭 랭크 정보를 블라인드 탐지하는 장치 및 방법
Jia et al. Blind adaptive identification of 2‐channel systems using bias‐compensated RLS algorithm
WO2017129009A1 (zh) 一种信号检测方法及装置
JP6585565B2 (ja) 通信システムおよび受信装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18756773

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18756773

Country of ref document: EP

Kind code of ref document: A1