WO2015184967A1 - 抑制同频干扰的方法和装置 - Google Patents

抑制同频干扰的方法和装置 Download PDF

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Publication number
WO2015184967A1
WO2015184967A1 PCT/CN2015/080498 CN2015080498W WO2015184967A1 WO 2015184967 A1 WO2015184967 A1 WO 2015184967A1 CN 2015080498 W CN2015080498 W CN 2015080498W WO 2015184967 A1 WO2015184967 A1 WO 2015184967A1
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data
data point
interference
preset
matrix
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PCT/CN2015/080498
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English (en)
French (fr)
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汪浩
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华为技术有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/08—Modifications for reducing interference; Modifications for reducing effects due to line faults ; Receiver end arrangements for detecting or overcoming line faults

Definitions

  • the embodiments of the present invention relate to the field of communications technologies, and in particular, to a method and apparatus for suppressing co-channel interference.
  • LTE Long Term Evolution
  • UMTS Universal Mobile Telecommunications System
  • MIMO Multiple-Input Multiple-Output
  • OFDM Orthogonal Frequency Division Multiplex
  • the user equipment may be interfered by the UE in the same time and frequency resources of the scheduled UE, which may seriously degrade the performance of the demodulated data channel of the UE.
  • the high density and heterogeneity of the base station is the evolution direction of the LTE network structure. This obviously brings more serious inter-cell co-channel interference.
  • Interference Rejection Combining (IRC) and other algorithms are generally used to suppress interference.
  • the IRC algorithm utilizes The correlation data of the pilot point or the data point estimates the statistical characteristics of the interference signal and performs interference whitening based on the statistical characteristic to reduce the influence of the interference signal on the performance of the demodulated data channel of the UE.
  • the key of the IRC algorithm is to accurately estimate the statistical characteristics of the interference signal, that is, to accurately calculate the interference autocorrelation matrix.
  • the demodulation translation is used when calculating the interference autocorrelation matrix. After the code is de-encoded, the data obtained by inverse coding in the low-to-noise ratio (SNR) is very poor, which makes the calculated interference autocorrelation matrix appear very Large deviations, so that interference cannot be effectively suppressed.
  • SNR low-to-noise ratio
  • Embodiments of the present invention provide a method and apparatus for suppressing co-channel interference, which can improve the accuracy of calculating an interference autocorrelation matrix, and can effectively suppress interference by using the matrix.
  • an embodiment of the present invention provides a method for suppressing co-channel interference, the method comprising:
  • the preset area includes a preset number of resource blocks, where the resource block includes the data point and the pilot point;
  • the received signal y is subjected to interference suppression combining using the Ruu '.
  • the acquiring a bit log likelihood ratio of the data point further includes:
  • the new bit log likelihood ratio is taken as the bit log likelihood ratio of the data point.
  • the average algorithm is used to calculate the data according to the bit log likelihood ratio
  • the mean values of the data carried by the points include:
  • the mean calculation formula includes:
  • E(x j ) is the mean of the data carried by the data points
  • x j is the transmitted symbol of the data point
  • Pr(b j,i ) is the probability of i bits b j,i corresponding to x j Calculating Pr(b j,i ) based on the bit log likelihood ratio.
  • the second interference autocorrelation matrix R uu ' includes:
  • the first covariance matrix calculation formula includes:
  • R uu ' is a second interference autocorrelation matrix in the preset area
  • y k and H k are respectively a received signal and an equivalent channel matrix corresponding to the kth pilot point in the preset area
  • p k is the kth pilot symbol
  • y i and H i are respectively the received signal and the equivalent channel matrix of the corresponding i th data point in the preset area
  • x i is the transmitted symbol of the i th data point
  • the second interference autocorrelation matrix R uu ' within also includes:
  • the variance calculation formula includes:
  • E(x j ) is the mean of the data carried by the data point
  • Var(x j ) is the variance of the data carried by the data point
  • x j is the transmitted symbol of the data point
  • the second covariance matrix calculation formula includes:
  • R uu ' is a second interference autocorrelation matrix in the preset area
  • y k and H k are respectively a received signal and an equivalent channel matrix corresponding to the kth pilot point in the preset area
  • p k is the kth pilot symbol
  • y i and H i are respectively the received signal and the equivalent channel matrix of the corresponding i th data point in the preset area
  • x i is the transmitted symbol of the i th data point, when
  • x i is an M-dimensional vector, that is, when x i is M-layer data
  • the method further includes:
  • the data point that meets the preset condition in the preset area needs to be selected as the data point used in the step of calculating the second interference autocorrelation matrix Ruu ′;
  • the data points in the preset area are arbitrarily selected as data points used in the step of calculating the second interference autocorrelation matrix Ruu ′.
  • the data point that meets the preset condition in the preset area is selected as the second interference autocorrelation matrix R
  • the data points used in the uu 'step include:
  • the method before the acquiring a bit log likelihood ratio of the data point, the method further includes:
  • interference suppression combining is performed on the received signal y by using the Ruu .
  • an embodiment of the present invention provides an apparatus for suppressing co-channel interference, the apparatus comprising:
  • a first suppression unit configured to perform, by using a first interference autocorrelation matrix Ruu of the pilot points in the preset region in the time-frequency domain, the received signal y and the equivalent channel matrix H of the data points in the preset region Interference suppression combining, obtaining interference suppression combined signal And equivalent frequency domain channel matrix
  • the preset area includes a preset number of resource blocks, where the resource block includes the data point and the pilot point;
  • a first obtaining unit configured to with Performing multiple input and multiple output MIMO detection and demodulation, and acquiring a bit log likelihood ratio of the data point;
  • a second acquiring unit configured to calculate, according to the mean value algorithm, an average value of data carried by the data point according to the bit log likelihood ratio
  • a third acquiring unit configured to calculate, according to the mean value, a second interference autocorrelation matrix R uu ' in the preset area, according to the covariance matrix algorithm;
  • a second suppression unit configured to perform interference suppression combining on the received signal y by using the Ruu ′.
  • the first acquiring unit is further configured to:
  • the new bit log likelihood ratio is taken as the bit log likelihood ratio of the data point.
  • the second acquiring unit is specifically configured to:
  • the mean calculation formula includes:
  • E(x j ) is the mean of the data carried by the data points
  • x j is the transmitted symbol of the data point
  • Pr(b j,i ) is the probability of i bits b j,i corresponding to x j Calculating Pr(b j,i ) based on the bit log likelihood ratio.
  • the third obtaining unit is specifically configured to:
  • the first covariance matrix calculation formula includes:
  • R uu ' is a second interference autocorrelation matrix in the preset area
  • y k and H k are respectively a received signal and an equivalent channel matrix corresponding to the kth pilot point in the preset area
  • p k is the kth pilot symbol
  • y i and H i are respectively the received signal and the equivalent channel matrix of the corresponding i th data point in the preset area
  • x i is the transmitted symbol of the i th data point
  • the third obtaining unit is further configured to:
  • the variance calculation formula includes:
  • E(x j ) is the mean of the data carried by the data point
  • Var(x j ) is the variance of the data carried by the data point
  • x j is the transmitted symbol of the data point
  • the second covariance matrix calculation formula includes:
  • R uu ' is a second interference autocorrelation matrix in the preset area
  • y k and H k are respectively a received signal and an equivalent channel matrix corresponding to the kth pilot point in the preset area
  • p k is the kth pilot symbol
  • y i and H i are respectively the received signal and the equivalent channel matrix of the corresponding i th data point in the preset area
  • x i is the transmitted symbol of the i th data point, when
  • x i is an M-dimensional vector, that is, when x i is M-layer data
  • the apparatus further includes:
  • a first determining unit configured to calculate a ratio of an average value of the diagonal element modes in the first interference autocorrelation matrix Ruu to an average value of the non-diagonal element modes
  • the data point that meets the preset condition in the preset area needs to be selected as the data point used in the step of calculating the second interference autocorrelation matrix Ruu ′;
  • the data points in the preset area are arbitrarily selected as data points used in the step of calculating the second interference autocorrelation matrix Ruu '.
  • the device further includes:
  • a selection unit configured to select a data point that meets a preset condition in the preset area as a data point used by the third acquiring unit, and select a data point that meets a preset condition in the preset area as the
  • the data points used by the third obtaining unit include:
  • the device further includes:
  • a second determining unit configured to compare a rank of the user equipment with a number of receiving antennas before acquiring a bit log likelihood ratio of the data point
  • interference suppression combining is performed on the received signal y by using the Ruu .
  • the method and apparatus for suppressing co-channel interference provided by the embodiment of the present invention firstly use the first interference autocorrelation matrix Ruu of the pilot points in the preset region in the time-frequency domain to receive the signal y of the data points in the preset area. Perform interference suppression combining with the equivalent channel matrix H to obtain the received signal after interference suppression combining And equivalent frequency domain channel matrix Then right with Performing multi-input and multi-output MIMO detection and demodulation to obtain a bit log likelihood ratio of data points; based on the mean algorithm, calculating a mean value of data carried by the data points according to the bit log likelihood ratio; based on the covariance matrix algorithm, according to the mean value, The second interference autocorrelation matrix R uu ' in the preset region is calculated; finally, the interference suppression merging is performed on the received signal y by using Ruu '. In this way, the accuracy of calculating the interference autocorrelation matrix can be improved, and the matrix can be used to effectively suppress interference.
  • FIG. 1 is a schematic flowchart 1 of a method for suppressing co-channel interference according to an embodiment of the present invention
  • FIG. 2 is a second schematic flowchart of a method for suppressing co-channel interference according to an embodiment of the present invention
  • FIG. 3 is a schematic flowchart of acquiring a bit log likelihood ratio of data points in a preset area according to an embodiment of the present invention
  • FIG. 4 is a schematic diagram of an effect of acquiring a bit log likelihood ratio of data points in a preset area according to an embodiment of the present invention
  • FIG. 5 is a schematic diagram of an effect of acquiring a second interference autocorrelation matrix R uu ' in a preset area according to an embodiment of the present invention
  • FIG. 6 is a schematic structural diagram 1 of an apparatus for suppressing co-channel interference according to an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram 2 of an apparatus for suppressing co-channel interference according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram 3 of an apparatus for suppressing co-channel interference according to an embodiment of the present invention.
  • the embodiment of the invention provides a method for suppressing co-channel interference. As shown in FIG. 1 , the method includes:
  • Step 101 Perform interference suppression on the received signal y and the equivalent channel matrix H of the data points in the preset area by using the first interference autocorrelation matrix R uu of the pilot points in the preset area in the time-frequency domain to obtain interference. Suppressed combined received signal And equivalent frequency domain channel matrix
  • the preset area includes a preset number of resource blocks, where the resource block includes data points and pilot points.
  • Step 102 right with Perform multi-input and multi-output MIMO detection demodulation to obtain the bit log likelihood ratio of the data points.
  • Step 103 Calculate an average value of data carried by the data point according to the bit log likelihood ratio based on the mean algorithm.
  • Step 104 Calculate a second interference autocorrelation matrix R uu ' in the preset region according to the mean value based on the covariance matrix algorithm.
  • Step 105 Perform interference suppression combining on the received signal y by using R uu '.
  • the interference suppression combining of the received signal and the equivalent channel matrix of the data point by using the interference autocorrelation matrix may be interference whitening of the received signal of the data point and the equivalent channel matrix, and interference whitening is only one implementation of interference suppression combining.
  • the specific implementation manner of the interference suppression combining is not limited in the embodiment of the present invention, and a specific implementation manner may be selected by those skilled in the art according to actual conditions.
  • the method for suppressing co-channel interference provided by the embodiment of the present invention firstly uses the first interference autocorrelation matrix R uu of the pilot points in the preset region in the time-frequency domain to receive the signal y and the data points in the preset region.
  • the effect channel matrix H performs interference suppression combining to obtain the received signal after interference suppression combining And equivalent frequency domain channel matrix Then right with Performing multi-input and multi-output MIMO detection and demodulation to obtain a bit log likelihood ratio of data points; based on the mean algorithm, calculating a mean value of data carried by the data points according to the bit log likelihood ratio; based on the covariance matrix algorithm, according to the mean value, Calculating a second interference autocorrelation matrix R uu ' in the preset area; finally performing interference suppression combining on the received signal y by using R uu '.
  • the accuracy of calculating the interference autocorrelation matrix can be improved, and the interference can be effectively suppressed by using the matrix.
  • the method includes:
  • Step 201 Compare the rank of the user equipment with the number of receiving antennas.
  • step 202 is performed;
  • step 203 If the rank of the user equipment is less than the number of received antennas, step 203 and subsequent steps are performed.
  • the rank of the UE that is, the number of data layers. If the rank of the UE is smaller than the number of receiving antennas of the UE, step 202 is performed. Step 202 is actually that the IRC algorithm in the prior art estimates the interference autocorrelation matrix in the preset region by using the pilot point data in the preset region, and uses the obtained interference. The correlation matrix suppresses interference.
  • the RI is greater than or equal to the number of receiving antennas
  • the gain of calculating the interference autocorrelation matrix provided by the embodiment of the present invention compared to the accuracy obtained by the prior art is relatively limited.
  • the prior art scheme can be selected if the RI is greater than or equal to the number of receiving antennas.
  • Step 202 Perform interference suppression combining on the received signal y of the data points in the preset area by using the first interference autocorrelation matrix Ruu of the pilot points in the preset area in the time-frequency domain.
  • the preset area includes a preset number of resource blocks (RBs), where the resource block RB includes consecutive OFDM symbols in the time domain, and includes several consecutive subcarriers in the frequency domain, and each RB
  • the resource element (Resource Element, RE) is included, and the RE is a pilot point or a data point.
  • the number of the RBs and the number of the pilot points and the data points are selected according to actual needs, which is not limited by the present invention.
  • step 202 is actually a scheme for suppressing co-channel interference in the prior art, and an exemplary description is made below:
  • the formula for calculating the first interference autocorrelation matrix R uu of the preset region is as follows:
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the region
  • p k is the kth pilot symbol
  • L p is the number of pilot points.
  • L -1 is used to perform interference suppression combining on the received signal y of the data point.
  • the received signal y (superimposed with the interference signal) may be filtered, that is, the interference signal in the y is interfered and whitened, and the following is obtained:
  • the interference whitening is only one of the implementations of the interference suppression combining.
  • the specific implementation manner of the interference suppression combining is not limited in the embodiment of the present invention, and a specific implementation manner may be selected by a person skilled in the art according to actual conditions. .
  • Step 203 Obtain a bit log likelihood ratio of the data point.
  • the steps of acquiring the bit log likelihood ratio of the data points include:
  • Step 203a Acquire a first interference autocorrelation matrix R uu of pilot points in a preset area.
  • the first interference autocorrelation matrix R uu of the pilot points in the preset area is calculated according to the method given in step 202.
  • Step 203b using R uu to perform interference suppression combining on the received signal y of the data point and the equivalent channel matrix H to obtain the received signal after interference suppression combining And equivalent frequency domain channel matrix
  • the received signal y and the equivalent channel matrix H of the data points in the preset area are interfered and whitened according to the method given in step 202.
  • Step 203c pair with Perform multi-input and multi-output MIMO detection demodulation to obtain the bit log likelihood ratio of the data points.
  • step 203b the received signal of the data point after interference whitening is obtained. And equivalent channel matrix Then, after MIMO detection and demodulation, a Bit Log Likelihood Ratio (BLLR) is obtained.
  • BLLR Bit Log Likelihood Ratio
  • the MIMO detection demodulation to obtain the bit log likelihood ratio can use a variety of methods, the following is an implementation algorithm:
  • bit-log likelihood ratio ⁇ j,i of the i-th bit b j,i of x j is:
  • Pr(b j,i ) is the probability of the i-th bit b j,i corresponding to x j
  • x j is the transmitted symbol of the data point
  • bit log likelihood ratio has been obtained through steps 203a, 203b and 203c, and the bit log likelihood ratio obtained in step 203c can be further processed in order to obtain a more accurate bit log likelihood ratio.
  • Step 203d performing decoding processing, soft value rate matching processing, coding block concatenation processing, and soft value scrambling processing on the bit log likelihood ratio of the data points obtained after detecting and demodulating, and acquiring a new bit log number Rather than.
  • bit log likelihood ratio obtained by detecting demodulation is subjected to soft value rate matching, coded block (CB) concatenation, and soft value scrambling (and related operations in conventional coding).
  • CB coded block
  • soft value scrambling and related operations in conventional coding.
  • the difference is only after the input changes from 0/1 bits to the bit log likelihood ratio), the length/sequence and the bit-log likelihood ratio obtained after demodulation are completely matched to the new bit-log likelihood ratio. .
  • Step 203e using the new bit log likelihood ratio as the bit log likelihood ratio of the data point.
  • the new bit log likelihood ratio obtained in step 203d is taken as the bit log likelihood ratio of the data points to be acquired in step 203.
  • FIG. 4 shows the above two ways of obtaining a bit log likelihood ratio.
  • bit log likelihood ratio obtained after the steps 203a, 203b, and 203c is compared with the bit log likelihood ratio obtained after the steps 203a to 203e, and the bit obtained by the latter method is used.
  • the log likelihood ratio is more accurate.
  • Step 204 Calculate a ratio of an average value of the diagonal element modes in the first interference autocorrelation matrix Ruu of the pilot points to an average value of the non-diagonal element modes.
  • the formula for calculating the ratio of the average of the Ruu diagonal element modulo to the average of the non-diagonal element modulo is as follows:
  • c i and d i are the i-th diagonal and non-diagonal elements in R uu , respectively, and N d and N nd are the number of diagonal and non-diagonal elements, respectively.
  • the Ratio is compared with the first preset threshold Ts. If Ratio>Ts, the data points satisfying the preset condition in the preset area need to be selected as the data points used in the step of calculating the second interference autocorrelation matrix Ruu ′, and the steps are performed.
  • step 206 is a step of selecting data points; if Ratio ⁇ Ts, arbitrarily selecting data point data points in the preset area as a second interference autocorrelation matrix R
  • step 209 is performed.
  • Step 205 Calculate, according to the mean value algorithm, the mean value of the data carried by the data point according to the bit log likelihood ratio and calculate the variance of the data carried by the data point according to the mean value.
  • the mean calculation formula includes:
  • the formula for calculating variance includes:
  • E(x j ) is the mean of the data carried by the data points
  • Var(x j ) is the variance of the data carried by the data points
  • x j is the transmitted symbol of the data points
  • Pr(b j,i ) is the corresponding x j
  • the probability of the i-th bit b j,i is calculated from the bit log likelihood ratio Pr(b j,i ).
  • the formula for calculating Pr(b j,i ) is: with ⁇ j,i is the bit log likelihood ratio of bits b j,i .
  • Step 206 Select a data point that meets the preset condition in the preset area as a data point used in the subsequent calculation of the second interference autocorrelation matrix R uu ' in the preset area.
  • the variance of the data carried by each data point of the obtained preset area is compared with a second preset threshold, where the variance of the data carried by each data point includes the variance of the preset layer data, and the preset layer
  • the number is the number of data layers of the UE; if the variance of the data of any layer of the data point is greater than the second preset threshold, it is determined that the data point does not satisfy the preset condition; if the variance of the data of each layer of the data point is less than or equal to the second Determining a threshold, determining that the data point satisfies a preset condition; if the data point satisfies a preset condition, calculating, as a subsequent step 207 or step 208, a data point used when calculating a second interference autocorrelation matrix R uu ' in the preset region; If the data point does not satisfy the preset condition, the data point is not used when the second interference autocorrelation matrix Ruu ' in the preset region is calculated in the subsequent step
  • the variance Var(x j ) of each layer of data of the data points calculated according to step 205 is compared with a second preset threshold T var , if the variance of any layer of data Var(x j )>T var , Then, the data point does not satisfy the preset condition. If the variance of each layer of data Var(x j ) ⁇ T var , the data point is considered to satisfy the preset condition.
  • Step 207 Calculate, as an input of the first covariance matrix calculation formula, a second interference autocorrelation matrix R uu ' in the preset region.
  • the first covariance matrix calculation formula includes:
  • R uu ' is the interference autocorrelation matrix in the preset region
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the preset region
  • p k is the kth guide
  • the frequency symbols, y i and H i are respectively the received signals corresponding to the i-th data point and the equivalent channel matrix in the preset area
  • x i is the transmitted symbol of the i-th data point
  • x i is an M-dimensional vector, ie, x
  • L is the guide in the preset region participating in the summation
  • the sum of the number k of frequency points and the number i of data points, and the superscript H indicates transposition of the matrix conjugate.
  • Step 208 Calculate the second interference autocorrelation matrix R uu ' in the preset region by using the mean and the variance as the input of the second covariance matrix calculation formula.
  • the calculation formula of the second covariance matrix includes:
  • R uu ' is the interference autocorrelation matrix in the preset region
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the preset region
  • p k is the kth guide
  • the frequency symbols, y i and H i are respectively the received signals corresponding to the i-th data point and the equivalent channel matrix in the preset area
  • x i is the transmitted symbol of the i-th data point
  • x i is an M-dimensional vector, ie, x
  • L is the sum of the number of pilot points k and the number of data points i in the preset region participating in the summation, Used to compensate for errors
  • FIG. 5 is a schematic diagram showing the effect of acquiring the second interference autocorrelation matrix Ruu ′ in the preset region.
  • the interference autocorrelation matrix calculated by the method provided by the embodiment of the present invention is more accurate than the interference autocorrelation matrix calculated by the prior art.
  • Step 209 Perform interference suppression combining on the received signal y by using R uu '.
  • the interference suppression combining of the received signal y may be performing interference whitening on the received signal y.
  • interference suppression combining may be performed by using an interference autocorrelation matrix commonly used in the field, and interference whitening is only one of the implementation manners.
  • the specific implementation manner of the interference suppression combining in the present invention is not limited.
  • R uu LL H
  • L is a lower triangular matrix
  • L -1 can be used to perform interference suppression combining on the interference signals in the preset area.
  • the received signal y (superimposed with the interference signal) of the pilot point can be filtered, that is, the interference signal in the y is interfered and whitened.
  • the correlation of the interference signal in the received signal y is removed, wherein To interfere with the received signal after whitening.
  • GSM Global System for Mobile Communications
  • CDMA Code Division Multiple Access
  • WCDMA code division multiple access
  • TD-SCDMA Time Division-Synchronous Code Division Multiple Access
  • LTE Long Term Evolution
  • the method for suppressing co-channel interference provided by the embodiment of the present invention firstly uses the first interference autocorrelation matrix R uu of the pilot points in the preset region in the time-frequency domain to receive the signal y and the data points in the preset region.
  • the effect channel matrix H performs interference suppression combining to obtain the received signal after interference suppression combining And equivalent frequency domain channel matrix Then right with Performing multi-input and multi-output MIMO detection and demodulation to obtain a bit log likelihood ratio of data points; based on the mean algorithm, calculating a mean value of data carried by the data points according to the bit log likelihood ratio; based on the covariance matrix algorithm, according to the mean value,
  • the second interference autocorrelation matrix R uu ' in the preset region is calculated; finally, the interference suppression merging is performed on the received signal y by using Ruu '. In this way, the accuracy of calculating the interference autocorrelation matrix can be improved, and the interference can be effectively suppressed by using the matrix.
  • An embodiment of the present invention provides a device 00 for suppressing co-channel interference.
  • the device 00 includes: a first suppression unit 10, a first acquisition unit 20, a second acquisition unit 30, and a third acquisition unit 40. And a second suppression unit 50.
  • the first suppression unit 10 is configured to interfere with the received signal y and the equivalent channel matrix H of the data points in the preset area by using the first interference autocorrelation matrix Ruu of the pilot points in the preset area in the time-frequency domain. Suppress the combination and obtain the received signal after interference suppression combining And equivalent frequency domain channel matrix
  • the preset area includes a preset number of resource blocks, where the resource block includes data points and pilot points.
  • the first obtaining unit 20 is configured to with Perform multi-input and multi-output MIMO detection demodulation to obtain the bit log likelihood ratio of the data points.
  • the preset area includes a preset number of resource blocks, where the resource block RB includes (continuous number of OFDM symbols in the time domain, and consecutive number of subcarriers in the frequency domain), and is included in each RB.
  • a number of resource elements (Resources, REs), REs are pilot points or data points, and the number of RBs and the number of pilot points and data points are selected according to actual needs, which is not limited by the present invention.
  • the formula for calculating the first interference autocorrelation matrix R uu of the pilot points of the preset region is as follows:
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the region
  • p k is the kth pilot symbol
  • L p is the number of pilot points.
  • the received signal of the data point after the interference whitening obtained according to the above And equivalent channel matrix The bit log likelihood ratio is then obtained after MIMO detection and demodulation.
  • the MIMO detection demodulation to obtain the bit log likelihood ratio can use a variety of methods, the following is an implementation algorithm:
  • bit-log likelihood ratio ⁇ j,i of the i-th bit b j,i of x j is:
  • Pr(b j,i ) is the probability of the i-th bit b j,i corresponding to x j
  • x j is the transmitted symbol of the data point
  • the second obtaining unit 30 is configured to calculate, according to the mean value algorithm, the mean value of the data carried by the data point according to the bit log likelihood ratio.
  • the third obtaining unit 40 is configured to calculate, according to the mean value, a second interference autocorrelation matrix R uu ' in the preset region according to the covariance matrix algorithm.
  • the second suppression unit 50 is configured to perform interference suppression combining on the received signal y by using R uu '.
  • the first obtaining unit 20 is further configured to:
  • the new bit log likelihood ratio is taken as the bit log likelihood ratio of the data points.
  • the bit log likelihood ratio obtained by detecting demodulation is subjected to soft value rate matching, soft coded block cascading, and soft value scrambling (the basic operation is the same as in the conventional encoding, and the difference is only at the input. From the 0/1 bit to the bit log likelihood ratio), the bit/logarithm ratio obtained after the length/sequence and the demodulated bit-log likelihood ratio is completely matched, and the new bit-pair likelihood ratio is obtained.
  • the number likelihood ratio is similar to the bit log of the data point in the preset area to be acquired by the first acquiring unit 20 Rather than.
  • the second obtaining unit 30 may be specifically configured to:
  • the mean calculation formula includes:
  • E(x j ) is the mean of the data carried by the data points
  • x j is the transmitted symbol of the data point
  • Pr(b j,i ) is the probability of i bits b j,i corresponding to x j , according to the bit pair
  • the number likelihood ratio is calculated as Pr(b j,i ).
  • the third obtaining unit 40 may be specifically configured to:
  • the formula for calculating the first covariance matrix includes:
  • R uu ' is the second interference autocorrelation matrix in the preset region
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the preset region
  • p k is the kth
  • the pilot symbols, y i and H i are respectively the received signal and the equivalent channel matrix corresponding to the i-th data point in the preset area
  • the sum of the number of pilot points k and the number of data points i, the superscript H indicates the conjugate transpose of the matrix.
  • the third obtaining unit 40 is further configured to:
  • the formula for calculating variance includes:
  • E(x j ) is the mean of the data carried by the data points
  • Var(x j ) is the variance of the data carried by the data points
  • x j is the transmitted symbol of the data points
  • Pr(b j,i ) is the corresponding x j
  • the formula for calculating the second covariance matrix includes:
  • R uu ' is the second interference autocorrelation matrix in the preset region
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the preset region
  • p k is the kth
  • the pilot symbols, y i and H i are respectively the received signal and the equivalent channel matrix corresponding to the i-th data point in the preset area
  • the device 00 further includes:
  • a first determining unit 60 configured to calculate a ratio of an average value of the diagonal element modes in the first interference autocorrelation matrix Ruu to an average value of the non-diagonal element modes
  • the data point that meets the preset condition in the preset area needs to be selected as the data point used in the step of calculating the second interference autocorrelation matrix Ruu ′;
  • the data points in the preset area are arbitrarily selected as the data points used in the step of calculating the second interference autocorrelation matrix Ruu '.
  • the ratio of the average value of the Ruu diagonal element mode to the average value of the non-diagonal element mode is calculated according to the first interference autocorrelation matrix Ruu , as follows:
  • c i and d i are the i-th diagonal and non-diagonal elements in R uu , respectively, and N d and N nd are the number of diagonal and non-diagonal elements, respectively.
  • Ratio Comparing Ratio with the first preset threshold Ts, if Ratio>Ts, it is necessary to select a data point that satisfies the preset condition in the preset area as the data point used in the third acquiring unit 40; if Ratio ⁇ Ts, arbitrarily select The data points in the preset area are used as data points used in the third acquisition unit 40.
  • the selecting unit 70 is configured to select a data point that meets the preset condition in the preset area as the data point used in the third acquiring unit 40.
  • the variance of the data carried by each data point in the obtained preset area is compared with a second preset threshold, where the variance of the data carried by each data point includes the variance of the preset layer data;
  • the variance of the data of each layer of the data point is less than or equal to the second preset threshold, it is determined that the data point satisfies the preset condition as the data point used by the third acquiring unit 40 to calculate the second interference autocorrelation matrix Ruu ′.
  • the second determining unit 80 is configured to compare the rank of the user equipment with the number of receiving antennas before acquiring the bit log likelihood ratio of the data points;
  • interference suppression combining is performed on the received signal y using Ruu .
  • the rank of the UE that is, the number of data layers. If the rank of the UE is smaller than the number of receiving antennas of the UE, the IRC algorithm in the prior art is used to estimate the interference signal covariance matrix in the preset region using the pilot point data in the preset region, and the interference is suppressed by using the obtained covariance matrix. The reason for this is that when the RI is greater than or equal to the number of receiving antennas, the gain of calculating the interference signal covariance matrix provided by the embodiment of the present invention is less than that obtained by the prior art. When the UE complexity is limited, the prior art scheme can be selected if the RI is greater than or equal to the number of receiving antennas.
  • This embodiment is used to implement the foregoing method embodiments, and the workflow of each unit in this embodiment.
  • the process and the working principle refer to the description in the foregoing method embodiments, and details are not described herein again.
  • the apparatus in the above embodiments may be integrated in a multi-antenna receiver based on all co-channel interference of various communication systems, and may be in a receiver of a base station or a receiver of a UE, and the present invention does not limit.
  • the apparatus for suppressing co-channel interference provided by the embodiment of the present invention firstly uses the first interference autocorrelation matrix Ruu of the pilot points in the preset region in the time-frequency domain to receive the signal y and the data points in the preset area.
  • the effect channel matrix H performs interference suppression combining to obtain the received signal after interference suppression combining And equivalent frequency domain channel matrix Then right with Performing multi-input and multi-output MIMO detection and demodulation to obtain a bit log likelihood ratio of data points; based on the mean algorithm, calculating a mean value of data carried by the data points according to the bit log likelihood ratio; based on the covariance matrix algorithm, according to the mean value,
  • the second interference autocorrelation matrix R uu ' in the preset region is calculated; finally, the interference suppression merging is performed on the received signal y by using Ruu '. In this way, the accuracy of calculating the interference autocorrelation matrix can be improved, and the interference can be effectively suppressed by using the matrix.
  • the embodiment of the present invention further provides an apparatus 90 for suppressing co-channel interference.
  • the apparatus 90 includes: a bus 94; and a processor 91, a memory 92 and an interface 93 connected to the bus 94, wherein the interface 93 is for communication; the memory 92 is for storing instructions, and the processor 91 is configured to execute the instructions for:
  • the preset area includes a preset number of resource blocks, where the resource block includes data points and pilot points;
  • Interference suppression combining is performed on the received signal y using Ruu '.
  • the processor 91 executes the instruction to obtain a bit log likelihood ratio of the data point, and may further include:
  • the new bit log likelihood ratio is taken as the bit log likelihood ratio of the data points.
  • the processor 91 is configured to calculate, according to the mean value algorithm, the average value of the data carried by the data point according to the bit log likelihood ratio, which may specifically include:
  • the mean calculation formula includes:
  • E(x j ) is the mean of the data carried by the data points
  • x j is the transmitted symbol of the data point
  • Pr(b j,i ) is the probability of i bits b j,i corresponding to x j , according to the bit pair
  • the number likelihood ratio is calculated as Pr(b j,i ).
  • the processor 91 is configured to calculate, according to the covariance matrix algorithm, the second interference autocorrelation matrix R uu ′ in the preset area according to the mean value, which may include:
  • the formula for calculating the first covariance matrix includes:
  • R uu ' is the second interference autocorrelation matrix in the preset region
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the preset region
  • p k is the kth
  • the pilot symbols, y i and H i are respectively the received signal and the equivalent channel matrix corresponding to the i-th data point in the preset area
  • the sum of the number of pilot points k and the number of data points i, the superscript H indicates the conjugate transpose of the matrix.
  • the processor 91 is configured to calculate, according to the covariance matrix algorithm, the second interference autocorrelation matrix R uu ′ in the preset area according to the mean value, and specifically, the method further includes:
  • the formula for calculating variance includes:
  • E(x j ) is the mean of the data carried by the data points
  • Var(x j ) is the variance of the data carried by the data points
  • x j is the transmitted symbol of the data points
  • Pr(b j,i ) is the corresponding x j
  • the formula for calculating the second covariance matrix includes:
  • R uu ' is the second interference autocorrelation matrix in the preset region
  • y k and H k are respectively the received signal and the equivalent channel matrix corresponding to the kth pilot point in the preset region
  • p k is the kth
  • the pilot symbols, y i and H i are respectively the received signal and the equivalent channel matrix corresponding to the i-th data point in the preset area
  • the processor 91 executes the instruction and is further used to:
  • the data point that meets the preset condition in the preset area needs to be selected as the data point used in the step of calculating the second interference autocorrelation matrix Ruu ′;
  • the data points in the preset area are arbitrarily selected as the data points used in the step of calculating the second interference autocorrelation matrix Ruu '.
  • the processor 91 is configured to: select a data point that meets a preset condition in the preset area as a data point used in the step of calculating the second interference autocorrelation matrix Ruu ′, and specifically includes:
  • the variance of the data of each layer of the data point is less than or equal to the second preset threshold, it is determined that the data point satisfies the preset condition as the data point used in the step of calculating the second interference autocorrelation matrix Ruu '.
  • the processor 91 executes the instruction and is further used to:
  • interference suppression combining is performed on the received signal y using Ruu .
  • the apparatus for suppressing co-channel interference provided by the embodiment of the present invention firstly uses the first interference autocorrelation matrix Ruu of the pilot points in the preset region in the time-frequency domain to receive the signal y and the data points in the preset area.
  • the effect channel matrix H performs interference suppression combining to obtain the received signal after interference suppression combining And equivalent frequency domain channel matrix Then right with Performing multi-input and multi-output MIMO detection and demodulation to obtain a bit log likelihood ratio of data points; based on the mean algorithm, calculating a mean value of data carried by the data points according to the bit log likelihood ratio; based on the covariance matrix algorithm, according to the mean value,
  • the second interference autocorrelation matrix R uu ' in the preset region is calculated; finally, the interference suppression merging is performed on the received signal y by using Ruu '. In this way, the accuracy of calculating the interference autocorrelation matrix can be improved, and the interference can be effectively suppressed by using the matrix.
  • the aforementioned program can be stored in a computer readable storage medium.
  • the program when executed, performs the steps including the foregoing method embodiments; and the foregoing storage medium includes various media that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disk.

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Abstract

本发明实施例提供一种抑制同频干扰的方法和装置，能够提高计算干扰自相关矩阵的准确度，并且利用该矩阵可以有效抑制干扰。具体方案为：首先利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号y͂和等效频域信道矩阵H͂；然后对y͂和H͂进行多入多出MIMO检测解调，获取数据点的比特对数似然比；基于均值算法，根据比特对数似然比计算数据点承载的数据的均值；基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'；最后利用Ruu'对接收信号y进行干扰抑制合并。本发明的实施例用于抑制同频干扰。

Description

抑制同频干扰的方法和装置
本申请要求于2014年6月4日提交中国专利局、申请号为201410245825.X、发明名称为“抑制同频干扰的方法和装置”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。
技术领域
本发明实施例涉及通信技术领域，尤其涉及一种抑制同频干扰的方法和装置。
背景技术
随着无线宽带通信技术的发展,用户对通信系统的性能提出更高要求，比如更高的峰值吞吐量和平均吞吐量，使得位于小区边缘的用户也能获得质量高的通信话音和数据业务服务。第3代合作伙伴计划(3rd Generation Partnership Project,3GPP)开始了通用移动通信系统(Universal Mobile Telecommunications System,UMTS)技术的长期演进(Long Term Evolution,LTE)系统。多输入多输出(Multiple-Input Multiple-Output，MIMO)和正交频分复用(Orthogonal Frequency Division Multiplex,OFDM)是LTE最关键的两个技术。
在LTE实际场景中，用户设备(User Equipment,UE)可能受到邻小区被调度UE相同时间、频率资源上的干扰，会严重降低UE解调数据信道的性能。基站高密度和异构是LTE网络结构的演进大方向，这显然会带来更加严重的小区间同频干扰，一般采用干扰抑制合并(Interference Rejection Combining,IRC)等算法来抑制干扰，IRC算法利用导频点或数据点的相关数据估计干扰信号的统计特性并基于该统计特性进行干扰白化以减少干扰信号对UE解调数据信道性能的影响。
IRC算法的关键在于准确估计干扰信号的统计特性也即准确计算干扰自相关矩阵，但是现有技术中，计算干扰自相关矩阵时由于要利用解调译 码后再进行反编码得到的数据，在中低信噪比(Signal-to-Noise Ratio,SNR)时反编码得到的数据准确度很差，这就使得计算出的干扰自相关矩阵会出现很大偏差，从而无法有效抑制干扰。
发明内容
本发明实施例提供一种抑制同频干扰的方法和装置，能够提高计算干扰自相关矩阵的准确度，并且利用该矩阵可以有效抑制干扰。
第一方面，本发明的实施例提供一种抑制同频干扰的方法，所述方法包括：
利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对所述预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000001
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000002
其中，所述预设区域包括预设个数的资源块，所述资源块中包含有所述数据点和所述导频点；
对所述
Figure PCTCN2015080498-appb-000003
和
Figure PCTCN2015080498-appb-000004
进行多入多出MIMO检测解调，获取所述数据点的比特对数似然比；
基于均值算法，根据所述比特对数似然比计算所述数据点承载的数据的均值；
基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'；
利用所述Ruu'对所述接收信号y进行干扰抑制合并。
结合第一方面，在第一种可能的实现方式中，所述获取所述数据点的比特对数似然比还包括：
对经过所述检测解调后获取的所述数据点的比特对数似然比进行译码处理、软值速率匹配处理、编码块级联处理以及软值加扰处理后获取新的比特对数似然比；
将所述新的比特对数似然比作为所述数据点的比特对数似然比。
结合第一方面或第一方面的第一种可能的实现方式，在第二种可能的实现方式中，所述基于均值算法，根据所述比特对数似然比计算所述数据 点承载的数据的均值包括：
根据均值计算公式计算所述数据点承载的数据的均值；
所述均值计算公式包括：
Figure PCTCN2015080498-appb-000005
其中，E(xj)为所述数据点承载的数据的均值，xj为所述数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)。
结合第一方面或第一方面的第一种或第二种可能的实现方式，在第三种可能的实现方式中，所述基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'包括：
将所述均值作为第一协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
所述第一协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000006
其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值为：E(xi)＝(E(xi，1),…,E(xi,M))T，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，上标H表示对矩阵共轭转置。
结合第一方面或第一方面的第一种或第二种可能的实现方式，在第四种可能的实现方式中，所述基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'还包括：
根据所述均值以及方差计算公式计算所述数据点承载的数据的方差；
所述方差计算公式包括：
Figure PCTCN2015080498-appb-000007
其中E(xj)为所述数据点承载的数据的均值，Var(xj)为所述数据点承载的数据的方差，xj为所述数据点的发射 符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)；
将所述均值与方差作为第二协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
所述第二协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000008
其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值和方差分别为：E(xi)＝(E(xi，1),…,E(xi,M))T和Vi＝diag{Var(xi,1),…,Var(xi,M)}，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，
Figure PCTCN2015080498-appb-000009
用于补偿误差，上标H表示对矩阵共轭转置。
结合第一方面，在第五种可能的实现方式中，在所述获取所述数据点的比特对数似然比之后还包括：
计算所述第一干扰自相关矩阵Ruu中对角线元素模的平均值与非对角线元素模的平均值的比值；
当所述比值大于第一预设阈值，则需要挑选所述预设区域内满足预设条件的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点；
当所述比值小于或等于所述第一预设阈值，则任意选取所述预设区域内的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
结合第一方面的第五种可能的实现方式，在第六种可能的实现方式中，所述挑选所述预设区域内满足预设条件的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点包括：
将获取的所述预设区域内的每个数据点承载的数据的方差与第二预设阈值比较，其中，所述每个数据点承载的数据的方差包括预设层数数据的方差；
若所述数据点任一层数据的方差大于所述第二预设阈值，则删除所述数据点；
若所述数据点每层数据的方差小于或等于所述第二预设阈值，则确定所述数据点满足预设条件作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
结合第一方面，在第七种可能的实现方式中，在所述获取所述数据点的比特对数似然比之前还包括：
将用户设备的秩与接收天线数进行比较；
若所述秩小于所述接收天线数，则执行所述获取所述数据点的比特对数似然比；
若所述秩大于或等于所述接收天线数，则利用所述Ruu对所述接收信号y进行干扰抑制合并。
第二方面，本发明的实施例提供一种抑制同频干扰的装置，所述装置包括：
第一抑制单元，用于利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对所述预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000010
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000011
其中，所述预设区域包括预设个数的资源块，所述资源块中包含有所述数据点和所述导频点；
第一获取单元，用于对所述
Figure PCTCN2015080498-appb-000012
和
Figure PCTCN2015080498-appb-000013
进行多入多出MIMO检测解调，获取所述数据点的比特对数似然比；
第二获取单元，用于基于均值算法，根据所述比特对数似然比计算所述数据点承载的数据的均值；
第三获取单元，用于基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'；
第二抑制单元，用于利用所述Ruu'对所述接收信号y进行干扰抑制合并。
结合第二方面，在第一种可能的实现方式中，所述第一获取单元还用于：
对经过所述检测解调后获取的所述数据点的比特对数似然比进行译码处理、软值速率匹配处理、编码块级联处理以及软值加扰处理后获取新的比特对数似然比；
将所述新的比特对数似然比作为所述数据点的比特对数似然比。
结合第二方面或第二方面的第一种可能的实现方式，在第二种可能的实现方式中，所述第二获取单元具体用于：
根据均值计算公式计算所述数据点承载的数据的均值；
所述均值计算公式包括：
Figure PCTCN2015080498-appb-000014
其中，E(xj)为所述数据点承载的数据的均值，xj为所述数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)。
结合第二方面或第二方面的第一种或第二种可能的实现方式，在第三种可能的实现方式中，所述第三获取单元具体用于：
将所述均值作为第一协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
所述第一协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000015
其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值为：E(xi)＝(E(xi，1),…,E(xi,M))T，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，上标H表示对矩阵共轭转置。
结合第二方面或第二方面的第一种或第二种可能的实现方式，在第四种可能的实现方式中，所述第三获取单元还具体用于：
根据所述均值以及方差计算公式计算所述数据点承载的数据的方差；
所述方差计算公式包括：
Figure PCTCN2015080498-appb-000016
其中E(xj)为所述数据点承载的数据的均值，Var(xj)为所述数据点承载的数据的方差，xj为所述数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)；
将所述均值与方差作为第二协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
所述第二协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000017
其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值和方差分别为：E(xi)＝(E(xi，1),…,E(xi,M))T和Vi＝diag{Var(xi,1),…,Var(xi,M)}，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，
Figure PCTCN2015080498-appb-000018
用于补偿误差，上标H表示对矩阵共轭转置。
结合第二方面的第四种可能的实现方式，在第五种可能的实现方式中，所述装置还包括：
第一判断单元，用于计算所述第一干扰自相关矩阵Ruu中对角线元素模的平均值与非对角线元素模的平均值的比值；
当所述比值大于第一预设阈值，则需要挑选所述预设区域内满足预设条件的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点；
当所述比值小于或等于所述第一预设阈值，则任意选取所述预设区域 内的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
结合第二方面的第五种可能的实现方式，在第六种可能的实现方式中，所述装置还包括：
挑选单元，用于挑选所述预设区域内满足预设条件的数据点作为所述第三获取单元使用的数据点，所述挑选所述预设区域内满足预设条件的数据点作为所述第三获取单元使用的数据点包括：
将获取的所述预设区域内的每个数据点承载的数据的方差与第二预设阈值比较，其中，所述每个数据点承载的数据的方差包括预设层数数据的方差；
若所述数据点任一层数据的方差大于所述第二预设阈值，则删除所述数据点；
若所述数据点每层数据的方差小于或等于所述第二预设阈值，则确定所述数据点满足预设条件作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
结合第二方面，在第七种可能的实现方式中，所述装置还包括：
第二判断单元，用于在获取所述数据点的比特对数似然比之前，将用户设备的秩与接收天线数进行比较；
若所述秩小于所述接收天线数，则执行所述获取所述数据点的比特对数似然比；
若所述秩大于或等于所述接收天线数，则利用所述Ruu对所述接收信号y进行干扰抑制合并。
本发明实施例提供的抑制同频干扰的方法和装置，首先利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000019
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000020
然后对
Figure PCTCN2015080498-appb-000021
和
Figure PCTCN2015080498-appb-000022
进行多入多出MIMO检测解调，获取数据点的比特对数似然比；基于均值算法，根据比特对数似然比计算数据点承载的数据的均值；基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'；最后利用Ruu'对接收信号y进行干扰抑制合并。这样，能够提高计算干扰自相关矩阵的准确度，并且利用该矩 阵可以有效抑制干扰。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本发明的实施例提供的抑制同频干扰的方法的流程示意图一；
图2为本发明的实施例提供的抑制同频干扰的方法的流程示意图二；
图3为本发明的实施例中获取预设区域内的数据点的比特对数似然比的流程示意图；
图4为本发明的实施例中获取预设区域内的数据点的比特对数似然比的效果示意图；
图5为本发明的实施例中获取预设区域内的第二干扰自相关矩阵Ruu'的效果示意图；
图6为本发明的实施例提供的抑制同频干扰的装置的结构示意图一；
图7为本发明的实施例提供的抑制同频干扰的装置的结构示意图二；
图8为本发明的实施例提供的抑制同频干扰的装置的结构示意图三。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明实施例提供一种抑制同频干扰的方法，如图1所示，该方法包括：
步骤101、利用时频域上预设区域内的导频点的第一干扰自相关矩阵 Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000023
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000024
其中，预设区域包括预设个数的资源块，资源块中包含有数据点和导频点。
步骤102、对
Figure PCTCN2015080498-appb-000025
和
Figure PCTCN2015080498-appb-000026
进行多入多出MIMO检测解调，获取数据点的比特对数似然比。
步骤103、基于均值算法，根据比特对数似然比计算数据点承载的数据的均值。
步骤104、基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'。
步骤105、利用Ruu'对接收信号y进行干扰抑制合并。
其中，利用干扰自相关矩阵对数据点的接收信号、等效信道矩阵进行干扰抑制合并可以是对数据点的接收信号、等效信道矩阵进行干扰白化，干扰白化只是干扰抑制合并的其中一种实现方式，本发明实施例对干扰抑制合并的具体实现方式不做限定，本领域技术人员可以根据实际选择具体的实现方式。
本发明实施例提供的抑制同频干扰的方法，首先利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000027
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000028
然后对
Figure PCTCN2015080498-appb-000029
和
Figure PCTCN2015080498-appb-000030
进行多入多出MIMO检测解调，获取数据点的比特对数似然比；基于均值算法，根据比特对数似然比计算数据点承载的数据的均值；基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'；最后利用Ruu'对所述接收信号y进行干扰抑制合并。这样，能够提高计算干扰自相关矩阵的准确度，并且利用该矩阵可以有效抑制干扰。
为了使本领域技术人员能够更清楚地理解本发明实施例提供的技术方案，下面通过具体的实施例，对本发明的实施例提供的抑制同频干扰的方法进行详细说明，如图2所示，该方法包括：
步骤201、将用户设备的秩与接收天线数进行比较。
若用户设备的秩大于或等于接收天线数，则执行步骤202；
若用户设备的秩小于接收到天线数，则执行步骤203及后续步骤。
示例性的，UE的秩(Rank Index，RI)，即数据层数。若UE的秩小于UE的接收天线数，则执行步骤202，步骤202实际上就是现有技术中IRC算法使用预设区域内导频点数据估算预设区域内干扰自相关矩阵并用得到的干扰自相关矩阵抑制干扰。当RI大于或等于接收天线数时，利用本发明实施例提供的计算干扰自相关矩阵相比现有技术所获得的准确度的增益是比较有限的。在UE复杂度受限的时候，可以在RI大于或等于接收天线数的情况下选择使用现有技术的方案。
步骤202、利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y进行干扰抑制合并。
其中，预设区域包括预设个数的资源块(Resource Block，RB)，该资源块RB在时域上包含连续的若干个OFDM符号，在频域上包含连续的若干个子载波，每个RB中包括若干个资源单元(Resource Element，RE)，RE是导频点或数据点，RB的个数以及其中导频点和数据点的个数根据实际需要选取，本发明对此不作限定。
具体的，步骤202实际上就是现有技术中抑制同频干扰的方案，下面进行示例性的说明：
给定一个LTE系统，假设UE侧接收天线个数为N，用x、y和H分别表示某个RE的发射符号向量、接收向量和等效频域信道矩阵，那么信道模型可以表示为：y＝Hx+u，其中u是N维向量，表示UE受到的同频干扰和加性高斯白噪声。
计算预设区域的第一干扰自相关矩阵Ruu的公式如下：
Figure PCTCN2015080498-appb-000031
其中，yk和Hk分别为该区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，Lp是导频点个数。需要说明的是，上述公式中，计算该预设区域内所有导频点的干扰自相关矩阵，然后取平均值作为该区域内导频点的第一干扰自相关矩阵。
对上述Ruu进行Cholesky分解，即Ruu＝LLH，其中L是下三角阵。
然后利用L-1对数据点的接收信号y进行干扰抑制合并，例如，可以对接收信号y(叠加有干扰信号)滤波也即对y中的干扰信号进行干扰白化，得到：
Figure PCTCN2015080498-appb-000032
即接收信号y中干扰信号的相关性被去除，其中
Figure PCTCN2015080498-appb-000033
为干扰白化后的接收信号，上述干扰白化只是干扰抑制合并的其中一种实现方式，本发明实施例对干扰抑制合并的具体实现方式不做限定，本领域技术人员可以根据实际选择具体的实现方式。
步骤203、获取数据点的比特对数似然比。
具体的，如图3所示，获取数据点的比特对数似然比的步骤包括：
步骤203a、获取预设区域内的导频点的第一干扰自相关矩阵Ruu。
示例性的，按照步骤202中给出的方法计算预设区域内的导频点的第一干扰自相关矩阵Ruu。
步骤203b、利用Ruu对数据点的接收信号y和等效信道矩阵H进行干扰抑制合并得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000034
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000035
示例性的，按照步骤202给出的方法对预设区域内数据点的接收信号y和等效信道矩阵H做干扰白化。
对Ruu进行Cholesky分解，即Ruu＝LLH，利用L-1对数据点的接收信号y干扰白化，得到
Figure PCTCN2015080498-appb-000036
Figure PCTCN2015080498-appb-000037
为干扰白化后的接收信号；利用L-1对数据点的等效信道矩阵H干扰白化，得到
Figure PCTCN2015080498-appb-000038
Figure PCTCN2015080498-appb-000039
为干扰白化后的等效信道矩阵。
步骤203c、对
Figure PCTCN2015080498-appb-000040
和进行多入多出MIMO检测解调，获取数据点的比特对数似然比。
示例性的，根据步骤203b得到干扰白化后的数据点的接收信号
Figure PCTCN2015080498-appb-000042
和等效信道矩阵
Figure PCTCN2015080498-appb-000043
然后经过MIMO检测解调后得到比特对数似然比(Byte Log Likelihood Ratio，BLLR)。
其中，MIMO检测解调获取比特对数似然比可以使用多种方法，以下为一种实现算法：
例如，基于最大似然准则，xj的第i个比特bj,i的比特对数似然比γj,i为：
Figure PCTCN2015080498-appb-000044
其中，Pr(bj,i)为对应xj的第i个比特bj,i的概率，xj为数据点的发射符号，
Figure PCTCN2015080498-appb-000045
为干扰抑制合并后的接收信号，
Figure PCTCN2015080498-appb-000046
为干扰抑制合并后的等效频域 信道矩阵。
经过步骤203a、203b以及步骤203c已经得到比特对数似然比，为了获得更准确的比特对数似然比还可以对步骤203c得到的比特对数似然比进行进一步处理。
步骤203d、对经过检测解调后获取的数据点的比特对数似然比进行译码处理、软值速率匹配处理、编码块级联处理以及软值加扰处理后获取新的比特对数似然比。
示例性的，利用检测解调后得到的比特对数似然比，经过软值速率匹配、软值编码块(Coded Block，CB)级联和软值加扰(和传统编码中的相关操作基本相同，区别仅在输入从0/1比特变成了比特对数似然比)操作后得到长度/顺序和解调后得到的比特对数似然比完全匹配的新的比特对数似然比。
步骤203e、将新的比特对数似然比作为数据点的比特对数似然比。
具体的，将步骤203d获取的新的比特对数似然比作为步骤203要获取的数据点的比特对数似然比。
示意性的，图4所示为上述两种获取比特对数似然比的方式。
需要说明的是，经过步骤203a、步骤203b以及步骤203c后得到的比特对数似然比与经过步骤203a至步骤203e后得到的比特对数似然比相比，采用后一种方式得到的比特对数似然比准确度更高些。
步骤204、计算导频点的第一干扰自相关矩阵Ruu中对角线元素模的平均值与非对角线元素模的平均值的比值。
示例性的，对于根据步骤203a已经得到的矩阵Ruu，计算Ruu对角线元素模的平均值与非对角线元素模的平均值的比值的公式如下：
Figure PCTCN2015080498-appb-000047
其中，ci和di分别是Ruu中第i个对角线和非对角线元素，Nd和Nnd分别是对角线和非对角线元素的个数。
将Ratio和第一预设阈值Ts比较，若Ratio＞Ts，则需要挑选预设区域内满足预设条件的数据点作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点，执行步骤205、步骤206、步骤207或步骤208、步骤209，其 中步骤206为挑选数据点的步骤；若Ratio≤Ts，则任意选取预设区域内的数据点数据点作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点，执行步骤205、步骤207或步骤208、步骤209。
步骤205、基于均值算法，根据比特对数似然比计算数据点承载的数据的均值以及根据该均值计算数据点承载的数据的方差。
示例性的，根据比特对数似然比以及均值计算公式和方差计算公式计算数据点承载的数据的均值与方差；
均值计算公式包括：
Figure PCTCN2015080498-appb-000048
方差计算公式包括：
Figure PCTCN2015080498-appb-000049
其中，E(xj)为数据点承载的数据的均值，Var(xj)为数据点承载的数据的方差，xj为数据点的发射符号，Pr(bj,i)为对应xj的第i个比特bj,i的概率，根据比特对数似然比计算Pr(bj,i)。
其中，计算Pr(bj,i)的公式为：
Figure PCTCN2015080498-appb-000050
和
Figure PCTCN2015080498-appb-000051
γj,i是比特bj,i的比特对数似然比。
步骤206、挑选预设区域内满足预设条件的数据点作为后续计算预设区域内第二干扰自相关矩阵Ruu'时使用的数据点。
具体的，将获取的预设区域的每个数据点承载的数据的方差与第二预设阈值比较，其中，每个数据点承载的数据的方差包括预设层数数据的方差，预设层数即UE的数据层数；若该数据点任一层数据的方差大于第二预设阈值，则确定该数据点不满足预设条件；若该数据点每层数据的方差小于或等于第二预设阈值，则确定该数据点满足预设条件；若该数据点满足预设条件则作为后续步骤207或步骤208计算预设区域内第二干扰自相关矩阵Ruu'时使用的数据点；若该数据点不满足预设条件则在后续步骤207或步骤208计算预设区域内第二干扰自相关矩阵Ruu'时不使用该数据点。
示例性的，将根据步骤205计算得到的数据点的每层数据的方差Var(xj)与第二预设阈值Tvar比较，若任一层数据的方差Var(xj)＞Tvar，则该数据点不满足预设条件，若每层数据的方差Var(xj)≤Tvar，则认为该数据点 满足预设条件。
步骤207、将均值作为第一协方差矩阵计算公式的输入计算预设区域内的第二干扰自相关矩阵Ruu'。
具体的，其中，第一协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000052
其中，Ruu'为预设区域内的干扰自相关矩阵，yk和Hk分别为预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值为：E(xi)＝(E(xi，1),…,E(xi,M))T，L为参与求和的预设区域内的导频点个数k与数据点个数i的和，上标H表示对矩阵共轭转置。
步骤208、将均值与方差作为第二协方差矩阵计算公式的输入计算预设区域内的第二干扰自相关矩阵Ruu'。
具体的，其中，第二协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000053
其中，Ruu'为预设区域内的干扰自相关矩阵，yk和Hk分别为预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值和方差分别为：E(xi)＝(E(xi，1),…,E(xi,M))T和Vi＝diag{Var(xi,1),…,Var(xi,M)}，L为参与求和的预设区域内的导频点个数k与数据点个数i的和，
Figure PCTCN2015080498-appb-000054
用于补偿误差，上标H表示对矩阵共轭转置。
示意性的，图5所示为上述获取预设区域内的第二干扰自相关矩阵Ruu'的效果示意图。
需要说明的是，现有技术中当译码质量很差，计算干扰自相关矩阵的时候，反编码错误的符号与真实传输符号的误差一般较大，而且计算干扰自相关矩阵未考虑误差的存在。在本发明的实施例中，用均值替代真实传输信号，因为是平均值，误差会小很多；另外，在计算干扰自相关矩阵时还可以通过引入方差进一步减小误差。这样，通过本发明实施例提供的方法计算的干扰自相关矩阵相对现有技术计算的干扰自相关矩阵更加准确。
步骤209、利用Ruu'对接收信号y进行干扰抑制合并。
其中，对接收信号y进行干扰抑制合并可以是对接收信号y进行干扰白化，步骤209可以采用本领域通用的利用干扰自相关矩阵进行干扰抑制合并，干扰白化只是给出的其中一种实现方式，本发明对干扰抑制合并的具体实现方式不做限定。
示例性的，对根据步骤207或步骤208得到的Ruu'进行Cholesky分解，即Ruu'＝LLH，其中L是下三角阵。
然后可以利用L-1对预设区域内的干扰信号进行干扰抑制合并，例如，可以对导频点的接收信号y(叠加有干扰信号)滤波也即对y中的干扰信号进行干扰白化，得到：
Figure PCTCN2015080498-appb-000055
即接收信号y中干扰信号的相关性被去除，其中
Figure PCTCN2015080498-appb-000056
为干扰白化后的接收信号。
需要说明的是，本发明的技术方案，可以应用于各种通信系统，例如：全球移动通信系统(Global System for Mobile Communications，GSM)、码分多址(Code Division Multiple Access，CDMA)系统、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)系统、时分同步码分多址(Time Division-Synchronous Code Division Multiple Access，TD-SCDMA)系统、LTE系统等。本发明对此不做限定。
本发明实施例提供的抑制同频干扰的方法，首先利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000057
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000058
然后对
Figure PCTCN2015080498-appb-000059
和
Figure PCTCN2015080498-appb-000060
进行多入多出MIMO检测解调，获取数据点的比特对数似然比；基于均值算法，根据比特对数似然比计算数据点承载的数据的均值；基于协方差矩阵算法，根据均值，计算预设区域 内的第二干扰自相关矩阵Ruu'；最后利用Ruu'对接收信号y进行干扰抑制合并。这样，能够提高计算干扰自相关矩阵的准确度，并且利用该矩阵可以有效抑制干扰。
本发明的实施例提供一种抑制同频干扰的装置00，如图6所示，该装置00包括：第一抑制单元10、第一获取单元20、第二获取单元30、第三获取单元40以及第二抑制单元50。
第一抑制单元10，用于利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000061
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000062
其中，预设区域包括预设个数的资源块，该资源块中包含有数据点和导频点。
第一获取单元20，用于对
Figure PCTCN2015080498-appb-000063
和
Figure PCTCN2015080498-appb-000064
进行多入多出MIMO检测解调，获取数据点的比特对数似然比。
示例性的，预设区域包括预设个数的资源块，该资源块RB在(时域上包含连续的若干个OFDM符号，在频域上包含连续的若干个子载波)，每个RB中包括若干个资源单元(Resource Element，RE)，RE是导频点或数据点，RB的个数以及其中导频点和数据点的个数根据实际需要选取，本发明对此不作限定。
给定一个LTE系统，假设UE侧接收天线个数为N，用x、y和H分别表示某个RE的发射符号向量、接收向量和等效频域信道矩阵，那么信道模型可以表示为：y＝Hx+u，其中u是N维向量，表示UE受到的同频干扰和加性高斯白噪声。
计算预设区域的导频点的第一干扰自相关矩阵Ruu的公式如下：
Figure PCTCN2015080498-appb-000065
其中，yk和Hk分别为该区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，Lp是导频点个数。需要说明的是，上述公式中，计算该预设区域内所有导频点的干扰自相关矩阵，然后取平均值作为该区域内导频点的第一干扰自相关矩阵。
对上述Ruu进行Cholesky分解，即Ruu＝LLH，利用L-1对数据点的接收信号y干扰白化，得到
Figure PCTCN2015080498-appb-000066
Figure PCTCN2015080498-appb-000067
为干扰白化后的接收信号；利用L-1对数据点的等效信道矩阵H干扰白化，得到
Figure PCTCN2015080498-appb-000068
Figure PCTCN2015080498-appb-000069
为干扰白化后的等效信道矩阵。
根据上述得到的干扰白化后的数据点的接收信号
Figure PCTCN2015080498-appb-000070
和等效信道矩阵
Figure PCTCN2015080498-appb-000071
然后经过MIMO检测解调后得到比特对数似然比。
其中，MIMO检测解调获取比特对数似然比可以使用多种方法，以下为一种实现算法：
例如，基于最大似然准则，xj的第i个比特bj,i的比特对数似然比γj,i为：
Figure PCTCN2015080498-appb-000072
其中，Pr(bj,i)为对应xj的第i个比特bj,i的概率，xj为数据点的发射符号，
Figure PCTCN2015080498-appb-000073
为干扰抑制合并后的接收信号，
Figure PCTCN2015080498-appb-000074
为干扰抑制合并后的等效频域信道矩阵。
第二获取单元30，用于基于均值算法，根据比特对数似然比计算数据点承载的数据的均值。
第三获取单元40，用于基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'。
第二抑制单元50，用于利用Ruu'对接收信号y进行干扰抑制合并。
可选的，第一获取单元20还可以具体用于：
对经过检测解调后获取的数据点的比特对数似然比进行译码处理、软值速率匹配处理、软值编码块级联处理以及软值加扰处理后获取新的比特对数似然比；
将新的比特对数似然比作为数据点的比特对数似然比。
示例性的，利用检测解调后得到的比特对数似然比，经过软值速率匹配、软值编码块级联和软值加扰(和传统编码中的相关操作基本相同，区别仅在输入从0/1比特变成了比特对数似然比)操作后得到长度/顺序和解调后得到的比特对数似然比完全匹配的新的比特对数似然比，将新的比特对数似然比作为第一获取单元20要获取的预设区域内数据点的比特对数似 然比。
可选的，第二获取单元30可以具体用于：
根据均值计算公式计算数据点承载的数据的均值；
均值计算公式包括：
Figure PCTCN2015080498-appb-000075
其中，E(xj)为数据点承载的数据的均值，xj为数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据比特对数似然比计算Pr(bj,i)。
示例性的，其中，计算Pr(bj,i)的公式为：
Figure PCTCN2015080498-appb-000076
和
Figure PCTCN2015080498-appb-000077
γj,i是比特bj,i的比特对数似然比。
可选的，第三获取单元40可以具体用于：
将均值作为第一协方差矩阵计算公式的输入计算预设区域内的第二干扰自相关矩阵Ruu'；
第一协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000078
其中，Ruu'为预设区域内的第二干扰自相关矩阵，yk和Hk分别为预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值为：E(xi)＝(E(xi，1),…,E(xi,M))T，L为参与求和的预设区域内的导频点个数k与数据点个数i的和，上标H表示对矩阵共轭转置。
可选的，第三获取单元40还可以具体用于：
根据均值以及方差计算公式计算数据点承载的数据的方差；
方差计算公式包括：
Figure PCTCN2015080498-appb-000079
其中E(xj)为数据点承载的数据的均值，Var(xj)为数据点承载的数据的方差，xj为数据点的发射符号，Pr(bj,i) 为对应xj的i个比特bj,i的概率，根据比特对数似然比计算Pr(bj,i)；
将均值与方差作为第二协方差矩阵计算公式的输入计算预设区域内的第二干扰自相关矩阵Ruu'；
第二协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000080
其中，Ruu'为预设区域内的第二干扰自相关矩阵，yk和Hk分别为预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值和方差分别为：E(xi)＝(E(xi，1),…,E(xi,M))T和Vi＝diag{Var(xi,1),…,Var(xi,M)}，L为参与求和的预设区域内的导频点个数k与数据点个数i的和，
Figure PCTCN2015080498-appb-000081
用于补偿误差，上标H表示对矩阵共轭转置。
可选的，如图7所示，该装置00还包括：
第一判断单元60，用于计算第一干扰自相关矩阵Ruu中对角线元素模的平均值与非对角线元素模的平均值的比值；
当比值大于第一预设阈值，则需要挑选预设区域内满足预设条件的数据点作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点；
当比值小于或等于第一预设阈值，则任意选取预设区域内的数据点作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点。
示例性的，根据第一干扰自相关矩阵Ruu计算Ruu对角线元素模的平均值与非对角线元素模的平均值的比值，公式如下：
Figure PCTCN2015080498-appb-000082
其中，ci和di分别是Ruu中第i个对角线和非对角线元素，Nd和Nnd分别是对角线和非对角线元素的个数。
将Ratio和第一预设阈值Ts比较，若Ratio＞Ts，则需要挑选预设区域内满足预设条件的数据点作为第三获取单元40中使用的数据点；若Ratio≤Ts，则任意选取预设区域内的数据点作为第三获取单元40中使用的数据点。
挑选单元70，用于挑选预设区域内满足预设条件的数据点作为第三获取单元40中使用的数据点。
具体的，将获取的预设区域内的每个数据点承载的数据的方差与第二预设阈值比较，其中，每个数据点承载的数据的方差包括预设层数数据的方差；
若该数据点任一层数据的方差大于第二预设阈值，则确定该数据点不满足预设条件剔除该数据点；
若该数据点每层数据的方差小于或等于第二预设阈值，则确定该数据点满足预设条件作为第三获取单元40计算第二干扰自相关矩阵Ruu'时使用的数据点。
示例性的，将计算得到的数据点的每层数据的方差Var(xj)与第二预设阈值Tvar比较，若任一层数据的方差Var(xj)＞Tvar，则该数据点不满足预设条件，若每层数据的方差Var(xj)≤Tvar，则认为该数据点满足预设条件。
第二判断单元80，用于在获取数据点的比特对数似然比之前，将用户设备的秩与接收天线数进行比较；
若秩小于接收天线数，则执行获取数据点的比特对数似然比；
若秩大于或等于接收天线数，则利用Ruu对接收信号y进行干扰抑制合并。
示例性的，UE的秩，即数据层数。若UE的秩小于UE的接收天线数，则利用现有技术中IRC算法使用预设区域内导频点数据估算预设区域内干扰信号协方差矩阵并用得到的协方差矩阵抑制干扰。这么做的原因是：当RI大于或等于接收天线数时，利用本发明实施例提供的计算干扰信号协方差矩阵相比现有技术所获得的准确度的增益是比较有限的。在UE复杂度受限的时候，可以在RI大于或等于接收天线数的情况下选择使用现有技术的方案。
本实施例用于实现上述各方法实施例，本实施例中各个单元的工作流 程和工作原理参见上述各方法实施例中的描述，在此不再赘述。
另外，上述实施例中的装置可以集成在基于各种通信系统的所有同频干扰下的多天线接收机中，可以在基站的接收机中也可以在UE的接收机中，本发明对此不作限制。
本发明实施例提供的抑制同频干扰的装置，首先利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000083
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000084
然后对
Figure PCTCN2015080498-appb-000085
和
Figure PCTCN2015080498-appb-000086
进行多入多出MIMO检测解调，获取数据点的比特对数似然比；基于均值算法，根据比特对数似然比计算数据点承载的数据的均值；基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'；最后利用Ruu'对接收信号y进行干扰抑制合并。这样，能够提高计算干扰自相关矩阵的准确度，并且利用该矩阵可以有效抑制干扰。
本发明实施例还提供了一种抑制同频干扰的装置90，如图8所示，该装置90包括：总线94；以及连接到总线94的处理器91、存储器92和接口93，其中该接口93用于通信；该存储器92用于存储指令，处理器91用于执行该指令用于：
利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000087
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000088
其中，预设区域包括预设个数的资源块，资源块中包含有数据点和导频点；
对
Figure PCTCN2015080498-appb-000089
和
Figure PCTCN2015080498-appb-000090
进行多入多出MIMO检测解调，获取数据点的比特对数似然比；
基于均值算法，根据比特对数似然比计算数据点承载的数据的均值；
基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'；
利用Ruu'对接收信号y进行干扰抑制合并。
可选地，处理器91执行该指令用于获取数据点的比特对数似然比，还可以包括：
对经过检测解调后获取的数据点的比特对数似然比进行译码处理、软值速率匹配处理、编码块级联处理以及软值加扰处理后获取新的比特对数 似然比；
将新的比特对数似然比作为数据点的比特对数似然比。
可选的，处理器91执行该指令用于基于均值算法，根据比特对数似然比计算数据点承载的数据的均值，具体可以包括：
根据均值计算公式计算数据点承载的数据的均值；
均值计算公式包括：
Figure PCTCN2015080498-appb-000091
其中，E(xj)为数据点承载的数据的均值，xj为数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据比特对数似然比计算Pr(bj,i)。
可选的，处理器91执行该指令用于基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'，具体可以包括：
将均值作为第一协方差矩阵计算公式的输入计算预设区域内的第二干扰自相关矩阵Ruu'；
第一协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000092
其中，Ruu'为预设区域内的第二干扰自相关矩阵，yk和Hk分别为预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值为：E(xi)＝(E(xi，1),…,E(xi,M))T，L为参与求和的预设区域内的导频点个数k与数据点个数i的和，上标H表示对矩阵共轭转置。
可选的，处理器91执行该指令用于基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'，具体还可以包括：
根据均值以及方差计算公式计算数据点承载的数据的方差；
方差计算公式包括：
Figure PCTCN2015080498-appb-000093
其中E(xj)为数据点承载的数据的均值，Var(xj)为数据点承载的数据的方差，xj为数据点的发射符号，Pr(bj,i) 为对应xj的i个比特bj,i的概率，根据比特对数似然比计算Pr(bj,i)；
将均值与方差作为第二协方差矩阵计算公式的输入计算预设区域内的第二干扰自相关矩阵Ruu'；
第二协方差矩阵计算公式包括：
Figure PCTCN2015080498-appb-000094
其中，Ruu'为预设区域内的第二干扰自相关矩阵，yk和Hk分别为预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值和方差分别为：E(xi)＝(E(xi，1),…,E(xi,M))T和Vi＝diag{Var(xi,1),…,Var(xi,M)}，L为参与求和的预设区域内的导频点个数k与数据点个数i的和，
Figure PCTCN2015080498-appb-000095
用于补偿误差，上标H表示对矩阵共轭转置。
可选的，在获取数据点的比特对数似然比之后，处理器91执行该指令还用于：
计算第一干扰自相关矩阵Ruu中对角线元素模的平均值与非对角线元素模的平均值的比值；
当比值大于第一预设阈值，则需要挑选预设区域内满足预设条件的数据点作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点；
当比值小于或等于第一预设阈值，则任意选取预设区域内的数据点作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点。
可选的，处理器91执行该指令用于挑选预设区域内满足预设条件的数据点作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点，具体可以包括：
将获取的预设区域内的每个数据点承载的数据的方差与第二预设阈值比较，其中，每个数据点承载的数据的方差包括预设层数数据的方差；
若数据点任一层数据的方差大于第二预设阈值，则删除数据点；
若数据点每层数据的方差小于或等于第二预设阈值，则确定数据点满足预设条件作为计算第二干扰自相关矩阵Ruu'步骤中使用的数据点。
可选的，在获取数据点的比特对数似然比之前，处理器91执行该指令还用于：
将用户设备的秩与接收天线数进行比较；
若秩小于接收天线数，则执行获取数据点的比特对数似然比；
若秩大于或等于接收天线数，则利用Ruu对接收信号y进行干扰抑制合并。
本发明实施例提供的抑制同频干扰的装置，首先利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
Figure PCTCN2015080498-appb-000096
和等效频域信道矩阵
Figure PCTCN2015080498-appb-000097
然后对
Figure PCTCN2015080498-appb-000098
和
Figure PCTCN2015080498-appb-000099
进行多入多出MIMO检测解调，获取数据点的比特对数似然比；基于均值算法，根据比特对数似然比计算数据点承载的数据的均值；基于协方差矩阵算法，根据均值，计算预设区域内的第二干扰自相关矩阵Ruu'；最后利用Ruu'对接收信号y进行干扰抑制合并。这样，能够提高计算干扰自相关矩阵的准确度，并且利用该矩阵可以有效抑制干扰。
本领域普通技术人员可以理解：实现上述各方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成。前述的程序可以存储于一计算机可读取存储介质中。该程序在执行时，执行包括上述各方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (16)

  1. 一种抑制同频干扰的方法，其特征在于，包括：
    利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对所述预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
    Figure PCTCN2015080498-appb-100001
    和等效频域信道矩阵
    Figure PCTCN2015080498-appb-100002
    其中，所述预设区域包括预设个数的资源块，所述资源块中包含有所述数据点和所述导频点；
    对所述
    Figure PCTCN2015080498-appb-100003
    和
    Figure PCTCN2015080498-appb-100004
    进行多入多出MIMO检测解调，获取所述数据点的比特对数似然比；
    基于均值算法，根据所述比特对数似然比计算所述数据点承载的数据的均值；
    基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'；
    利用所述Ruu'对所述接收信号y进行干扰抑制合并。
  2. 根据权利要求1所述的方法，其特征在于，所述获取所述数据点的比特对数似然比还包括：
    对经过所述检测解调后获取的所述数据点的比特对数似然比进行译码处理、软值速率匹配处理、编码块级联处理以及软值加扰处理后获取新的比特对数似然比；
    将所述新的比特对数似然比作为所述数据点的比特对数似然比。
  3. 根据权利要求1或2所述的方法，其特征在于，所述基于均值算法，根据所述比特对数似然比计算所述数据点承载的数据的均值包括：
    根据均值计算公式计算所述数据点承载的数据的均值；
    所述均值计算公式包括：
    Figure PCTCN2015080498-appb-100005
    其中，E(xj)为所述数据点承载的数据的均值，xj为所述数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)。
  4. 根据权利要求1或2或3所述的方法，其特征在于，所述基于协方 差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'包括：
    将所述均值作为第一协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
    所述第一协方差矩阵计算公式包括：
    Figure PCTCN2015080498-appb-100006
    其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值为：E(xi)＝(E(xi，1),…,E(xi,M))T，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，上标H表示对矩阵共轭转置。
  5. 根据权利要求1或2或3所述的方法，其特征在于，所述基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'还包括：
    根据所述均值以及方差计算公式计算所述数据点承载的数据的方差；
    所述方差计算公式包括：
    Figure PCTCN2015080498-appb-100007
    其中E(xj)为所述数据点承载的数据的均值，Var(xj)为所述数据点承载的数据的方差，xj为所述数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)；
    将所述均值与方差作为第二协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
    所述第二协方差矩阵计算公式包括：
    Figure PCTCN2015080498-appb-100008
    其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所 述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值和方差分别为：E(xi)＝(E(xi，1),…,E(xi,M))T和Vi＝diag{Var(xi,1),…,Var(xi,M)}，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，
    Figure PCTCN2015080498-appb-100009
    用于补偿误差，上标H表示对矩阵共轭转置。
  6. 根据权利要求1所述的方法，其特征在于，在所述获取所述数据点的比特对数似然比之后还包括：
    计算所述第一干扰自相关矩阵Ruu中对角线元素模的平均值与非对角线元素模的平均值的比值；
    当所述比值大于第一预设阈值，则需要挑选所述预设区域内满足预设条件的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点；
    当所述比值小于或等于所述第一预设阈值，则任意选取所述预设区域内的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
  7. 根据权利要求6所述的方法，其特征在于，所述挑选所述预设区域内满足预设条件的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点包括：
    将获取的所述预设区域内的每个数据点承载的数据的方差与第二预设阈值比较，其中，所述每个数据点承载的数据的方差包括预设层数数据的方差；
    若所述数据点任一层数据的方差大于所述第二预设阈值，则删除所述数据点；
    若所述数据点每层数据的方差小于或等于所述第二预设阈值，则确定所述数据点满足预设条件作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
  8. 根据权利要求1所述的方法，其特征在于，在所述获取所述数据点 的比特对数似然比之前还包括：
    将用户设备的秩与接收天线数进行比较；
    若所述秩小于所述接收天线数，则执行所述获取所述数据点的比特对数似然比；
    若所述秩大于或等于所述接收天线数，则利用所述Ruu对所述接收信号y进行干扰抑制合并。
  9. 一种抑制同频干扰的装置，其特征在于，包括：
    第一抑制单元，用于利用时频域上预设区域内的导频点的第一干扰自相关矩阵Ruu对所述预设区域内的数据点的接收信号y和等效信道矩阵H进行干扰抑制合并，得到干扰抑制合并后的接收信号
    Figure PCTCN2015080498-appb-100010
    和等效频域信道矩阵
    Figure PCTCN2015080498-appb-100011
    其中，所述预设区域包括预设个数的资源块，所述资源块中包含有所述数据点和所述导频点；
    第一获取单元，用于对所述
    Figure PCTCN2015080498-appb-100012
    和
    Figure PCTCN2015080498-appb-100013
    进行多入多出MIMO检测解调，获取所述数据点的比特对数似然比；
    第二获取单元，用于基于均值算法，根据所述比特对数似然比计算所述数据点承载的数据的均值；
    第三获取单元，用于基于协方差矩阵算法，根据所述均值，计算所述预设区域内的第二干扰自相关矩阵Ruu'；
    第二抑制单元，用于利用所述Ruu'对所述接收信号y进行干扰抑制合并。
  10. 根据权利要求9所述的装置，其特征在于，所述第一获取单元还用于：
    对经过所述检测解调后获取的所述数据点的比特对数似然比进行译码处理、软值速率匹配处理、编码块级联处理以及软值加扰处理后获取新的比特对数似然比；
    将所述新的比特对数似然比作为所述数据点的比特对数似然比。
  11. 根据权利要求9或10所述的装置，其特征在于，所述第二获取单元具体用于：
    根据均值计算公式计算所述数据点承载的数据的均值；
    所述均值计算公式包括：
    Figure PCTCN2015080498-appb-100014
    其中，E(xj)为所述数据点承载的数据的均值，xj为所述数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)。
  12. 根据权利要求9或10或11所述的装置，其特征在于，所述第三获取单元具体用于：
    将所述均值作为第一协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
    所述第一协方差矩阵计算公式包括：
    Figure PCTCN2015080498-appb-100015
    其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值为：E(xi)＝(E(xi，1),…,E(xi,M))T，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，上标H表示对矩阵共轭转置。
  13. 根据权利要求9或10或11所述的装置，其特征在于，所述第三获取单元还具体用于：
    根据所述均值以及方差计算公式计算所述数据点承载的数据的方差；
    所述方差计算公式包括：
    Figure PCTCN2015080498-appb-100016
    其中E(xj)为所述数据点承载的数据的均值，Var(xj)为所述数据点承载的数据的方差，xj为所述数据点的发射符号，Pr(bj,i)为对应xj的i个比特bj,i的概率，根据所述比特对数似然比计算Pr(bj,i)；
    将所述均值与方差作为第二协方差矩阵计算公式的输入计算所述预设区域内的第二干扰自相关矩阵Ruu'；
    所述第二协方差矩阵计算公式包括：
    Figure PCTCN2015080498-appb-100017
    其中，Ruu'为所述预设区域内的第二干扰自相关矩阵，yk和Hk分别为所述预设区域内对应第k个导频点的接收信号和等效信道矩阵，pk为第k个导频符号，yi和Hi分别为所述预设区域内对应第i个数据点的接收信号和等效信道矩阵，xi为第i个数据点的发射符号，当xi为M维向量即xi为M层数据时，其均值和方差分别为：E(xi)＝(E(xi，1),…,E(xi,M))T和Vi＝diag{Var(xi,1),…,Var(xi,M)}，L为参与求和的所述预设区域内的导频点个数k与数据点个数i的和，
    Figure PCTCN2015080498-appb-100018
    用于补偿误差，上标H表示对矩阵共轭转置。
  14. 根据权利要求9所述的装置，其特征在于，所述装置还包括：
    第一判断单元，用于计算所述第一干扰自相关矩阵Ruu中对角线元素模的平均值与非对角线元素模的平均值的比值；
    当所述比值大于第一预设阈值，则需要挑选所述预设区域内满足预设条件的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点；
    当所述比值小于或等于所述第一预设阈值，则任意选取所述预设区域内的数据点作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
  15. 根据权利要求14所述的装置，其特征在于，所述装置还包括：
    挑选单元，用于挑选所述预设区域内满足预设条件的数据点作为所述第三获取单元使用的数据点，所述挑选所述预设区域内满足预设条件的数据点作为所述第三获取单元使用的数据点包括：
    将获取的所述预设区域内的每个数据点承载的数据的方差与第二预设阈值比较，其中，所述每个数据点承载的数据的方差包括预设层数数据的方差；
    若所述数据点任一层数据的方差大于所述第二预设阈值，则删除所述数据点；
    若所述数据点每层数据的方差小于或等于所述第二预设阈值，则确定 所述数据点满足预设条件作为计算所述第二干扰自相关矩阵Ruu'步骤中使用的数据点。
  16. 根据权利要求9所述的装置，其特征在于，所述装置还包括：
    第二判断单元，用于在获取所述数据点的比特对数似然比之前，将用户设备的秩与接收天线数进行比较；
    若所述秩小于所述接收天线数，则执行所述获取所述数据点的比特对数似然比；
    若所述秩大于或等于所述接收天线数，则利用所述Ruu对所述接收信号y进行干扰抑制合并。
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