WO2017121175A1 - 一种数据处理方法和装置 - Google Patents

一种数据处理方法和装置 Download PDF

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Publication number
WO2017121175A1
WO2017121175A1 PCT/CN2016/104137 CN2016104137W WO2017121175A1 WO 2017121175 A1 WO2017121175 A1 WO 2017121175A1 CN 2016104137 W CN2016104137 W CN 2016104137W WO 2017121175 A1 WO2017121175 A1 WO 2017121175A1
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Prior art keywords
user
beamforming vector
group
vertical beamforming
users
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PCT/CN2016/104137
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English (en)
French (fr)
Inventor
项飞
吕刚明
田华
张国梅
秦洪峰
王绍鹏
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中兴通讯股份有限公司
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Publication of WO2017121175A1 publication Critical patent/WO2017121175A1/zh

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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
    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present invention relates to the field of communications, and in particular, to a data processing method and apparatus.
  • MIMO Multiple-Input Multiple-Output
  • a full-dimensional MIMO (Full MIMO) system adopts a 2D area array antenna array structure, and a large number of antenna elements are used as antennas of active antenna elements at the base station end, allowing dynamic adaptive pre-preparation. Encoding, so that more users perform downlink transmission on the same time and frequency resources, so that high-order multi-user MIMO (MU-MIMO) transmission can be realized.
  • MU-MIMO multi-user MIMO
  • the core of multi-user precoding is to pre-process the signal at the transmitting end to eliminate the interference between users in advance to achieve multi-user communication. Therefore, under the FD-MIMO system, designing an effective precoding scheme to further improve the system performance of MU-MIMO has become the research focus.
  • the main way to reduce the complexity of precoding design is to approximate or simplify the mathematical model of existing precoding scheme design, such as precoding algorithm based on truncated polynomial approximation matrix inverse operation and simplified signal leakage ratio. (SLNR) method.
  • precoding algorithm based on the truncated polynomial approximation matrix inverse operation since the precoding computational complexity is mainly related to the matrix inversion operation, the precoding solution complexity is mainly reduced by polynomial approximation, but the system obtained by this method has great performance.
  • the degree depends on the polynomial approximation order, which has certain limitations.
  • the conversion matrix is used to reduce the number of required inverses of the precoding matrix solution, but the implementation of the method is that when the number of users is much smaller than Launch day The number of lines is valid, so its application scenario is limited.
  • an embodiment of the present invention provides a data processing method and apparatus.
  • User grouping is performed by using channel correlation, and a group of users after grouping is obtained;
  • the determining, according to the vertical beamforming vector, the intra-group precoding vector of each user in the user set including:
  • An intra-group precoding vector for each user in the user set is determined according to the equivalent horizontal channel information.
  • the user grouping by using channel correlation includes:
  • a group user that matches the primary user is selected based on a preset policy.
  • determining a group precoding vector of each user in the user set includes:
  • a vertical beamforming vector for each user in the set of users is determined based on preset criteria.
  • configuring a vertical beamforming vector for each user in the user set includes:
  • a beamforming vector is configured for each group of users, and each column of the vertical beamforming vector takes the same value.
  • the data processing apparatus of the embodiment of the present invention includes a user grouping module, a configuration module, and a determining module;
  • the user grouping module is configured to perform user grouping by using channel correlation, to obtain a group of users after grouping;
  • the configuration module is configured to configure a vertical beamforming vector for each user in the user set
  • the determining module is configured to determine an intra-group precoding vector of each user in the user set according to the vertical beamforming vector.
  • the determining module includes a calculating unit and a determining unit
  • the calculating unit is configured to calculate equivalent horizontal channel information by using the vertical beamforming vector
  • the determining unit is configured to determine an intra-group precoding vector of each user in the user set according to the equivalent horizontal channel information.
  • the user grouping module includes a first selecting unit and a second selecting unit
  • the first selecting unit is configured to select a primary user of each group by using a proportional fair manner
  • the second selection unit is configured to select an intra-group user that matches the primary user based on a preset policy.
  • the determining module is further configured to determine the use according to a preset criterion.
  • the vertical beamforming vector for each user in the user set is further configured to determine the use according to a preset criterion.
  • the user grouping module, the configuration module, the determining module, the calculating unit, the determining unit, the first selecting unit, and the second selecting unit may use a central processing unit (CPU) and a digital signal processor (DSP, when performing processing). Digital Singnal Processor) or Field-Programmable Gate Array (FPGA) implementation.
  • CPU central processing unit
  • DSP digital signal processor
  • DSP digital signal processor
  • FPGA Field-Programmable Gate Array
  • the configuration module is further configured to configure a vertical beamforming vector for each user, and each column of the vertical beamforming vector has a different value; or, configure a vertical for each user.
  • a beamforming vector, and each column of the vertical beamforming vector takes the same value; or, one beamforming vector is configured for each group of users, and each column of the vertical beamforming vector takes a different value; or A beamforming vector is configured for each group of users, and each column of the vertical beamforming vector takes the same value.
  • the data processing method and apparatus provided by the embodiments of the present invention use the channel correlation to perform user grouping to obtain a grouped user set; configure a vertical beamforming vector for each user in the user set; and according to the vertical beam assignment A shape vector that determines an intra-group precoding vector for each user in the set of users.
  • the embodiment of the present invention achieves the effect of channel dimension reduction while reducing the inter-group interference by configuring the vertical beamforming vector; further, the horizontal precoding can realize the transmission of multiple users in the group, thereby effectively solving the existing precoding method.
  • FIG. 1 is a schematic flowchart 1 of an implementation process of a data processing method according to an embodiment of the present invention
  • FIG. 2 is a second schematic diagram of an implementation process of a data processing method according to an embodiment of the present invention.
  • FIG. 3 is a schematic flowchart 3 of an implementation process of a data processing method according to an embodiment of the present invention.
  • FIG. 4 is a structural diagram of a MU FD-MIMO system according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of user channel correlation according to an embodiment of the present invention.
  • FIG. 6 is a spectrum efficiency diagram of a system under different vertical beamforming mechanisms according to an embodiment of the present invention.
  • FIG. 7 is a diagram showing an edge spectral efficiency diagram of different vertical beamforming mechanisms according to an embodiment of the present invention.
  • FIG. 8 is a diagram showing a system spectrum efficiency diagram of a different user grouping scheme according to an embodiment of the present invention.
  • FIG. 9 is a diagram showing an edge spectrum efficiency diagram of different user grouping schemes according to an embodiment of the present invention.
  • FIG. 10 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention.
  • user grouping is performed by using channel correlation to obtain a group of users after grouping; a vertical beamforming vector is configured for each user in the user set; and the vertical beamforming vector is determined according to the vertical beamforming vector The intra-group precoding vector for each user in the user set.
  • FIG. 1 is a schematic flowchart 1 of an implementation process of a data processing method according to an embodiment of the present invention. As shown in FIG. 1 , a data processing method according to an embodiment of the present invention includes:
  • Step 101 Perform user grouping by using channel correlation to obtain a group of users after grouping
  • user grouping is performed by using channel correlation to ensure strong channel correlation among user groups in a group, and channel correlation between user lines in a group is poor, and the user is Each user in the collection can serve on the same time-frequency resource.
  • the user group after the user grouping has the strong correlation of the channel in the user column in the group, and the channel correlation between the users in the group is poor, which is equivalent to reducing the inter-group to some extent by combining the channel correlation.
  • Step 102 Configure a vertical beamforming vector for each user in the user set.
  • a vertical beamforming vector may be configured for each user in the user set.
  • the target user's receiving power can be enhanced, and the leakage power to other groups of users can be reduced, and the effective power can be reduced.
  • configuring a vertical beamforming vector for each user in the user set includes the following four methods:
  • a vertical beamforming vector is configured for each user, and each column of the vertical beamforming vector has a different value
  • a vertical beamforming vector is configured for each user, and each column of the vertical beamforming vector has the same value;
  • a beamforming vector is configured for each group of users, and each column of the vertical beamforming vector has a different value
  • a beamforming vector is configured for each group of users, and each column of the vertical beamforming vector has the same value.
  • Step 103 Determine, according to the vertical beamforming vector, an intra-group precoding vector of each user in the user set.
  • the embodiment of the present invention may follow a relatively classic precoding criterion, such as maximizing signal leakage noise.
  • user grouping is performed by using channel correlation to obtain a grouped user set; a vertical beamforming vector is configured for each user in the user set; and the vertical beamforming vector is obtained according to the vertical beam forming vector Determining an intra-group precoding vector for each user in the set of users.
  • the embodiment of the present invention achieves the effect of channel dimension reduction while reducing the inter-group interference by configuring the vertical beamforming vector; further, the horizontal precoding can realize the transmission of multiple users in the group, thereby effectively solving the existing precoding method. There is a problem of high computational complexity and limited application scenarios.
  • FIG. 2 is a schematic flowchart of the implementation of a data processing method according to an embodiment of the present invention. As shown in FIG. 2, the data processing method in the embodiment of the present invention includes:
  • Step 1011 Select a primary user of each group by using a proportional fair manner
  • Step 1012 Select an intra-group user that matches the primary user based on a preset policy.
  • a proportional fair-maximum chord distance (PF-MCD) method may be used to perform user grouping, that is, a proportional fair PC mode is used to select a primary user of each group, and the users in the group are based on MCD selection;
  • PF-CC mode can also be used for user grouping, that is, the proportional user is used to select the primary users of each group, and when the users in the group are selected, the user and the primary user are only required to be compared each time after sorting through the column channels.
  • the row correlation is selected, and the user with the worst correlation with the primary user row is selected as the same group user; the PF method can also be used for user grouping, that is, the primary user selects proportionally, and the users in the group randomly select.
  • Step 102 Configure a vertical beamforming vector for each user in the user set.
  • a vertical beamforming vector may be configured for each user in the user set.
  • the leakage power to other groups of users can be reduced, and the effective channel dimension can be reduced.
  • configuring a vertical beamforming vector for each user in the user set includes the following four methods:
  • a vertical beamforming vector is configured for each user, and each column of the vertical beamforming vector has a different value
  • a vertical beamforming vector is configured for each user, and each column of the vertical beamforming vector has the same value;
  • a beamforming vector is configured for each group of users, and each column of the vertical beamforming vector has a different value
  • a beamforming vector is configured for each group of users, and each column of the vertical beamforming vector has the same value.
  • Step 103 Determine, according to the vertical beamforming vector, an intra-group precoding vector of each user in the user set.
  • the embodiment of the present invention may follow a relatively classic precoding criterion, such as a max-SLNR criterion, Maximize ZF, BD precoding methods, etc.
  • the data processing method in the embodiment of the present invention selects a primary user of each group by using a proportional fair manner; selects an intra-group user that matches the primary user according to a preset policy; and configures a vertical for each user in the user set. a beamforming vector; determining an intra-group precoding vector for each user in the user set based on the vertical beamforming vector.
  • the embodiment of the present invention achieves the effect of channel dimension reduction while reducing the inter-group interference by configuring the vertical beamforming vector; further, the horizontal precoding can realize the transmission of multiple users in the group, thereby effectively solving the existing precoding method.
  • FIG. 3 is a schematic flowchart 3 of an implementation process of a data processing method according to an embodiment of the present invention. As shown in FIG. 3, a data processing method according to an embodiment of the present invention includes:
  • Step 101 Perform user grouping by using channel correlation to obtain a group of users after grouping
  • user grouping is performed by using channel correlation to ensure strong channel correlation among user groups in a group, and channel correlation between user lines in a group is poor, and the user is Each user in the collection can serve on the same time-frequency resource.
  • the user group after the user grouping has the strong correlation of the channel in the user column in the group, and the channel correlation between the users in the group is poor, which is equivalent to reducing the inter-group to some extent by combining the channel correlation.
  • Step 102 Configure a vertical beamforming vector for each user in the user set.
  • a vertical beamforming vector may be configured for each user in the user set.
  • the leakage power to other groups of users can be reduced, and the effective channel dimension can be reduced.
  • configuring a vertical beamforming vector for each user in the user set includes the following four methods:
  • a vertical beamforming vector is configured for each user, and each column of the vertical beamforming vector has a different value
  • a vertical beamforming vector is configured for each user, and each column of the vertical beamforming vector has the same value;
  • a beamforming vector is configured for each group of users, and each column of the vertical beamforming vector has a different value
  • a beamforming vector is configured for each group of users, and each column of the vertical beamforming vector has the same value.
  • Step 1031 Calculate the equivalent horizontal channel information by using the vertical beamforming vector
  • the vertical beamforming vector is applied to each column channel for calculation to obtain equivalent horizontal channel information.
  • Step 1032 Determine an intra-group precoding vector of each user in the user set according to the equivalent horizontal channel information.
  • the embodiment of the present invention may comply with a method such as max-SLNR criterion, maximum ZF, and BD precoding. Pre-coding guidelines.
  • user grouping is performed by using channel correlation to obtain a grouped user set; and vertical beamforming vectors are configured for each user in the user set. And calculating, by using the vertical beamforming vector, equivalent horizontal channel information; determining an intra-group precoding vector of each user in the user set according to the equivalent horizontal channel information.
  • the embodiment of the present invention achieves the effect of channel dimension reduction while reducing the inter-group interference by configuring the vertical beamforming vector; further, the horizontal precoding can realize the transmission of multiple users in the group, thereby effectively solving the existing precoding method.
  • N t N v ⁇ N h antennas (where the vertical direction N v antennas, horizontal Direction N h antenna).
  • Each cell randomly and uniformly broadcasts N users, wherein the scheduled users are divided into L groups, each group of N u users, and each user configures a single antenna.
  • H kl is a channel matrix of 1 ⁇ (N v ⁇ N h ) dimensions of the kth user of the first group, wherein A channel vector representing the 1 ⁇ N v dimension of the i-th column antenna.
  • the base station transmit power is P
  • each user receives noise as z k and the noise power is ⁇ 2 .
  • the received signal of the kth user of the first group can be expressed as:
  • s kl is the transmission signal of the kth group of the first group
  • the received signal to interference and noise ratio (SINR) of the kth user of the first group can be obtained by the formula (1), and the signal leakage ratio (SLNR) is as shown in the formulas (2) and (3):
  • the edge spectral efficiency is the 5% of the average spectral efficiency per user.
  • an application example data processing method of the present invention includes the following implementation flow:
  • the base station selects the scheduled user according to the channel state information, and considers the user scheduling fairness, the primary user uses the proportional fairness (PF) to select, and the users in the group select according to the chord distance criterion.
  • PF proportional fairness
  • PF Proportional Fairness
  • the third step 1) compare the column channel correlations of the primary user k 1 and the remaining N-1 users and sort them from large to small (since each user has N h columns, all of the comparison calculations are large, and after theoretical verification, SVD decomposition is performed for each user channel, and the left singular vector can represent the user column channel.
  • N v ⁇ 1 dimensional vector represents the user column channel, which greatly reduces the comparison times)
  • selection The first N s users, the users within the group according to the maximum chord distance criterion N u -1 users are selected from N s -1 users, wherein H h is N h ⁇ 1 dimensional user line channel information (users in the group have strong column correlation and weak line correlation);
  • the fourth step since the channel correlation of the user column is sorted in the third step, the second group of primary users k 2 is taken out (the channel correlation with the first group of primary user columns is weak), and N s users are correspondingly extracted. And the third step 2) select the second group of remaining N u -1;
  • the fifth step after the selection of the L group of users, the loop ends;
  • Step 6 Each time slot updates the selected user group according to the above method
  • the calculation of the two-step precoding matrix is performed on the selected user set at the base station side, which is divided into the following two steps:
  • the base station calculates a beamforming vector for each column in the vertical direction based on the user channel information and maximizing the vertical direction signal to noise and noise ratio (SLNR). (N v ⁇ 1 dimension), as shown below:
  • a correlation matrix representing N v ⁇ N v the first representing the correlation matrix of the optimized column of the target user, and the second representing the correlation matrix of the remaining columns of the target user, similarly, the latter two respectively represent the optimized column and residual of the interfering user
  • the correlation matrix of the columns, and they are all Hermitian matrices; Where C 1 and C 2 are constants, so further simplifying (7) is
  • the precoding vector of a column of antennas can be obtained. among them Then, the precoding vector of other column antennas is obtained by simple iteration, that is, there is a N h column antenna, and a user precoding matrix can be obtained by N h iterations;
  • the second step is to obtain the vertical beamforming vector in the first step. Applies to each column channel to get equivalent horizontal channel information, defined here Designing the horizontal multi-user precoding matrix with the maximum letter-to-noise ratio criterion for the 1 ⁇ N h -dimensional equivalent channel vectors of the target user and the interfering user, respectively.
  • the channel WINNERII/+3D channel model between the base station and the user is configured with a 16*8 uniform area array, that is, a vertical 16-row antenna, a horizontal 8-row antenna, and a vertical antenna spacing of 0.5 ⁇ ( ⁇ indicates wavelength), and a horizontal antenna spacing of 10 ⁇ .
  • the solid line CC-Intra indicates the correlation of the corresponding channel of the user column antenna in the group
  • the dotted line CC-Inter indicates the correlation of the corresponding channel of the user column antenna between the groups
  • the dotted line ECC-Intra indicates the group.
  • the correlation of channels between different users is given. It can be seen from the figure that the correlation between the user column channels in the group is basically above 0.8, and the correlation of the user column channels between the groups is basically below 0.2. In this way, if the requirements of the pre-coding scheme for the user channel are met, the interference of the users between the groups can be effectively reduced according to the design scheme of the above pre-coding matrix. It can also be seen that the correlation of the equivalent channel matrix of the users in the group after the precoding matrix is low, and the multi-user transmission system is required to meet the inter-channel orthogonality between users, and the performance gain of multiple users can be obtained. .
  • the DC-pUE scheme indicates that a vertical precoding matrix W v is designed for each user, and each column Different purposes; purpose: one precoding vector per user to make the beam more accurately aimed at the target user, and the leakage to other users is minimal;
  • the IC-pUE scheme also design a vertical precoding matrix W v for each user, and each column
  • mechanism 2 only needs to calculate the precoding vector of one column of antennas for each user, so the complexity of precoding vector is reduced compared with mechanism 1.
  • the DC-eGU scheme Each group of users designs a beamforming vector, and each column of the beamforming vector is different.
  • the complexity of the precoding matrix design is lower than that of mechanism 1 and mechanism 2, that is, one beam covers a group of users;
  • the IC-eGU scheme Each group of users designs a beamforming vector, and each column of the beamforming vector is the same.
  • the precoding matrix design complexity is lower than the previous three mechanisms, that is, one beam covers a group of users.
  • the four precoding mechanisms mentioned are much better than the conjugate beamforming mechanism when the number of vertical antennas is changed from 2 to 16; the conjugate beam is used when the number of vertical antennas is 16.
  • the spectral efficiency and edge spectral efficiency of the proposed scheme are increased by 1.34 to 1.99 times and 1.8 to 2.6 times, respectively, and the edge spectral efficiency is significantly improved.
  • mechanism 2 is compared with mechanism 3: when the number of vertical antennas N v is greater than the number of horizontal antennas N h ( When 8 ⁇ N v ⁇ 16), the spectral efficiency and edge spectral efficiency of the mechanism 3 system is better than that of mechanism 2, because after vertical precoding, the equivalent channel orthogonality under mechanism 3 is less damaged, that is, the mechanism Under the equivalent channel orthogonality of 2, the equivalent channel orthogonality of mechanism 3 is strong. Therefore, under mechanism 2, the user interference between users is large.
  • mechanism 3 is better than that of mechanism 2; N v of vertical antenna level less than or equal
  • N v of vertical antenna level less than or equal
  • the system scene is unchanged, and the SSE and ESE of different user grouping schemes are simulated based on the first vertical beamforming scheme (The DC-pUE scheme).
  • Figure 8 and Figure 9 show the system spectral efficiency and edge spectral efficiency of different user grouping schemes based on the first precoding scheme;
  • PF-MCD is selected by the user according to the proportional fairness, and the users in the group according to the maximum chord distance (MCD) )
  • PF-CC that is, the primary user selects the same as the PF-MCD, but selects the users in the group, after sorting through the column channels, each time only needs to compare the row correlation between N s -1 users and the primary user, and Select the user with the worst correlation with the primary user line as the same group of users;
  • PF the primary user selects the proportion fairly, and the users in the group randomly select; compare the three user schemes, as shown in Table 2, the complexity is in turn In the same way, the performance of the first two schemes is greatly improved compared with the third scheme.
  • the embodiment of the present invention reduces the complexity from two aspects: on the one hand, compared with the original channel matrix H k , the equivalent channel matrix
  • the dimension is reduced to two dimensions, that is, from 1 ⁇ (N v ⁇ N h ) to 1 ⁇ N h dimension; on the other hand, the calculation amount required to obtain W v and W h by the data processing method in the embodiment of the present invention is respectively O(N h ⁇ N v 3 ) and O(N h 3 ), the total precoding vector calculation complexity is the sum of the two, while the traditional method requires the calculation amount to be O((N v ⁇ N h ) 3 Therefore, the data processing method in the embodiment of the present invention achieves lower computational complexity of the precoding vector.
  • the data processing apparatus 00 includes a user grouping module 01, a configuration module 02, and a determining module 03;
  • the user grouping module 01 is configured to perform user grouping by using channel correlation, to obtain a group of users after grouping;
  • the configuration module 02 is configured to configure a vertical beamforming vector for each user in the user set
  • the determining module 03 is configured to determine an intra-group precoding vector of each user in the user set according to the vertical beamforming vector.
  • the determining module 03 includes a calculating unit 031 and a determining unit 032;
  • the calculating unit 031 is configured to calculate equivalent horizontal channel information by using the vertical beamforming vector
  • the determining unit 032 is configured to determine an intra-group precoding vector of each user in the user set according to the equivalent horizontal channel information.
  • the user grouping module 01 includes a first selection unit. 011 and second selection unit 012;
  • the first selecting unit 011 is configured to select a primary user of each group by using a proportional fair manner
  • the second selecting unit 012 is configured to select an intra-group user that matches the primary user based on a preset policy.
  • the determining module 03 is further configured to determine a vertical beamforming vector of each user in the set of users based on a preset criterion.
  • the configuration module 02 is further configured to configure each user with a vertical beamforming vector, and each column of the vertical beamforming vector has a different value; or, configure a vertical for each user. a beamforming vector, and each column of the vertical beamforming vector takes the same value; or, one beamforming vector is configured for each group of users, and each column of the vertical beamforming vector takes a different value; or A beamforming vector is configured for each group of users, and each column of the vertical beamforming vector takes the same value.
  • each module in the data processing apparatus and each unit included in each module of the embodiment of the present invention may be implemented by a processor in the data processing apparatus, or may be implemented by a specific logic circuit; For example, in practical applications, it may be implemented by a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA) located in the data processing apparatus.
  • CPU central processing unit
  • MPU microprocessor
  • DSP digital signal processor
  • FPGA field programmable gate array
  • user grouping is performed by using channel correlation to obtain a group of users after grouping; a vertical beamforming vector is configured for each user in the user set; and the user is determined according to the vertical beamforming vector
  • the intra-group precoding vector for each user in the collection achieves the problem of reducing inter-group interference by configuring a vertical beamforming vector.
  • the effect of channel dimensionality reduction; further, the horizontal precoding can realize the transmission of multiple users in the group, thereby effectively solving the problem of high computational complexity and limited application scenarios in the existing precoding method.

Abstract

本发明实施例提供一种数据处理方法和装置，利用信道相关性进行用户分组，得到分组后的用户集合；为所述用户集合中的各用户配置垂直波束赋形矢量；根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。

Description

一种数据处理方法和装置 技术领域
本发明涉及通信领域，尤其涉及一种数据处理方法和装置。
背景技术
大规模多入多出(Multiple-Input Multiple-Output，MIMO)系统使得频谱效率和系统容量的大大提升，但是实际系统中受天线尺寸及基站空间限制，不可能在水平方向上摆放大量天线。为解决此问题，全维MIMO(Full dimensional MIMO，FD-MIMO)系统采用2D面阵天线阵结构，在基站端配备大量的天线阵元为有源天线阵元的天线，允许进行动态自适应预编码，从而使得更多的用户在相同的时、频资源上进行下行传输，从而可实现高阶多用户MIMO(MU-MIMO)传输。其中，多用户预编码的核心就是在发送端对信号进行预处理从而将用户间的干扰预先消除，以实现多用户通信。因此在FD-MIMO系统下，设计有效的预编码方案进一步提升MU-MIMO的系统性能成为研究重点。
然而，随着天线数的增多，信道矩阵的维度随之扩展，导致传统的预编码方案复杂度随之提高。在FD-MIMO系统下，降低预编码设计的复杂度主要方式是对现有预编码方案设计的数学模型做近似或简化，如基于截断多项式近似矩阵逆运算的预编码算法和简化信漏噪比(SLNR)的方法。对于基于截断多项式近似矩阵逆运算的预编码算法而言，由于预编码计算复杂度主要与矩阵求逆操作有关，主要通过多项式近似降低预编码求解复杂度，但该方法所获得的系统性能很大程度上取决于多项式近似阶数，存在一定局限性；对于简化SLNR而言，即通过转换矩阵以降低预编码矩阵求解的需要求逆的次数，但该方法的实现前提是当用户数远远小于发射天 线数时有效，故其应用场景有限。
发明内容
有鉴于此，为解决上述问题本发明实施例提供一种数据处理方法和装置。
为达到上述目的，本发明实施例的技术方案是这样实现的：
本发明实施例的数据处理方法，包括：
利用信道相关性进行用户分组，得到分组后的用户集合；
为所述用户集合中的各用户配置垂直波束赋形矢量；
根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
本发明实施例中，所述根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量，包括：
利用所述垂直波束赋形矢量，计算得到等效水平信道信息；
根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量。
本发明实施例中，所述利用信道相关性进行用户分组，包括：
利用比例公平方式选择各组的主用户；
基于预设策略选择与所述主用户相匹配的组内用户。
本发明实施例中，确定所述用户集合中各用户的组内预编码矢量，包括：
基于预设准则确定所述用户集合中的各用户的垂直波束赋形矢量。
本发明实施例中，为所述用户集合中的各用户配置垂直波束赋形矢量，包括：
为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
或，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；
或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
本发明实施例的数据处理装置，包括用户分组模块、配置模块和确定模块；
所述用户分组模块，配置为利用信道相关性进行用户分组，得到分组后的用户集合；
所述配置模块，配置为给所述用户集合中的各用户配置垂直波束赋形矢量；
所述确定模块，配置为根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
本发明实施例中，所述确定模块包括计算单元和确定单元；
所述计算单元，配置为利用所述垂直波束赋形矢量，计算得到等效水平信道信息；
所述确定单元，配置为根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量。
本发明实施例中，所述用户分组模块包括第一选择单元和第二选择单元；
所述第一选择单元，配置为利用比例公平方式选择各组的主用户；
所述第二选择单元，配置为基于预设策略选择与所述主用户相匹配的组内用户。
本发明实施例中，所述确定模块，还配置为基于预设准则确定所述用 户集合中的各用户的垂直波束赋形矢量。
用户分组模块、配置模块、确定模块、计算单元、确定单元、第一选择单元、第二选择单元在执行处理时，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。
本发明实施例中，所述配置模块，还配置为给每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；或，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
本发明实施例所提供的数据处理方法和装置，利用信道相关性进行用户分组，得到分组后的用户集合；为所述用户集合中的各用户配置垂直波束赋形矢量；根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。如此，本发明实施例通过配置垂直波束赋形矢量来降低用户组间干扰的同时达到信道降维的效果；进一步通过水平预编码能够实现组内多用户的传输，从而有效解决现有预编码方法中存在的计算复杂度高且应用场景受限的问题。
附图说明
图1为本发明实施例数据处理方法的实现流程示意图一；
图2为本发明实施例数据处理方法的实现流程示意图二；
图3为本发明实施例数据处理方法的实现流程示意图三；
图4为本发明实施例MU FD-MIMO系统架构图；
图5为本发明实施例用户信道相关性示意图；
图6为本发明实施例不同垂直波束赋形机制下系统频谱效率图；
图7为本发明实施例不同垂直波束赋形机制下边缘频谱效率图；
图8为本发明实施例不同用户分组方案下的系统频谱效率图；
图9为本发明实施例不同用户分组方案下的边缘频谱效率图；
图10为本发明实施例数据处理装置的组成结构示意图。
具体实施方式
在本发明实施例中，利用信道相关性进行用户分组，得到分组后的用户集合；为所述用户集合中的各用户配置垂直波束赋形矢量；根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
下面结合附图及具体实施例对本发明再作进一步详细的说明。
实施例一
图1为本发明实施例数据处理方法的实现流程示意图一，如图1所示，本发明实施例数据处理方法包括：
步骤101，利用信道相关性进行用户分组，得到分组后的用户集合；
具体地，在多用户全维多输入多输出MU FD-MIMO系统中，利用信道相关性进行用户分组，以保证组内用户列信道相关性强，组间用户列信道相关性差，且所述用户集合中的各用户均可在同一时频资源上服务。换句话说，即通过用户分组后的用户集合具有组内用户列信道相关性强，组间用户列信道相关性差的特性，相当于通过结合信道相关性的方式，在一定程度上降低了组间用户干扰。
步骤102，为所述用户集合中的各用户配置垂直波束赋形矢量；
这里，在MU FD-MIMO系统中，为了消除组间用户干扰，可以为所述用户集合中的各用户配置垂直波束赋形矢量。这样，当针对每用户均配置垂直波束赋形矢量，除了可以达到消除组间用户干扰的效果，以期增强目标用户的接收功率，还能够降低对其他组用户的泄露功率，同时降低有效 信道维度。
具体地，为所述用户集合中的各用户配置垂直波束赋形矢量，包括如下四种方式：
方式一，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
方式二，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；
方式三，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
方式四，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
步骤103，根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
具体地，本发明实施例在根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量的过程中，可以遵循比较经典的预编码准则，如最大化信漏噪比(max-SLNR)准则、最大化ZF、块对角化(BD)预编码方法等。
通过本发明实施例所述数据处理方法，利用信道相关性进行用户分组，得到分组后的用户集合；为所述用户集合中的各用户配置垂直波束赋形矢量；根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。如此，本发明实施例通过配置垂直波束赋形矢量来降低用户组间干扰的同时达到信道降维的效果；进一步通过水平预编码能够实现组内多用户的传输，从而有效解决现有预编码方法中存在的计算复杂度高且应用场景受限的问题。
实施例二
图2为本发明实施例数据处理方法的实现流程示意图二，如图2所示，本发明实施例数据处理方法包括：
步骤1011，利用比例公平方式选择各组的主用户；
步骤1012，基于预设策略选择与所述主用户相匹配的组内用户；
具体地，在本发明实施例步骤1011～1012中，可以采用比例公平-最大弦距离(PF-MCD)方式来进行用户分组，即利用比例公平PC方式选择各组的主用户，组内用户根据MCD选择；还可以采用PF-CC方式来进行用户分组，即利用比例公平PC方式选择各组的主用户，选择组内用户时则在经过列信道排序后，每次只需比较用户与主用户的行相关性，并且选出与主用户行相关性最差的用户作为同组用户；还可以采用PF方式来进行用户分组，即主用户用比例公平进行选择，组内用户随机选取。
步骤102，为所述用户集合中的各用户配置垂直波束赋形矢量；
这里，在MU FD-MIMO系统中，为了消除组间用户干扰，可以为所述用户集合中的各用户配置垂直波束赋形矢量。这样，当针对每用户均配置垂直波束赋形矢量，除了可以达到消除组间用户干扰的效果，以期增强目标用户的接收功率，还能够降低对其他组用户的泄露功率，同时降低有效信道维度。
具体地，为所述用户集合中的各用户配置垂直波束赋形矢量，包括如下四种方式：
方式一，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
方式二，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；
方式三，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
方式四，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
步骤103，根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
具体地，本发明实施例在根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量的过程中，可以遵循比较经典的预编码准则，如max-SLNR准则、最大化ZF、BD预编码方法等。
通过本发明实施例所述数据处理方法，利用比例公平方式选择各组的主用户；基于预设策略选择与所述主用户相匹配的组内用户；为所述用户集合中的各用户配置垂直波束赋形矢量；根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。如此，本发明实施例通过配置垂直波束赋形矢量来降低用户组间干扰的同时达到信道降维的效果；进一步通过水平预编码能够实现组内多用户的传输，从而有效解决现有预编码方法中存在的计算复杂度高且应用场景受限的问题。
实施例三
图3为本发明实施例数据处理方法的实现流程示意图三，如图3所示，本发明实施例数据处理方法包括：
步骤101，利用信道相关性进行用户分组，得到分组后的用户集合；
具体地，在多用户全维多输入多输出MU FD-MIMO系统中，利用信道相关性进行用户分组，以保证组内用户列信道相关性强，组间用户列信道相关性差，且所述用户集合中的各用户均可在同一时频资源上服务。换句话说，即通过用户分组后的用户集合具有组内用户列信道相关性强，组间用户列信道相关性差的特性，相当于通过结合信道相关性的方式，在一定程度上降低了组间用户干扰。
步骤102，为所述用户集合中的各用户配置垂直波束赋形矢量；
这里，在MU FD-MIMO系统中，为了消除组间用户干扰，可以为所述用户集合中的各用户配置垂直波束赋形矢量。这样，当针对每用户均配置垂直波束赋形矢量，除了可以达到消除组间用户干扰的效果，以期增强目标用户的接收功率，还能够降低对其他组用户的泄露功率，同时降低有效信道维度。
具体地，为所述用户集合中的各用户配置垂直波束赋形矢量，包括如下四种方式：
方式一，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
方式二，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；
方式三，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
方式四，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
步骤1031，利用所述垂直波束赋形矢量，计算得到等效水平信道信息；
具体地，将所述垂直波束赋形矢量应用于各列信道加以计算，得到等效水平信道信息。
步骤1032，根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量。
具体地，本发明实施例在根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量的过程中，可以遵从诸如max-SLNR准则、最大化ZF、BD预编码方法等预编码准则。
通过本发明实施例所述数据处理方法，利用信道相关性进行用户分组，得到分组后的用户集合；为所述用户集合中的各用户配置垂直波束赋形矢 量；利用所述垂直波束赋形矢量，计算得到等效水平信道信息；根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量。如此，本发明实施例通过配置垂直波束赋形矢量来降低用户组间干扰的同时达到信道降维的效果；进一步通过水平预编码能够实现组内多用户的传输，从而有效解决现有预编码方法中存在的计算复杂度高且应用场景受限的问题。
下面结合具体应用场景对本发明实施例所述数据处理方法进行具体描述。
考虑如图4所示的系统，单小区FD-MIMO下行传输系统，其中基站配置2D均匀面阵(UPA)，包含Nt＝Nv×Nh根天线(其中垂直方向Nv根天线，水平方向Nh根天线)。每小区随机均匀播撒N个用户，其中将调度的用户分为L组，每组Nu个用户，且每个用户配置单根天线。Hkl为第l组第k个用户的1×(Nv×Nh)维的信道矩阵，其中，
Figure PCTCN2016104137-appb-000001
表示第i列天线1×Nv维的信道矢量。假设基站发射功率为P，各用户接收噪声为zk，噪声功率为σ2。则第l组第k个用户的接收信号可以表示为：
Figure PCTCN2016104137-appb-000002
公式(1)中，skl为第l组k个用户的发送信号；
Figure PCTCN2016104137-appb-000003
表示第k个用户的垂直预编码矩阵，其中
Figure PCTCN2016104137-appb-000004
为Nv×1维矢量，且
Figure PCTCN2016104137-appb-000005
Wh(Nh×1维，||Wh||＝1)表示第k个用户的水平方向的预编码矩阵。由公式(1)可以得到第l组第k个用户的接收信干噪比(SINR)，和信漏噪比 (SLNR)如公式(2)、(3)所示：
Figure PCTCN2016104137-appb-000006
Figure PCTCN2016104137-appb-000007
系统总频谱效率和每用户平均频谱效率分别为公式(4)和公式(5)所示：
Figure PCTCN2016104137-appb-000008
Figure PCTCN2016104137-appb-000009
边缘频谱效率为每用户平均频谱效率的第5％。
基于如图4所示的系统，本发明一应用示例数据处理方法包括如下实现流程：
第一阶段，假设基站已知理想信道状态信息，则基站根据信道状态信息选择被调度用户，考虑用户调度公平性，主用户利用比例公平(PF)进行选择，组内用户根据弦距离准则选择，选择L组，每组Nu个用户；具体操作如下：
第一步，定义初始用户集合S＝{1,2,...N}，初始化已选用户集合
Figure PCTCN2016104137-appb-000010
第二步，首先利用比例公平(PF)准则选出第一组主用户，即：
Figure PCTCN2016104137-appb-000011
更新剩余用户集合S＝S-{k1}及已选用户集合κ1＝κ1+{k1}；
第三步，1)比较主用户k1和剩余N-1个用户的列信道相关性并且由大 到小排序(由于每用户有Nh列，全部进行比较计算量大，经过理论验证，对每用户信道做SVD分解，其左奇异矢量可以表征用户列信道，则用Ui(i＝1…N),Nv×1维矢量表示用户列信道，则大大降低比较次数)；2)选择前Ns个用户，组内用户根据最大弦距离准则
Figure PCTCN2016104137-appb-000012
从Ns-1个用户中选出Nu-1个用户，其中Hh为Nh×1维的用户行信道信息(组内用户具有列相关性强，行相关性弱特点)；
第四步，由于第三步中对用户列信道相关性进行了排序，因此，再取出第二组主用户k2(与第一组主用户列信道相关性弱)，相应取出Ns个用户，同第三步2)选出第二组剩余Nu-1；
第五步，直到选出L组用户后循环结束；
第六步：每个时隙根据上述方法更新所选用户组；
第二阶段，在基站端对已选出的用户集合进行两步预编码矩阵的计算，分以下两步：
第一步，基站根据用户信道信息，以最大化垂直方向信漏噪比(SLNR)为准则，计算垂直方向的每列波束赋形矢量
Figure PCTCN2016104137-appb-000013
(Nv×1维)，如下式所示：
Figure PCTCN2016104137-appb-000014
展开(6)式，优化目标列，固定其余列天线，则可以得到下式：
Figure PCTCN2016104137-appb-000015
其中，
Figure PCTCN2016104137-appb-000016
和
Figure PCTCN2016104137-appb-000017
表示Nv×Nv的相关矩阵，第一个表示目标用户的优化列的相关矩阵，第二个表 示目标用户的其余列的相关矩阵，类似，后两项分别表示干扰用户的优化列和剩余列的相关矩阵，并且它们都是Hermitian矩阵；令
Figure PCTCN2016104137-appb-000018
其中C1和C2是常数，因此进一步简化(7)为
根据广义瑞利熵知识，可以得到一列天线的预编码矢量
Figure PCTCN2016104137-appb-000020
Figure PCTCN2016104137-appb-000021
其中
Figure PCTCN2016104137-appb-000022
Figure PCTCN2016104137-appb-000023
然后通过简单的迭代获得其他列天线的预编码矢量，即有Nh列天线，则通过Nh次迭代即可获得一个用户的预编码矩阵；
第二步，将第一步得到的垂直波束赋形矢量
Figure PCTCN2016104137-appb-000024
应用于各列信道，得到等效水平信道信息，此处定义
Figure PCTCN2016104137-appb-000025
分别为目标用户和干扰用户的1×Nh维的等效信道矢量，采用最大化信漏噪比准则设计水平方向的多用户预编码矩阵
Figure PCTCN2016104137-appb-000026
Figure PCTCN2016104137-appb-000027
则
Figure PCTCN2016104137-appb-000028
其中，
Figure PCTCN2016104137-appb-000029
在应用示例一中：
考虑3D-UMi单小区100用户场景，基站与用户间信道WINNERⅡ/+3D信道模型。基站配置天线为16*8均匀面阵，即垂直方向16行天线，水平方向8列天线，且垂直天线间距为0.5λ(λ表示波长)，水平天线间距为10λ。 以L＝2，Ns＝20,Nu＝8为例，每用户配备单根天线。基站发射功率P＝44dBm，噪声功率Noise＝-174dBm/Hz。仿真10个Drop，每个Drop包含100个TTI。在该场景下来验证本发明实施例所提及的用户分组方案的有效性。
如图5所示，所述实线CC-Intra表示组内用户列天线对应信道的相关性，虚线CC-Inter表示组间用户列天线对应信道的相关性，点划线ECC-Intra表示组内用户等效信道的相关性。并给出了不同用户间信道的相关性。从图中可以看出，组内用户列信道间的相关性基本在0.8以上，组间用户列信道的相关性基本在0.2以下。如此，满足本预编码方案对用户信道的要求，则根据以上预编码矩阵的设计方案可以有效地降低组间用户的干扰。从中还可以看出，通过预编码矩阵作用后的组内用户的等效信道矩阵的相关性较低，满足多用户传输系统对用户间信道间正交性的要求，可以获得多用户的性能增益。
在应用示例二中：
考虑与应用示例一相同的系统场景，将基站天线配置改为水平天线配置8根天线，垂直天线数从2～16变化，在该场景下，采用系统频谱效率(SSE)和边缘频谱效率(ESE)作为本方案性能的度量，来比较随着垂直天线数变化，不同垂直波束赋形机制下的性能。
图6中和图7中对比所提四种机制和大规模MIMO下共轭波束赋形机制，提供了系统频谱效率和边缘频谱效率，先来介绍所提四种机制如下：
The DC-pUE scheme：表示为每用户设计一个垂直预编码矩阵Wv，且每列
Figure PCTCN2016104137-appb-000030
各不相同；目的：每用户一个预编码矢量使其波束更精准的对准目标用户，且对其他用户泄漏最小；
The IC-pUE scheme：同样为每个用户设计一个垂直预编码矩阵Wv，且每列
Figure PCTCN2016104137-appb-000031
都相同；目的：与机制1相比机制2每个用户只需计算一列天线的预编码矢量，因此相比机制1，预编码矢量的复杂度有所降低；
The DC-eGU scheme：每组用户设计一个波束赋形矢量，且该波束赋形矢量的每列都不同，预编码矩阵设计复杂度低于机制1和机制2，即一个波束覆盖一组用户；
The IC-eGU scheme：每组用户设计一个波束赋形矢量，且该波束赋形矢量的每列都相同，预编码矩阵设计复杂度低于前面三种机制，即一个波束覆盖一组用户。
从整体观察到，所提四种预编码机制随着垂直天线数由2～16变化系统频谱效率和边缘频谱效率远优于共轭波束赋形机制；当垂直天线数为16时，与共轭波束赋形机制相比，所提方案获得的系统频谱效率和边缘频谱效率分别提高1.34～1.99倍和1.8～2.6倍，边缘频谱效率显著提升；当Nv＝16时，如表1所示，由上到下，复杂度降低，相应的性能也降低，即复杂度降低以牺牲部分性能为代价，符合理论分析；机制2与机制3相比：当垂直天线数Nv大于水平天线数Nh(8<Nv≤16)时，机制3系统频谱效率和边缘频谱效率优于机制2，因为经过垂直预编码后，在机制3下等效信道正交性遭到的破坏较小，即较机制2下的等效信道正交性，机制3等效信道正交性较强，因此在机制2下，用户所受组间用户干扰较大，该情况下，机制3性能优于机制2；当垂直天线数Nv小于或等于水平天线数Nh(Nv≤8)时，机制2性能优于机制3，因为，水平天线数固定为Nh＝8，系统性能主要取决于垂直天线数，而此时因为每组服务8用户，机制2、3垂直维度没有富余的自由度抑制组间干扰，因此性能基本相同；机制1与机制3相比：随垂直天线数的增多，每组用户一个波束与每用户一个波束获得系统频谱效率和边缘频谱效率很接近，说明简单迭代的方案可以进一步降低预编码复杂度的同时还能获得较好的性能，充分说明本发明实施例所述方案的有效性；除此之外，从图中还可看出，随着垂直天线数由2～16变化，系统频谱效率和边缘频谱效率显著提升，且天线数越多，抑制用户间干扰能力越强。
表1
Figure PCTCN2016104137-appb-000032
应用示例三：
在上述应用示例二的基础上，系统场景不变，基于第一种垂直波束赋形方案(The DC-pUE scheme)来仿真不同用户分组方案的SSE和ESE。
图8和图9给出了基于第一种预编码方案下不同用户分组方案的系统频谱效率和边缘频谱效率；其中PF-MCD为主用户根据比例公平选择，组内用户根据最大弦距离(MCD)选择；PF-CC：即主用户选择与PF-MCD相同，但选择组内用户，在经过列信道排序后，每次只需比较Ns-1个用户与主用户的行相关性，并且选出与主用户行相关性最差的用户作为同组用户；PF：即主用户用比例公平进行选择，组内用户随机选取；对比三种用户方案，如表2所示，其复杂度依次递减，同样其性能也依次递减，但前两种方案相比于第三种方案，性能大大提升，同样也说明本发明实施例所提用户分组方案的有效性。另外，还可看出，随着垂直天线数增加，三种用户分组方案下系统频谱效率和边缘频谱效率显著提升，也说明天线数越多，则有更多自由度抑制干扰。
表2
Figure PCTCN2016104137-appb-000033
Figure PCTCN2016104137-appb-000034
综合可以得出，本发明实施例从两方面降低了复杂度：一方面与原始信道矩阵Hk相比，等效信道矩阵
Figure PCTCN2016104137-appb-000035
维度降到两维，即从1×(Nv×Nh)降到1×Nh维；另一方面，本发明实施例所述数据处理方法获得Wv和Wh所需计算量分别为O(Nh×Nv 3)和O(Nh 3)，则总的预编码矢量计算复杂度为两者之和，而传统方法所需计算量为O((Nv×Nh)3)，因此，本发明实施例所述数据处理方法实现预编码矢量的计算复杂度更低。
实施例四
图10为本发明实施例数据处理装置的组成结构示意图，如图10所示，所述数据处理装置00包括用户分组模块01、配置模块02和确定模块03；
所述用户分组模块01，配置为利用信道相关性进行用户分组，得到分组后的用户集合；
所述配置模块02，配置为给所述用户集合中的各用户配置垂直波束赋形矢量；
所述确定模块03，配置为根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
在一示例中，如图10所示，所述确定模块03包括计算单元031和确定单元032；
所述计算单元031，配置为利用所述垂直波束赋形矢量，计算得到等效水平信道信息；
所述确定单元032，配置为根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量。
在一示例中，如图10所示，所述用户分组模块01包括第一选择单元 011和第二选择单元012；
所述第一选择单元011，配置为利用比例公平方式选择各组的主用户；
所述第二选择单元012，配置为基于预设策略选择与所述主用户相匹配的组内用户。
在一示例中，所述确定模块03，还配置为基于预设准则确定所述用户集合中的各用户的垂直波束赋形矢量。
在一示例中，所述配置模块02，还配置为给每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；或，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
在实际应用中，本发明实施例所述数据处理装置中的各模块及其各模块所包括的各单元均可以通过所述数据处理装置中的处理器实现，也可以通过具体的逻辑电路实现；比如，在实际应用中，可由位于所述数据处理装置的中央处理器(CPU)、微处理器(MPU)、数字信号处理器(DSP)、或现场可编程门阵列(FPGA)等实现。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。
工业实用性
采用本发明实施例，利用信道相关性进行用户分组，得到分组后的用户集合；为所述用户集合中的各用户配置垂直波束赋形矢量；根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。如此，本发明实施例通过配置垂直波束赋形矢量来降低用户组间干扰的同时达到 信道降维的效果；进一步通过水平预编码能够实现组内多用户的传输，从而有效解决现有预编码方法中存在的计算复杂度高且应用场景受限的问题。

Claims (10)

  1. 一种数据处理方法，所述方法包括：
    利用信道相关性进行用户分组，得到分组后的用户集合；
    为所述用户集合中的各用户配置垂直波束赋形矢量；
    根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
  2. 根据权利要求1所述的方法，其中，所述根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量，包括：
    利用所述垂直波束赋形矢量，计算得到等效水平信道信息；
    根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量。
  3. 根据权利要求1所述的方法，其中，所述利用信道相关性进行用户分组，包括：
    利用比例公平方式选择各组的主用户；
    基于预设策略选择与所述主用户相匹配的组内用户。
  4. 根据权利要求1所述的方法，其中，确定所述用户集合中各用户的组内预编码矢量，包括：
    基于预设准则确定所述用户集合中的各用户的垂直波束赋形矢量。
  5. 根据权利要求1至4任一项所述的方法，其中，为所述用户集合中的各用户配置垂直波束赋形矢量，包括：
    为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；
    或，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；
    或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的 每列取值不同；
    或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
  6. 一种数据处理装置，所述装置包括用户分组模块、配置模块和确定模块；
    所述用户分组模块，配置为利用信道相关性进行用户分组，得到分组后的用户集合；
    所述配置模块，配置为给所述用户集合中的各用户配置垂直波束赋形矢量；
    所述确定模块，配置为根据所述垂直波束赋形矢量，确定所述用户集合中各用户的组内预编码矢量。
  7. 根据权利要求6所述的装置，其中，所述确定模块包括计算单元和确定单元；
    所述计算单元，配置为利用所述垂直波束赋形矢量，计算得到等效水平信道信息；
    所述确定单元，配置为根据所述等效水平信道信息确定所述用户集合中各用户的组内预编码矢量。
  8. 根据权利要求6所述的装置，其中，所述用户分组模块包括第一选择单元和第二选择单元；
    所述第一选择单元，配置为利用比例公平方式选择各组的主用户；
    所述第二选择单元，配置为基于预设策略选择与所述主用户相匹配的组内用户。
  9. 根据权利要求6所述的装置，其中，
    所述确定模块，还配置为基于预设准则确定所述用户集合中的各用户的垂直波束赋形矢量。
  10. 根据权利要求6至9任一项所述的装置，其中，
    所述配置模块，还配置为给每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；或，为每个用户配置一个垂直波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同；或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值不同；或，为每组用户配置一个波束赋形矢量，且所述垂直波束赋形矢量的每列取值均相同。
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