WO2018126990A1 - 空分用户选择的方法及装置 - Google Patents

空分用户选择的方法及装置 Download PDF

Info

Publication number
WO2018126990A1
WO2018126990A1 PCT/CN2017/119585 CN2017119585W WO2018126990A1 WO 2018126990 A1 WO2018126990 A1 WO 2018126990A1 CN 2017119585 W CN2017119585 W CN 2017119585W WO 2018126990 A1 WO2018126990 A1 WO 2018126990A1
Authority
WO
WIPO (PCT)
Prior art keywords
matrix
user
channel response
candidate
users
Prior art date
Application number
PCT/CN2017/119585
Other languages
English (en)
French (fr)
Inventor
吴昊
李�杰
欧阳恩山
许应
Original Assignee
中兴通讯股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中兴通讯股份有限公司 filed Critical 中兴通讯股份有限公司
Priority to JP2019536942A priority Critical patent/JP6839290B2/ja
Priority to EP17890687.1A priority patent/EP3567741B1/en
Publication of WO2018126990A1 publication Critical patent/WO2018126990A1/zh

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/0486—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking channel rank into account
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • 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
    • H04L25/0202—Channel estimation
    • 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
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • H04L25/0246—Channel estimation channel estimation algorithms using matrix methods with factorisation
    • 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
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0256—Channel estimation using minimum mean square error criteria
    • 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 disclosure relates to the field of communication technologies, for example, to a method and apparatus for air separation user selection.
  • the related art generally selects an air-divided user according to the correlation between the two of the users, but this does not reflect the correlation between the multiple users. Using this algorithm may result in low correlation between two users, but the correlation between multiple users is very high, and the selected air separation users may not be the most linearly independent. If the traversal method is used to select the largest linear independent user from the candidate users for space division, the amount of operation will increase rapidly as the number of candidate users increases, and it is difficult to meet the real-time requirement.
  • the present disclosure provides a method and apparatus for air separation user selection to solve the problem of selection of air separation users.
  • the present disclosure provides a method for air separation user selection, including:
  • the channel response matrix is subjected to Rank-Revealing QR (RRQR) decomposition, and the result of the RRQR decomposition is used to determine the largest linear independent candidate user that satisfies the requirement of the number of base station spatial division users as the air separation user.
  • RRQR Rank-Revealing QR
  • the step of constructing a channel response matrix of all candidate users by using the channel response of each candidate user includes:
  • the channel response of each candidate user is normalized, and the channel response obtained by the normalization is used as a column element to construct a channel response matrix of all candidate users.
  • the step of performing RRQR decomposition on the channel response matrix, and determining, by using the result of the RRQR decomposition, a maximum linear independent candidate user that satisfies the requirement of the number of space division users of the base station as the air separation user includes:
  • the candidate user is selected as the air separation user from the determined maximum linearly independent plurality of candidate users according to the number of space division users required by the base station.
  • the number of air separation users required by the base station is determined by the following steps:
  • the step of determining the maximum linear independent candidate user that satisfies the requirement of the number of base station spatial division users as the air separation user using the channel response matrix, Q matrix and R of the space division user a matrix that determines a matrix of shaped weights for the space division user.
  • the present disclosure also provides an air separation user selection device, including:
  • An alternate user channel response obtaining module is configured to obtain a channel response of each candidate user by performing channel estimation on the received data of each candidate user;
  • a channel response matrix construction module configured to construct a channel response matrix of all candidate users by using a channel response of each candidate user
  • the air separation user selection module is configured to perform a rank orthogonal triangle RRQR decomposition on the channel response matrix, and use the RRQR decomposition result to determine a maximum linear independent candidate user that satisfies the requirement of the number of base station air separation users as the air separation user.
  • the space division user selection module is configured to obtain a permutation matrix, a Q matrix, and an R matrix by performing RRQR decomposition on the channel response matrix, and determine a maximum linearity from all candidate users according to the permutation matrix.
  • the permutation matrix is equal to the Q matrix multiplied by the R matrix.
  • the space division user selection module is further configured to determine, by using the channel independent matrix, the Q matrix, and the R matrix of the linear independent candidate users, the shaping rights of the multiple linear independent multiple candidate users. And a value matrix, and determining, according to the power requirement of the base station and the maximum linear independent candidate weighting matrix of the plurality of candidate users, determining the number of air separation users that meet the requirements of the base station.
  • it also includes:
  • the shaping weight calculation module is configured to use the channel response matrix and the Q matrix of the space division user after determining the maximum linear independent candidate user that satisfies the requirement of the number of space division users of the base station as the space division user by using the RRQR decomposition result. And the R matrix, determining the shape weight matrix of the space division user.
  • the present disclosure also provides an air separation user selection device, including:
  • processors and memory storing the processor executable instructions
  • the channel response matrix is subjected to rank orthogonal trigonometric RRQR decomposition, and the RRQR decomposition result is used to determine the largest linear independent candidate user that satisfies the requirement of the number of base station air separation users as the air separation user.
  • the present disclosure also provides a computer readable storage medium storing computer executable instructions for performing the above method.
  • the present disclosure also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer, Having the computer perform any of the methods described above.
  • the present disclosure utilizes the RRQR decomposition result of the RRQR algorithm to determine the number of space-division users satisfying the quantity requirement, the calculation amount is low, the calculation speed is fast, and the purpose of selecting an air-divided user with a low calculation amount is optimally realized. Improve the air separation performance of the communication system.
  • FIG. 1 is a block diagram of a method for air separation user selection according to an embodiment
  • FIG. 2 is a block diagram of an apparatus for air separation user selection according to an embodiment
  • FIG. 3 is a flow chart of air separation user selection provided by an embodiment
  • FIG. 4 is a schematic diagram of an apparatus for air separation user selection according to an embodiment
  • FIG. 5 is a schematic diagram of a hardware structure of an apparatus for air separation user selection according to an embodiment.
  • the zero-forcing precoding algorithm can achieve optimal performance with low complexity. performance.
  • FIG. 1 is a block diagram of a method for air separation user selection according to an embodiment. As shown in FIG. 1, the method includes:
  • Step 110 Obtain a channel response of each candidate user by performing channel estimation on the received data of each candidate user. For example, Least Square (LS) or Minimum Mean Square Error (MMSE) channel estimation, the channel response of each candidate user is obtained.
  • LS Least Square
  • MMSE Minimum Mean Square Error
  • Step 120 Construct a channel response matrix of all candidate users by using the channel response of each candidate user.
  • the channel response of each candidate user is normalized, and then the channel response of each candidate user obtained by the normalization process is used as a column element to construct a channel response matrix of all candidate users.
  • Step 130 Perform RRQR decomposition on the channel response matrix, and use the decomposition result to determine a maximum linear independent candidate user that meets the requirement of the number of space division users of the base station as the air separation user.
  • Step 130 includes: determining, by using the RRQR decomposition of the channel response matrix, a plurality of candidate users that are maximum linearly independent, and selecting, according to the number of air separation users required by the base station, among the multiple linear independent multiple candidate users. Select an alternate user as the air separation user.
  • the RRQR decomposition is performed on the channel response matrix to obtain a permutation matrix, a Q matrix, and an R matrix, wherein the channel response matrix multiplied by the permutation matrix is equal to the Q matrix multiplied by the R matrix.
  • a plurality of candidate users whose maximum linear independence is determined from all the candidate users can determine the maximum linear independent candidate user by finding the position of the element "1" in each column in the permutation matrix, so that An air separation user that satisfies the number of air separation users required by the base station is selected from the maximum linear independent candidate users.
  • the number of space division users may be determined by a power requirement of the base station and a weighted weight matrix of the maximum linear independent candidate user, wherein the maximum linear independent candidate user's weighting matrix is Determined by the channel response matrix, Q matrix, and R matrix of the largest linearly independent candidate user.
  • the channel response matrix, the Q matrix and the R matrix of the space division user may also be utilized to determine the shape weight matrix of the space division user to generate a beam with directivity.
  • the storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, or the like.
  • FIG. 2 is a block diagram of an air separation user selection apparatus according to an embodiment. As shown in FIG. 2, the method includes:
  • the alternate user channel response acquisition module 22 is arranged to perform channel estimation by receiving data for each candidate user. For example, LS channel estimation or MMSE channel estimation is performed to obtain a channel response of each candidate user.
  • the channel response matrix construction module 23 is arranged to construct a channel response matrix for all candidate users using the channel response of each of the candidate users.
  • the channel response matrix constructing module 23 is configured to perform normalization processing on the channel response of each candidate user, and then construct a channel by using a channel response of each candidate user obtained by the normalization process as a column element.
  • the channel response matrix of the alternate user is configured to perform normalization processing on the channel response of each candidate user, and then construct a channel by using a channel response of each candidate user obtained by the normalization process as a column element.
  • the air separation user selection module 24 is configured to perform RRQR decomposition on the channel response matrix, and use the RRQR decomposition result to determine the largest linear independent candidate user that satisfies the requirement of the number of space division users of the base station as the air separation user. That is, the air separation user selection module 24 determines the largest linear independent candidate user by performing RRQR decomposition on the channel response matrix, and among the largest linear independent candidate users, according to the air separation user that satisfies the requirements of the base station. Number, select an alternate user as the air separation user.
  • the space division user selection module 24 is configured to obtain a permutation matrix, a Q matrix, and an R matrix by performing RRQR decomposition on the channel response matrix, and determine a maximum linearity from all candidate users according to the permutation matrix.
  • An independent candidate user may determine the largest linearly independent candidate user by finding the location of the element "1" in each column in the permutation matrix, so as to select an empty space from the maximum linear independent candidate user that satisfies the requirements of the base station.
  • the above channel response matrix is multiplied by a permutation matrix equal to the above Q matrix multiplied by the R matrix.
  • the number of space division users described above may be determined by a base station power requirement and a matrix of shaped weights of the maximum linearly independent candidate users.
  • the shape-weighting matrix of the plurality of candidate users of the maximum linear independence may be determined by a channel response matrix, a Q matrix, and an R matrix of the plurality of candidate users that are linearly independent.
  • the apparatus may further include an shaping weight calculation module 25 configured to utilize the air separation user after determining, by using the RRQR decomposition result, a maximum linear independent candidate user that satisfies the requirement of the number of base station air separation users as the air separation user
  • the channel response matrix, the Q matrix and the R matrix determine the shape weight matrix of the space division user to generate a beam with directivity according to the shape weight matrix.
  • the device selected by the air separation user should be set as a base station.
  • an embodiment of the present application further provides an air separation user selection apparatus, including:
  • processor 50 and a memory 51 storing the processor executable instructions
  • the channel response matrix is subjected to RRQR decomposition, and the RRQR decomposition result is used to determine the largest linear independent candidate user that satisfies the requirement of the number of base station air separation users as the air separation user.
  • the processor 50 normalizes the channel response of each candidate user, and then constructs a channel response of all candidate users by using a channel response of each candidate user obtained by the normalization process as a column element. matrix.
  • the processor 50 performs RRQR decomposition on the channel response matrix to obtain a permutation matrix, a Q matrix, and an R matrix, and the channel response matrix is multiplied by a permutation matrix equal to the Q matrix multiplied by an R matrix; a matrix, determining a plurality of candidate users that are most linearly independent among all candidate users, and determining the maximum linear independent candidate by using a channel response matrix, a Q matrix, and an R matrix of the largest linear independent candidate user a shape weighting matrix of the user, determining, according to the power requirement of the base station and the shaped weight matrix of the maximum linear independent candidate user, the number of air separation users satisfying the requirements of the base station; and linearly independent according to the number of air separation users Among the candidate users, the user is selected as the air separation user.
  • the processor 50 uses the channel response matrix, the Q matrix, and the R of the space division user after determining, by using the RRQR decomposition result, the maximum linear independent candidate user that meets the requirement of the number of base station spatial division users as the air separation user. a matrix, determining a shape weight matrix of the space division user to generate a beam having directivity according to the shape weight matrix.
  • the memory 51 may include a storage program area and a storage data area, and the storage program area may store an operating system and an application required for at least one function.
  • the storage data area can store data and the like created according to the use of the electronic device.
  • the memory 51 may include, for example, a volatile memory of a random access memory, and may also include a nonvolatile memory. For example, at least one disk storage device, flash memory device, or other non-transitory solid state storage device.
  • logic instructions in the memory 51 described above can be implemented in the form of software functional units and sold or used as separate products, the logic instructions can be stored in a computer readable storage medium.
  • the method and apparatus for air separation user selection in FIGS. 1 and 2 can quickly select the air separation user from the candidate users by using the RRQR decomposition result, thereby improving the performance of the air separation system.
  • FIG. 3 is a flow chart of air separation user selection according to an embodiment. The method is applied to a communication system. As shown in FIG. 3, the following five steps are generally included.
  • Step 201 Determine the number of candidate users.
  • the number of alternative users can be determined based on the computing power of the communication system and the real-time traffic conditions.
  • Step 202 Obtain a channel response of each candidate user and perform a normalization process.
  • the received data of each candidate user through the channel can be obtained by the sounding signal.
  • j is the set of alternative users
  • is the norm of h j '.
  • Step 203 The channel response after each candidate user normalization process constructs a channel response matrix for the column elements.
  • the channel response matrix is H
  • the number of antennas is a
  • the number of candidate users is N.
  • the dimension of H is a*N.
  • h j is the jth column vector of H.
  • Step 204 Perform RRQR decomposition on the channel response matrix, and determine the selection of the air separation user according to the intermediate result of the RRQR decomposition.
  • RRQR decomposition algorithms such as QR decomposition of column-selected principals.
  • the M largest linearly independent users among the N candidate users can be determined by the permutation matrix ⁇ .
  • the permutation matrix ⁇ all the elements of the first column are selected, and the position of the element "1" is found therefrom, and the location is prepared.
  • the selected user is determined to be the largest linear independent candidate user, and the algorithm is as follows:
  • SelectUser is the M largest linear independent users selected among N candidate users.
  • the shape matrix B needs to satisfy the following condition: k * trace(B * B) ⁇ P, where k represents a power backoff factor, P represents a base station's transmit power limit, and trace represents a trace of the matrix. It can be set that the power back-off factor k is greater than a certain fixed value k C , and then it is necessary to satisfy trace(B * B) ⁇ P/k C .
  • Trace(B * B) can be calculated by the following formula:
  • trace (B * B) can be calculated.
  • the value of M such that Trace (B * B) does not exceed P / k C maximum integer.
  • a M * is the conjugate transposed matrix of A M
  • C * is the conjugate transposed matrix of C
  • B * is the conjugate transposed matrix of B
  • a M -1 is the inverse matrix of A M
  • (A M *) -1 is an inverse matrix a M *
  • (a M * a M) -1 is a M * a M inverse matrix
  • (C * C) -1 is the inverse matrix C * C.
  • Step 205 Calculate the zero-forced weights by using the RRQR decomposition result.
  • B can be calculated as follows:
  • FIG. 4 is a schematic diagram of an apparatus for air separation user selection according to another embodiment.
  • the apparatus is configured to improve air separation system performance. As shown in FIG. 4, the apparatus is in the alternative user channel response obtaining module 22 and channel of FIG. 2.
  • the response matrix construction module 23, the space division user selection module 24, and the shaping weight calculation module 25 are based on the addition of the candidate user number determination module 21.
  • the alternate number of users determination module 21 is arranged to determine the number of alternate users.
  • the channel response matrix construction module 23 is configured to construct a channel response matrix for the column elements after each candidate user normalization process; the space division user selection module 24 is configured to perform RRQR decomposition on the channel response matrix, according to the RRQR decomposition The intermediate result determines the selection of the air separation user; the shaping weight calculation module 25 calculates the zero-forcing shaping weight using the RRQR decomposition result.
  • the number of candidate users is first determined, and then each candidate user channel response is obtained and normalized, and then the channel response after each candidate user normalization is
  • the column elements construct a channel response matrix, then perform RRQR decomposition on the channel response matrix, determine the selection of the space-division user according to the intermediate result of the RRQR decomposition, and finally calculate the zero-forcing shape weight using the RRQR decomposition result.
  • Step 1 Determine the number of alternate users.
  • the number of candidate users can be determined to be 32 according to the computing power of the communication system and the real-time traffic situation.
  • Step 2 Obtain the channel response of each candidate user and perform normalization processing.
  • the received data of each candidate user through the channel can be obtained by the sounding signal.
  • j is the set of alternative users.
  • Step 3 The channel response after each candidate user normalization process constructs a channel response matrix for the column elements.
  • the channel response matrix is H
  • the number of antennas is 64
  • the number of alternate users is 32.
  • the dimension of H is 64*32.
  • h j is the jth column vector of H.
  • Step 4 Perform RRQR decomposition on the channel response matrix, and determine the selection of the air separation user according to the intermediate result of the RRQR decomposition.
  • the 64*64-dimensional matrix Q is an orthogonal matrix
  • the 32*32-dimensional matrix ⁇ is a permutation matrix
  • the M*M-dimensional matrix A M is an upper triangular matrix
  • E M is an M*(32-M) dimensional matrix
  • F M It is a (64-M)*(32-M) dimensional matrix.
  • RRQR decomposition algorithms such as QR decomposition of column-selected principals.
  • the M largest linearly independent users of the 32 candidate users can be determined by the permutation matrix ⁇ .
  • the algorithm is as follows:
  • SelectUser is the M largest linear independent users selected among 32 candidate users.
  • the shape matrix B needs to satisfy the following condition: k*trace(B * B) ⁇ P, where k represents the power backoff factor, P represents the base station transmit power limit, and trace represents the trace of the matrix. Since the power backoff factor can be greater than a fixed value k C , then trace(B * B) ⁇ P/k C needs to be satisfied.
  • Trace(B * B) can be calculated by the following formula:
  • trace (B * B) can be calculated.
  • the value of M such that Trace (B * B) does not exceed P / k C maximum integer.
  • Step 5 Calculate the zero-forced weights using the RRQR decomposition results.
  • B can be calculated as follows:
  • Step 1 Determine the number of alternate users.
  • the number of candidate users can be determined to be 16 based on the computing power of the communication system and the real-time traffic situation.
  • Step 2 Obtain the channel response of each candidate user and perform normalization processing.
  • the received data of each candidate user through the channel can be obtained by the sounding signal.
  • j is the set of alternative users.
  • Step 3 The channel response after each candidate user normalization process constructs a channel response matrix for the column elements.
  • the channel response matrix is H
  • the number of antennas is 64
  • the number of alternative users is 16.
  • the dimension of H is 64*16.
  • h j is the jth column vector of H.
  • Step 4 Perform RRQR decomposition on the channel response matrix, and determine the selection of the air separation user according to the intermediate result of the RRQR decomposition.
  • the 64*64-dimensional matrix Q is an orthogonal matrix
  • the 16*16-dimensional matrix ⁇ is a permutation matrix
  • the M*M-dimensional matrix A M is an upper triangular matrix
  • E M is an M*(16-M) dimensional matrix
  • F M is a (64-M)*(16-M) dimensional matrix.
  • RRQR decomposition algorithms such as QR decomposition of column-selected principals.
  • SelectUser is the M largest linear independent users selected among 16 candidate users.
  • the shape matrix B needs to satisfy the following condition: k*trace(B * B) ⁇ P, where k represents a power backoff factor, P represents a base station transmit power limit, and trace represents a trace of the matrix. Since the optional power backoff factor is greater than a fixed value k C , then trace(B * B) ⁇ P/k C needs to be satisfied.
  • Trace(B * B) can be calculated as follows:
  • a M is an upper triangular matrix
  • trace(B * B) can be easily calculated.
  • the value of M such that Trace (B * B) does not exceed P / k C maximum integer.
  • Step 5 Calculate the zero-forced weights using the RRQR decomposition results.
  • B can be calculated as follows:
  • the largest linear independent user can be selected from the candidate users, and the algorithm can be obtained with a lower computation amount than the other air separation user selection algorithms.
  • the method and device for space-division user selection use the RRQR algorithm to determine the RRQR decomposition result of the channel response matrix, and determine the space-dividing user that meets the quantity requirement, the calculation amount is low, the operation speed is fast, and the calculation is performed with a lower calculation amount.
  • Excellent selection of air separation users greatly improves the air separation performance of the communication system.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

空分用户选择的方法及装置，所述方法包括：通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应；利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；对所述信道响应矩阵进行示秩正交三角RRQR分解，并利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。

Description

空分用户选择的方法及装置 技术领域
本公开涉及通信技术领域，例如涉及一种空分用户选择的方法及装置。
背景技术
大规模多输入多输出技术以其在频谱效率，能量效率，可靠性和鲁棒性方面的巨大潜在优势，可能成为未来第五代移动通信技术(The 5th Generation Mobile Communication Technology，5G)中具有革命性技术之一。相关技术一般根据用户两两之间的相关性选择空分用户，但是这样不能反映多个用户之间的相关性。采用这种算法可能导致两两用户之间相关性很低，但是多个用户之间的相关性很高，选择的空分用户可能不是最大线性独立的。如果采用遍历的方法从备选用户中选取最大线性独立的用户进行空分，运算量会随着备选用户数目的增加而快速的增加，很难满足实时性要求。
发明内容
本公开提供一种空分用户选择的方法及装置，解决空分用户的选择问题。
本公开提供一种空分用户选择的方法，包括：
通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应；
利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；
对所述信道响应矩阵进行示秩正交三角(Rank-Revealing QR，RRQR)分解，并利用RRQR分解的结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
可选地，所述利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵的步骤包括：
对所述每个备选用户的信道响应进行归一处理，并将归一处理得到的信道响应作为列元素，构造所述所有备选用户的信道响应矩阵。
可选地，所述对所述信道响应矩阵进行RRQR分解，并利用RRQR分解的结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户的步骤包括：
通过对所述信道响应矩阵进行RRQR分解，得到置换矩阵、Q矩阵和R矩阵，其中，所述信道响应矩阵乘以置换矩阵等于所述Q矩阵乘以R矩阵；
根据所述置换矩阵，从所有备选用户中确定最大线性独立的多个备选用户；
按照基站要求的空分用户数目，从所确定的最大线性独立的多个备选用户中选择备选用户作为空分用户。
可选地，所述基站要求的空分用户数目通过以下步骤确定：
利用所述最大线性独立的多个备选用户的信道响应矩阵、Q矩阵和R矩阵，确定所述最大线性独立的多个备选用户的赋形权值矩阵；
根据所述基站的功率要求和所述最大线性独立的多个备选用户的赋形权值矩阵，确定满足所述基站要求的空分用户数目。
可选地，在利用RRQR分解的结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户的步骤之后，利用所述空分用户的信道响应矩阵、Q矩阵和R矩阵，确定所述空分用户的赋形权值矩阵。
本公开还提供一种空分用户选择装置，包括：
备选用户信道响应获取模块，设置为通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应；
信道响应矩阵构造模块，设置为利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；
空分用户选取模块，设置为对所述信道响应矩阵进行示秩正交三角RRQR分解，并利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
可选地，所述空分用户选取模块是设置为通过对所述信道响应矩阵进行RRQR分解，得到置换矩阵、Q矩阵和R矩阵，根据所述置换矩阵，从所有备选用户中确定最大线性独立的多个备选用户，并在所确定的最大线性独立的多个备选用户中，按照基站要求的空分用户数目，选择备选用户作为空分用户，其中，所述信道响应矩阵乘以置换矩阵等于所述Q矩阵乘以R矩阵。
可选地，所述空分用户选取模块还设置为利用所述线性独立的备选用户的信道响应矩阵、Q矩阵和R矩阵，确定所述最大线性独立的多个备选用户的赋形权值矩阵，并根据所述基站的功率要求和所述最大线性独立的多个备选用户的赋形权值矩阵，确定满足所述基站要求的空分用户数目。
可选地，还包括：
赋形权值计算模块，设置为在利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户之后，利用所述空分用户的信道响应矩阵、Q矩阵和R矩阵，确定所述空分用户的赋形权值矩阵。
本公开还提供一种空分用户选择装置，包括：
处理器以及存储有所述处理器可执行指令的存储器；
其中，当所述处理器执行指令时，执行如下操作：
通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应；
利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；
对所述信道响应矩阵进行示秩正交三角RRQR分解，并利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
本公开还提供一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行上述方法。
本公开还提供了一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行上述任意一种方法。
本公开利用RRQR算法对信道响应矩阵的RRQR分解结果，确定满足数量要求的空分用户，运算量低，运算速度快，实现以较低的运算量最优的选取空分用户的目的，极大地提高了通信系统的空分性能。
附图说明
图1是一实施例提供的空分用户选择的方法框图；
图2是一实施例提供的空分用户选择的装置框图；
图3是一实施例提供的空分用户选择的流程图；
图4是一实施例提供的空分用户选择的装置示意图；
图5是一实施例提供的一种空分用户选择的装置的硬件结构示意图。
具体实施方式
在大规模多输入多输出通信系统中，当系统用户很多的时候，很大概率情况下部分用户正交性很好，此时采用迫零预编码算法能以较低的复杂度实现最优的性能。
图1是一实施例提供的空分用户选择的方法框图，如图1所示，该方法包括：
步骤110：通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应。例如最小二乘(Least Square，LS)或最小均方误差(Minimum Mean Square Error，MMSE)信道估计，得到每个备选用户的信道响应。
步骤120：利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵。可选地，对所述每个备选用户的信道响应进行归一处理，然后将归一处理得到的每个备选用户的信道响应作为列元素，构造所有备选用户的信道响应矩阵。
步骤130：对所述信道响应矩阵进行RRQR分解，并利用分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
步骤130包括：通过对所述信道响应矩阵进行RRQR分解，确定最大线性独立的多个备选用户，并在所述最大线性独立的多个备选用户中，按照基站要求的空分用户数目，选择备选用户作为空分用户。通过对所述信道响应矩阵进行RRQR分解，得到置换矩阵、Q矩阵和R矩阵，其中，信道响应矩阵乘以置换矩阵等于所述Q矩阵乘以R矩阵。然后根据所述置换矩阵，从所有备选用户中确定最大线性独立的多个备选用户，可以通过查找置换矩阵中每列中的元素“1”的位置确定最大线性独立的备选用户，以便从所述最大线性独立的备选用户中选取满足基站要求的空分用户数目的空分用户。可选地，空分用户数目可以是由基站的功率要求和所述最大线性独立的备选用户的赋形权值矩阵确定的，所述最大线性独立的备选用户的赋形权值矩阵是由所述最大线性独立的备选用户的信道响应矩阵、Q矩阵和R矩阵确定的。
在选择空分用户之后，还可以利用所述空分用户的信道响应矩阵、Q矩阵和R矩阵，确定所述空分用户的赋形权值矩阵，以便产生具有指向性的波束。
本领域普通技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，所述的程序可以存储于计算机可读取存储介质中，该程序在执行时，包括步骤110至步骤130，或者上述其他空分用户选择方法。其中，所述的存储介质可以为只读存储器(Read-Only Memory，ROM)/随机存取(Random Access Memory，RAM)、磁碟、光盘等。
图2是一实施例提供的空分用户选择装置框图，如图2所示，包括：
备选用户信道响应获取模块22，设置为通过对每个备选用户的接收数据进 行信道估计。例如进行LS信道估计或MMSE信道估计，得到每个备选用户的信道响应。
信道响应矩阵构造模块23，设置为利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵。可选地，信道响应矩阵构造模块23是设置为对所述每个备选用户的信道响应进行归一处理，然后将归一处理得到的每个备选用户的信道响应作为列元素，构造所有备选用户的信道响应矩阵。
空分用户选取模块24，设置为对所述信道响应矩阵进行RRQR分解，并利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。也即是空分用户选取模块24通过对所述信道响应矩阵进行RRQR分解，确定最大线性独立的备选用户，并在所述最大线性独立的备选用户中，按照满足基站要求的空分用户数目，选择备选用户作为空分用户。
可选地，空分用户选取模块24是设置为通过对所述信道响应矩阵进行RRQR分解，得到置换矩阵、Q矩阵和R矩阵，并根据所述置换矩阵，从所有备选用户中确定最大线性独立的备选用户，可以是通过查找置换矩阵中每列中的元素“1”的位置确定最大线性独立的备选用户，以便从所述最大线性独立的备选用户中选取满足基站要求的空分用户数目的空分用户。上述信道响应矩阵乘以置换矩阵等于上述Q矩阵乘以R矩阵。
上述空分用户数目可以是由基站功率要求和所述最大线性独立的备选用户的赋形权值矩阵确定的。上述最大线性独立的多个备选用户的赋形权值矩阵可以是由所述最大线性独立的多个备选用户的信道响应矩阵、Q矩阵和R矩阵确定。
所述装置还可以包括赋形权值计算模块25，设置为在利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户之后，利用所述空分用户的信道响应矩阵、Q矩阵和R矩阵，确定所述空分用户的赋形权值矩阵，以便根据该赋形权值矩阵产生具有指向性的波束。
所述空分用户选择的装置应设置为基站上。
参考图5，本申请实施例还提供了一种空分用户选择装置，包括：
处理器50以及存储有所述处理器可执行指令的存储器51；
其中，当所述处理器50执行指令时，执行如下操作：
通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应；
利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；
对所述信道响应矩阵进行RRQR分解，并利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
可选地，处理器50对所述每个备选用户的信道响应进行归一处理，然后将归一处理得到的每个备选用户的信道响应作为列元素，构造所有备选用户的信道响应矩阵。
可选地，处理器50通过对所述信道响应矩阵进行RRQR分解，得到置换矩阵、Q矩阵和R矩阵，满足信道响应矩阵乘以置换矩阵等于所述Q矩阵乘以R矩阵；根据所述置换矩阵，从所有备选用户中确定最大线性独立的多个备选用户，并利用所述最大线性独立的备选用户的信道响应矩阵、Q矩阵和R矩阵，确定所述最大线性独立的备选用户的赋形权值矩阵，根据基站功率要求和所述最大线性独立的备选用户的赋形权值矩阵，确定满足基站要求的空分用户数目；按照空分用户数目从所述线性独立的备选用户中选取用户作为空分用户。
可选地，处理器50在利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户之后，利用所述空分用户的信道响应矩阵、Q矩阵和R矩阵，确定所述空分用户的赋形权值矩阵，以便根据该赋形权值矩阵产生具有指向性的波束。
存储器51可以包括存储程序区和存储数据区，存储程序区可以存储操作系统和至少一个功能所需的应用程序。存储数据区可以存储根据电子设备的使用所创建的数据等。此外，存储器51可以包括，例如，随机存取存储器的易失性存储器，还可以包括非易失性存储器。例如至少一个磁盘存储器件、闪存器件或者其他非暂态固态存储器件。
此外，在上述存储器51中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时，该逻辑指令可以存储在一个计算机可读取存储介质中。
本领域普通技术人员可理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指示相关的硬件完成的，该程序可存储于一个非暂态计算机可读存储介质中，该程序被执行时，可包括如上述方法的实施例的流程。
采用图1和图2的空分用户选择的方法及装置通过利用RRQR分解结果，可以快速从备选用户中选择空分用户，提高了空分系统的性能。
图3是一实施例提供的空分用户选择的流程图，该方法应用于通信系统中， 如图3所示，一般包括以下五个步骤。
步骤201：确定备选用户数目。
备选用户数目越多，选择正交性较好的用户概率越大，但是系统计算复杂度就越高。备选用户数目越少，选择正交性较好的用户概率越小，但是系统计算复杂度就越低。根据通信系统的运算能力和实时业务情况可以确定备选用户数目。
步骤202：获得每个备选用户的信道响应并进行归一处理。
通过探测信号可以获得每个备选用户经过信道的接收数据。对接收数据进行LS或者MMSE信道估计可以得到备选用户j的信道响应h j’，然后对备选用户j的信道响应h j’进行归一处理得到h j＝h j’/|h j’||。其中j的取值为备选用户集合，|h j’||为h j’的范数。
步骤203：每个备选用户归一处理之后的信道响应为列元素构造信道响应矩阵。
假设信道响应矩阵为H，天线数目为a，备选用户数目为N。那么H的维度为a*N。那么h j为H的第j列向量。
步骤204：对该信道响应矩阵进行RRQR分解，根据RRQR分解的中间结果确定空分用户的选取。
对H进行RRQR分解，得到下述分解结果：
Figure PCTCN2017119585-appb-000001
假设选取的空分用户数目为M。那么其中，Q是a*a维正交矩阵，П是N*N维置换矩阵，
Figure PCTCN2017119585-appb-000002
是R矩阵，A M是M*M维上三角矩阵，E M是M*(N-M)维矩阵，F M是(a-M)*(N-M)维矩阵。RRQR分解算法有很多种类型，比如还可以采用列选主元的QR分解实现。
通过置换矩阵П可以确定N个备选用户中M个最大线性独立的用户，在置换矩阵П中，选取第一列的所有元素，并从中找到元素“1”所在位置，并将该位置的备选用户确定为最大线性独立的备选用户，算法如下：
Location＝1：N
For index＝1：N
column＝П(：，index)；
location index＝find(column＝＝1)；
endfor
SelectUser＝location 1：M
其中SelectUser就是N个备选用户中选取的M个最大线性独立的用户。
M个最大线性独立的用户的信道响应矩阵为列向量构造矩阵C＝Q(1：M，1：M)A M，那么M个最大线性独立的用户的赋形矩阵(即赋形权值矩阵)B＝C(C *C) -1。由于基站有发射功率限制，赋形矩阵B需要满足下列条件：k *trace(B *B)＜P，其中k表示功率回退因子，P表示基站的发射功率限制，trace表示矩阵的迹。可以设定功率回退因子k大于某个固定的数值k C，那么需要满足trace(B *B)＜P/k C。trace(B *B)可以通过如下公式计算：
trace(B *B)＝trace(A M *A M) -1＝trace(A M -1(A M *) -1)
由于A M是上三角矩阵，trace(B *B)可以通过计算得到。M的取值为使得Trace(B *B)不超过P/k C的最大整数。
其中，A M *是A M的共轭转置矩阵，C *是C的共轭转置矩阵，B *是B的共轭转置矩阵，A M -1是A M的逆矩阵，(A M *) -1是A M *的逆矩阵，(A M *A M) -1是A M *A M的逆矩阵，(C *C) -1是C *C的逆矩阵。
步骤205：利用RRQR分解结果计算迫零赋形权值。
M个最大线性独立的用户的信道响应矩阵为列向量构造矩阵C＝Q(1：M，1：M)A M，那么M个最大线性独立的用户的赋形矩阵(即赋形权值矩阵)B＝C(C *C) -1。B可以如下计算得到：
B＝Q(1：M，1：M)A M((Q(1：M，1：M)A M) *Q(1：M，1：M)A M)) -1
＝Q(1：M，1：M)A M(A M *A M) -1
＝Q(1：M，1：M)(A M *) -1
图4是另一实施例提供的空分用户选择的装置示意图，该装置设置为提高空分系统性能，如图4所示，所述装置在图2的备选用户信道响应获得模块22、信道响应矩阵构造模块23、空分用户选取模块24、赋形权值计算模块25基础上，增加备选用户数目确定模块21。备选用户数目确定模块21设置为确定备选用户数目。信道响应矩阵构造模块23设置为以每个备选用户归一处理之后的信道响应为列元素构造信道响应矩阵；空分用户选取模块24设置为对该信道响应矩阵进行RRQR分解，根据RRQR分解的中间结果确定空分用户的选取；赋形 权值计算模块25利用RRQR分解结果计算迫零赋形权值。
采用图3和图4实施例的空分用户选择的方法和装置，可以很大程度的提高通信系统的空分性能。
根据本实施例的空分用户选择的方法和装置，首先确定备选用户数目，然后获得每个备选用户信道响应并进行归一处理，接着每个备选用户归一处理之后的信道响应为列元素构造信道响应矩阵，接着对该信道响应矩阵进行RRQR分解，根据RRQR分解的中间结果确定空分用户的选取，最后利用RRQR分解结果计算迫零赋形权值。
实施例1
假设备选用户数目N＝32，天线数目为64，实施过程如下：
步骤1：确定备选用户数目。
备选用户数目越多，选择正交性很好的用户概率越大，但是系统计算复杂度就越高。备选用户数目越少，选择正交性很好的用户概率越小，但是系统计算复杂度就越低。可以根据通信系统的运算能力和实时业务情况确定备选用户数目为32。
步骤2：获得每个备选用户的信道响应并进行归一处理。
通过探测信号可以获得每个备选用户经过信道的接收数据。对接收数据进行LS或者MMSE信道估计可以得到备选用户j的信道响应h j’，然后对备选用户j的信道响应h j’进行归一处理得到h j＝h j’/||h j’||。其中j的取值为备选用户集合。
步骤3：每个备选用户归一处理之后的信道响应为列元素构造信道响应矩阵。
假设信道响应矩阵为H，天线数目为64，备选用户数目为32。那么H的维度为64*32。那么h j为H的第j列向量。
步骤4：对该信道响应矩阵进行RRQR分解，根据RRQR分解的中间结果确定空分用户的选取。
对H进行RRQR分解得到下述分解结果：
Figure PCTCN2017119585-appb-000003
假设选取的空分用户数目为M。那么其中64*64维矩阵Q是正交矩阵，32*32维矩阵П是置换矩阵，M*M维矩阵A M是上三角矩阵，E M是M*(32-M)维矩阵，F M是(64-M)*(32-M)维矩阵。RRQR分解算法有很多种类型，比如可以采用列选 主元的QR分解实现。
通过置换矩阵П可以确定32个备选用户中M个最大线性独立的用户，算法如下：
Location＝1∶32
For index＝1∶32
column＝П(：，index)；
location index＝find(column＝＝1)；
endfor
SelectUser＝location 1：M
其中SelectUser就是32个备选用户中选取的M个最大线性独立的用户。
M个最大线性独立的用户的信道响应为列向量构造矩阵C＝Q(1：M，1：M)A M，那么M个最大线性独立的用户的赋形矩阵B＝C(C *C) -1。由于基站的有发射功率限制，赋形矩阵B需要满足下列条件：k*trace(B *B)＜P，其中k表示功率回退因子，P表示基站发射功率限制，trace表示矩阵的迹。由于功率回退因子可以大于某个固定的数值k C，那么需要满足trace(B *B)＜P/k C。trace(B *B)可以通过如下公式计算：
trace(B *B)＝trace(A M *A M) -1＝trace(A M -1(A M *) -1)
由于A M是上三角矩阵，trace(B *B)可以通过计算得到。M的取值为使得Trace(B *B)不超过P/k C的最大整数。
步骤5：利用RRQR分解结果计算迫零赋形权值。
M个最大线性独立的用户的信道响应为列向量构造矩阵C＝Q(1：M，1：M)A M，那么M个最大线性独立的用户的赋形矩阵B＝C(C *C) -1。B可以如下计算得到：
B＝Q(1：M，1：M)A M((Q(1：M，1：M)A M) *Q(1：M，1：M)A M)) -1
＝Q(1：M，1：M)A M(A M *A M) -1
＝Q(1：M，1：M)(A M *) -1
实施例2
假设备选用户数目N＝16，天线数目为64，实施过程如下：。
步骤1：确定备选用户数目。
备选用户数目越多，选择正交性很好的用户概率越大，但是系统计算复杂度就越高。备选用户数目越少，选择正交性很好的用户概率越小，但是系统计算复杂度就越低。可以根据通信系统运算能力和实时业务情况确定备选用户数 目为16。
步骤2：获得每个备选用户的信道响应并进行归一处理。
通过探测信号可以获得每个备选用户经过信道的接收数据。对接收数据进行LS或者MMSE信道估计可以得到备选用户j的信道响应h j’，然后对备选用户j的信道响应h j’进行归一处理得到h j＝h j’/||h j’||。其中j的取值为备选用户集合。
步骤3：每个备选用户归一处理之后的信道响应为列元素构造信道响应矩阵。
假设信道响应矩阵为H，天线数目为64，备选用户数目为16。那么H的维度为64*16。那么h j为H的第j列向量。
步骤4：对该信道响应矩阵进行RRQR分解，根据RRQR分解的中间结果确定空分用户的选取。
对H进行RRQR分解，得到下述分解结果：
Figure PCTCN2017119585-appb-000004
假设选取的空分用户数目为M。那么其中，64*64维矩阵Q是正交矩阵，16*16维矩阵П是置换矩阵，M*M维矩阵A M是上三角矩阵，E M是M*(16-M)维矩阵，F M是(64-M)*(16-M)维矩阵。RRQR分解算法有很多种类型，比如可以采用列选主元的QR分解实现。
通过置换矩阵Π可以确定16个备选用户中M个最大线性独立的用户，算法如下：
Location＝1∶16
For index＝1∶16
column＝П(：，index)；
location index＝find(column＝＝1)；
endfor
SelectUser＝location 1：M
其中SelectUser就是16个备选用户中选取的M个最大线性独立的用户。
M个最大线性独立的用户的信道响应为列向量构造矩阵C＝Q(1：M，1：M)A M，那么M个最大线性独立的用户的赋形矩阵B＝C(C *C) -1。由于基站有发射功率限制，赋形矩阵B需要满足下列条件：k*trace(B *B)＜P，其中k表示功率回退因子，P表示基站发射功率限制，trace表示矩阵的迹。由于可选功率回退因子大 于某个固定的数值k C，那么需要满足trace(B *B)＜P/k C。trace(B *B)可以如下计算：
trace(B *B)＝trace(A M *A M) -1＝trace(A M -1(A M *) -1)
由于A M是上三角矩阵，trace(B *B)可以很容易的计算得到。M的取值为使得Trace(B *B)不超过P/k C的最大整数。
步骤5：利用RRQR分解结果计算迫零赋形权值。
M个最大线性独立的用户的信道响应为列向量构造矩阵C＝Q(1：M，1：M)A M，那么M个最大线性独立的用户的赋形矩阵B＝C(C *C) -1。B可以如下计算得到：
B＝Q(1：M，1：M)A M((Q(1：M，1：M)A M) *Q(1：M，1：M)A M)) -1
＝Q(1：M，1：M)A M(A M *A M) -1
＝Q(1：M，1：M)(A M *) -1
本实施例能够从备选用户中选取最大线性独立用户，相对其他空分用户选取算法，能以较低的运算量获得最优的性能。
工业实用性
本公开提供的空分用户选择的方法及装置，利用RRQR算法对信道响应矩阵的RRQR分解结果，确定满足数量要求的空分用户，运算量低，运算速度快，实现以较低的运算量最优的选取空分用户，极大地提高了通信系统的空分性能。

Claims (11)

  1. 一种空分用户选择的方法，包括：
    通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应；
    利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；
    对所述信道响应矩阵进行示秩正交三角RRQR分解，并利用RRQR分解的结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
  2. 根据权利要求1所述的方法，所述利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵的步骤包括：
    对所述每个备选用户的信道响应进行归一处理，并将归一处理得到的信道响应作为列元素，构造所述所有备选用户的信道响应矩阵。
  3. 根据权利要求1所述的方法，所述对所述信道响应矩阵进行RRQR分解，并利用RRQR分解的结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户的步骤包括：
    通过对所述信道响应矩阵进行RRQR分解，得到置换矩阵、Q矩阵和R矩阵，其中，所述信道响应矩阵乘以置换矩阵等于所述Q矩阵乘以R矩阵；
    根据所述置换矩阵，从所有备选用户中确定最大线性独立的多个备选用户；
    按照基站要求的空分用户数目，从所确定的最大线性独立的多个备选用户中选择备选用户作为空分用户。
  4. 根据权利要求3所述的方法，所述基站要求的空分用户数目通过以下步骤确定：
    利用所述最大线性独立的多个备选用户的信道响应矩阵、Q矩阵和R矩阵，确定所述最大线性独立的多个备选用户的赋形权值矩阵；
    根据所述基站的功率要求和所述最大线性独立的多个备选用户的赋形权值矩阵，确定满足所述基站要求的空分用户数目。
  5. 根据权利要求3或4所述的方法，在利用RRQR分解的结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户的步骤之后，利用所述空分用户的信道响应矩阵、Q矩阵和R矩阵，确定所述空分用户的赋形权值矩阵。
  6. 一种空分用户选择装置，包括：
    备选用户信道响应获取模块，设置为通过对每个备选用户的接收数据进行 信道估计，得到每个备选用户的信道响应；
    信道响应矩阵构造模块，设置为利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；
    空分用户选取模块，设置为对所述信道响应矩阵进行示秩正交三角RRQR分解，并利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
  7. 根据权利要求6所述的装置，所述空分用户选取模块是设置为通过对所述信道响应矩阵进行RRQR分解，得到置换矩阵、Q矩阵和R矩阵，根据所述置换矩阵，从所有备选用户中确定最大线性独立的多个备选用户，并在所确定的最大线性独立的多个备选用户中，按照基站要求的空分用户数目，选择备选用户作为空分用户，其中，所述信道响应矩阵乘以置换矩阵等于所述Q矩阵乘以R矩阵。
  8. 根据权利要求7所述的装置，所述空分用户选取模块还设置为利用所述线性独立的备选用户的信道响应矩阵、Q矩阵和R矩阵，确定所述最大线性独立的多个备选用户的赋形权值矩阵，并根据所述基站的功率要求和所述最大线性独立的多个备选用户的赋形权值矩阵，确定满足所述基站要求的空分用户数目。
  9. 根据权利要求7或8所述的装置，还包括：
    赋形权值计算模块，设置为在利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户之后，利用所述空分用户的信道响应矩阵、Q矩阵和R矩阵，确定所述空分用户的赋形权值矩阵。
  10. 一种空分用户选择装置，包括：
    处理器以及存储有所述处理器可执行指令的存储器；
    其中，当所述处理器执行指令时，执行如下操作：
    通过对每个备选用户的接收数据进行信道估计，得到每个备选用户的信道响应；
    利用所述每个备选用户的信道响应，构造所有备选用户的信道响应矩阵；
    对所述信道响应矩阵进行示秩正交三角RRQR分解，并利用RRQR分解结果，确定满足基站空分用户数目要求的最大线性独立的备选用户作为空分用户。
  11. 一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令设置为执行权利要求1-5任一项的方法。
PCT/CN2017/119585 2017-01-06 2017-12-28 空分用户选择的方法及装置 WO2018126990A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2019536942A JP6839290B2 (ja) 2017-01-06 2017-12-28 空間分割ユーザの選択方法および装置
EP17890687.1A EP3567741B1 (en) 2017-01-06 2017-12-28 User selection method and device utilized in spatial division

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710010405.7A CN108282205B (zh) 2017-01-06 2017-01-06 一种空分用户选择的方法及装置
CN201710010405.7 2017-01-06

Publications (1)

Publication Number Publication Date
WO2018126990A1 true WO2018126990A1 (zh) 2018-07-12

Family

ID=62789199

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/119585 WO2018126990A1 (zh) 2017-01-06 2017-12-28 空分用户选择的方法及装置

Country Status (4)

Country Link
EP (1) EP3567741B1 (zh)
JP (1) JP6839290B2 (zh)
CN (1) CN108282205B (zh)
WO (1) WO2018126990A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111479258A (zh) * 2019-01-23 2020-07-31 中国移动通信有限公司研究院 一种用户划分的方法和设备

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060095236A1 (en) * 2004-09-02 2006-05-04 Phillips Joel R Circuit analysis utilizing rank revealing factorization
CN101414874A (zh) * 2007-10-19 2009-04-22 中兴通讯股份有限公司 一种时分双工系统的下行空分多址模式判断方法
CN101834646A (zh) * 2009-03-11 2010-09-15 上海交通大学 用户选择方法、用户选择装置和基站
CN102404026A (zh) * 2011-12-29 2012-04-04 东南大学 一种多输入多输出通信系统中多用户下行传输方法
CN102546488A (zh) * 2011-12-16 2012-07-04 华中科技大学 基于有效信道参数半正交的干扰消除方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101286756B (zh) * 2008-05-29 2012-02-29 上海交通大学 空分多址系统基于最优量化误差码本的方法及装置
US10063290B2 (en) * 2013-03-15 2018-08-28 Interdigital Patent Holdings, Inc. Methods and procedures for non-linear precoding based multiuser multiple input multiple output
US20140334561A1 (en) * 2013-05-13 2014-11-13 Blackberry Limited Method and System for Symbol Detection Using Matrix Decomposition
CN104683268A (zh) * 2013-11-28 2015-06-03 中南大学 基于qr分解的ofdm系统信道估计方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060095236A1 (en) * 2004-09-02 2006-05-04 Phillips Joel R Circuit analysis utilizing rank revealing factorization
CN101414874A (zh) * 2007-10-19 2009-04-22 中兴通讯股份有限公司 一种时分双工系统的下行空分多址模式判断方法
CN101834646A (zh) * 2009-03-11 2010-09-15 上海交通大学 用户选择方法、用户选择装置和基站
CN102546488A (zh) * 2011-12-16 2012-07-04 华中科技大学 基于有效信道参数半正交的干扰消除方法
CN102404026A (zh) * 2011-12-29 2012-04-04 东南大学 一种多输入多输出通信系统中多用户下行传输方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3567741A4 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111479258A (zh) * 2019-01-23 2020-07-31 中国移动通信有限公司研究院 一种用户划分的方法和设备
CN111479258B (zh) * 2019-01-23 2023-03-28 中国移动通信有限公司研究院 一种用户划分的方法和设备

Also Published As

Publication number Publication date
EP3567741A4 (en) 2020-09-30
CN108282205A (zh) 2018-07-13
JP2020505818A (ja) 2020-02-20
EP3567741A1 (en) 2019-11-13
JP6839290B2 (ja) 2021-03-03
EP3567741B1 (en) 2023-01-11
CN108282205B (zh) 2022-02-15

Similar Documents

Publication Publication Date Title
CN107450047B (zh) 嵌套阵下基于未知互耦信息的压缩感知doa估计方法
JP6192010B2 (ja) 重み設定装置および方法
CN105589056B (zh) 一种多目标远近场混合源定位方法
WO2022006919A1 (zh) 基于激活定点拟合的卷积神经网络训练后量化方法及系统
CN109947919A (zh) 用于生成文本匹配模型的方法和装置
EP3298430B1 (en) Direct closed-form covariance matrix and finite alphabet constant-envelope waveforms for planar array beampatterns
WO2023000614A1 (zh) 无线定位参数估计方法、装置、系统、计算机设备及存储介质
CN104793176A (zh) 一种基于fpga的doa估计快速实现方法
Steinwandt et al. Beamspace direction finding based on the conjugate gradient and the auxiliary vector filtering algorithms
Liao et al. Resolution Improvement for MUSIC and ROOT MUSIC Algorithms.
CN112255629A (zh) 基于联合uca阵列的序贯esprit二维不相干分布源参数估计方法
WO2018126990A1 (zh) 空分用户选择的方法及装置
CN107656239A (zh) 一种基于极化敏感阵列的相干信源测向方法
CN112906899B (zh) 基于量子计算的混合大规模mimo到达方向估计方法
CN108614234B (zh) 基于多采样快拍互质阵列接收信号快速傅里叶逆变换的波达方向估计方法
US20130246325A1 (en) Method for classification of newly arrived multidimensional data points in dynamic big data sets
CN115617636B (zh) 一种分布式性能测试系统
Wirtz et al. Distribution of the smallest eigenvalue in complex and real correlated Wishart ensembles
CN110635833B (zh) 一种基于深度学习的功率分配方法及分配装置
Wandale et al. Simulated annealing assisted sparse array selection utilizing deep learning
CN105846826A (zh) 基于近似平滑l0范数的压缩感知信号重构方法
CN106646347A (zh) 基于小生境差分进化的多重信号分类谱峰搜索方法
US11300648B2 (en) High-resolution, accurate, two-dimensional direction-of-arrival estimation method based on coarray tensor spatial spectrum searching with co-prime planar array
CN114185002B (zh) 一种基于波束空间矩阵束的三维参数估计方法
US11830244B2 (en) Image recognition method and apparatus based on systolic array, and medium

Legal Events

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

Ref document number: 17890687

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2019536942

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2017890687

Country of ref document: EP

Effective date: 20190806