CN109728891B - Clustering-based user pairing method in MU-MIMO system - Google Patents

Clustering-based user pairing method in MU-MIMO system Download PDF

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CN109728891B
CN109728891B CN201811600741.8A CN201811600741A CN109728891B CN 109728891 B CN109728891 B CN 109728891B CN 201811600741 A CN201811600741 A CN 201811600741A CN 109728891 B CN109728891 B CN 109728891B
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resource block
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CN109728891A (en
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卢小峰
赵丹萍
樊思涵
刘欢
郭惠
范宁
杨鲲
张海林
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Xidian University
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Abstract

The invention discloses a user pairing method based on clustering in an MU-MIMO system, which mainly solves the problems of low spectrum utilization rate and high solving complexity in the prior art. The technical scheme is as follows: 1. generating a resource block splitting complete set by using a splitting method; 2. determining the number and the center of the good classes in each resource block splitting set; 3. initializing user groups and resource block groups in each class; 4. and continuously executing user moving-out operation and user adding operation in each class until all the users are classified into the classes, and finishing the user pairing process. On the basis of considering the bit error rate constraint of the system, the minimum mean square error-sequencing-based continuous interference cancellation MMSE-OSIC technology is adopted at the receiving end, so that the dynamic multi-user pairing can be efficiently carried out, and the frequency spectrum utilization rate of the communication system can be maximized under the condition of meeting the communication quality requirement of the system. The method can be used for pairing the mobile phone users in the MU-MIMO system.

Description

Clustering-based user pairing method in MU-MIMO system
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a user pairing method based on clustering, which can be used for a multi-user-multi-input multi-output-single carrier-frequency division multiple access MU-MIMO-SC-FDMA system.
Background
In the 4G mobile communication system and the 5G mobile communication system in the coming future, the multi-user-multiple-input multiple-output MU-MIMO technology plays an important role. The MU-MIMO technology combines a plurality of user groups only provided with 1 antenna into a user pair at a transmitting end through a reasonable user pairing rule, the user pair is virtually regarded as an entity terminal provided with a plurality of antennas, and the entity terminal and a plurality of antennas assembled at a receiving end base station side form an MU-MIMO array together, so that the system spectrum efficiency is remarkably improved. In an MU-MIMO system, the most critical research problem is to select a proper user pairing method, and the main measure for evaluating the quality of a user pairing method is to see the index of the frequency spectrum utilization rate of the system.
The current classical user pairing algorithm is as follows: random pairing criteria, orthogonal user pairing algorithm, determinant pairing criteria and orthogonal defect degree pairing criteria. These basic user pairing algorithms, although ensuring as little interference as possible between users to some extent, do not significantly improve the spectrum utilization of the system.
In the paper "Dynamic User Grouping and Joint Resource Allocation With Multi-Cell coordination for Uplink Virtual MIMO Systems" (IEEE Transactions on Wireless Communications, vol.16, No.6, pp.3854-3869, Jun 2017.) published by XiaOfeng Lu et al, an iterative Hungarian method is proposed for the User pairing problem in MU-MIMO Systems. The method comprises the following specific steps: firstly, grouping users to generate a complete user grouping set; grouping the resource blocks to generate a complete resource block grouping set; and finally, obtaining the optimal combination condition between the user group and the resource block group and the system throughput by using an iterative Hungarian algorithm. The method has the disadvantages of higher complexity and lower realized spectrum efficiency.
Disclosure of Invention
The present invention is directed to provide a clustering-based user pairing method in an MU-MIMO system to reduce complexity and improve the spectrum utilization of the system, in view of the above-mentioned deficiencies of the prior art.
In order to achieve the purpose, the technical scheme comprises the following steps:
(1) generating a complete split set of resource blocks:
splitting all resource blocks in the system by using a splitting method to generate a complete resource block splitting set, wherein the complete resource block splitting set comprises a plurality of resource block splitting sets, and each resource block splitting set comprises a plurality of resource block groups;
(2) generating cluster number and initializing resource block group and user in each cluster:
defining the number of resource block groups in each resource block splitting set as the number K of classes, and defining the resource block groups in the K classes as T in turn1,…,Tm,…,TKSequentially putting each resource block group in the T1,…,Tm,…,TKIn (3), sequentially defining the user groups in K classes as omega1,…,Ωm,…,ΩKDefining the total number of users as L, selecting K users from the L users in sequence and putting the K users into omega1,…,Ωm,…,ΩKPerforming the following steps;
(3) calculating the center Z of each class according to the minimum mean square error-ordering-based continuous interference cancellation MMSE-OSIC detection method and the bit error rate constraint adaptive modulation method1,…,Zm,…,ZK；
(4) And executing user removal operation:
(4a) setting the number of users moved out of a class each time as eta;
(4b) selecting a class from the unselected classes, judging whether the number of users in the class is greater than eta, if so, executing (4c), otherwise, executing (4 e);
(4c) the user group omega in the mth classmResource block group T occupied by user umIs defined as a transmission efficiency of
Figure BDA0001922424800000023
Calculating the distance from each user in the cluster to the cluster center as
Figure BDA0001922424800000021
Then, the user with the minimum distance to the center is moved out of the class, and the center Z of each class is updated according to (3)1,…,Zm,…,ZK；
(4d) Repeating (4c) until η users are moved out of the class;
(4e) repeating (4b) - (4d) until all K classes are selected;
(5) and executing user adding operation:
(5a) setting the number of users added to the class each time as lambda;
(5b) selecting one user from the users to be added, and calculating the distance from the user to all the cluster centers to obtain
Figure BDA0001922424800000022
Adding the user to the class corresponding to the clustering center with the minimum distance;
(5c) repeating (5b) until λ users are all added to the class;
(5d) updating the center Z of each class according to (3)1,…,Zm,…,ZK；
(6) And (5) repeating the steps (4) and (5) until all L users are allocated to the class, and finishing the user pairing process.
Compared with the prior art, the invention has the following advantages:
firstly, because the invention adopts the method of dynamically pairing the users in multiple cells and allocating resources according to the channel state under the condition of giving the bit error rate threshold of the system, the problem that the communication quality of the system can not be ensured in the resource allocation process in the prior art is solved, the invention can ensure that the bit error rate of the system is below the threshold value while maximizing the frequency utilization rate of the system, and further improve the communication quality of the system;
secondly, the invention adopts a user clustering algorithm based on minimum mean square error-sequencing serial interference cancellation (MMSE) -OSIC technology to decompose the big problem of joint user pairing and resource allocation into a plurality of sub-problems with small scale to solve in parallel, thereby greatly reducing the search space for solving the problem, overcoming the problems that the prior art can not fully utilize the frequency spectrum and has high solving complexity, and improving the frequency spectrum efficiency while reducing the complexity.
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FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a complexity simulation result of the method of the present invention and a prior art method;
fig. 3 shows the results of the simulation of the spectral efficiency of the method of the present invention and the prior art method.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings.
Referring to fig. 1, the implementation steps of the invention are as follows:
step 1, generating a complete split resource block set.
Setting the number of resource blocks in a multi-user-multi-input multi-output-single carrier-frequency division multiple access MU-MIMO-SC-FDMA system as n, splitting the resource blocks by using a splitting method to generate a complete split resource block set, and specifically comprising the following steps:
(1a) all consecutive resource blocks in a MU-MIMO-SC-FDMA system are denoted as { B }1,B2,…,Bi,Bi+1,…,BnSequence of which B isiRepresents the ith resource block, i is from 1 to n, n represents the total number of resource blocks;
(1b) the resource block sequence is separated by n-1 transverse lines to obtain a separated resource block sequence { B1,_,B2,_,…,Bi,_,Bi+1,…,_,Bn}；
(1c) Randomly inserting digit 0 or digit 1 on n-1 horizontal lines, i.e. decimal numbers 1-2n-1Are all converted into binary sequences of length n-1, 2n-1The binary sequence is inserted into n-1 horizontal lines in sequence to obtain 2n-1Splitting and collecting the seed resource blocks;
(1d) in each resource block split set, judge BiAnd Bi+1The number inserted on the horizontal line between is 0, or 1: if the number is 0, then B is usediAnd Bi+1Forming a resource block group;
if it is the number 1, then B is usediAnd Bi+1Forming two different resource block groups;
(1e) will 2n-1The seed resource blocks are divided into a set to generate resourcesAnd splitting the blocks into complete sets.
And 2, generating the number of clusters and initializing the resource block group and the user group in each cluster.
(2a) Selecting a resource block splitting set from the resource block splitting complete set, and defining the number of resource block groups in the resource block splitting set as the number K of classes;
(2b) sequentially defining K resource block groups in classes as T1,…,Tm,…,TKM is from 1 to K, and the resource blocks in the resource block division are sequentially put into T1,…,Tm,…,TKPerforming the following steps;
(2c) defining the user groups in K classes as omega1,…,Ωm,…,ΩKDefining the total number of users as L, selecting K users from the L users in sequence and putting the K users into omega1,…,Ωm,…,ΩKIn (1).
Step 3, calculating the center of each class:
calculating the center Z of each class according to the existing minimum mean square error-sequencing-based continuous interference cancellation MMSE-OSIC detection method and the adaptive modulation method of bit error rate constraint1,…,Zm,…,ZKThe method comprises the following specific steps:
(3a) calculate the mth user group ΩmThe signal-to-interference-and-noise ratio of the e-th subcarrier occupied by the user u is as follows:
Figure BDA0001922424800000041
wherein e is from 1 to | Tm|，|TmI is resource block group TmThe number of subcarriers, u, is from 1 to | Ωm|，|Ωm| is the user group ΩmThe number of users is the number of users,
Figure BDA0001922424800000042
is a user group omegamThe detected vector of user u on the e-th subcarrier,
Figure BDA0001922424800000043
is a user group omegamAnd the vector, σ, corresponding to the u-th column of the channel matrix between the e-th subcarriers2Is the gaussian noise variance;
(3b) calculate the mth user group ΩmThe transmission efficiency of the user u occupying the e-th subcarrier is as follows:
Figure BDA0001922424800000044
wherein,
Figure BDA0001922424800000045
is a downward cut operation, BERtarIs the upper bound of the BER constraint for signal transmission;
(3c) calculate the mth user group ΩmResource block group T occupied by user umThe transmission efficiency of (a) is:
Figure BDA0001922424800000046
(3d) calculate the mth user group ΩmOccupied resource block group TmThe transmission efficiency of (a) is:
Figure BDA0001922424800000051
(3e) the center of the mth class is calculated as:
Zm＝Rm。
and 4, executing user moving-out operation:
(4a) setting the number of users moved out of a class each time as eta;
(4b) selecting a class from the unselected classes, judging whether the number of users in the class is greater than eta, if so, executing (4c), otherwise, executing (4 e);
(4c) the user group omega in the mth classmResource block group T occupied by user umIs defined as a transmission efficiency of
Figure BDA0001922424800000052
Calculating the distance from each user in the cluster to the cluster center as
Figure BDA0001922424800000053
Then, the user with the minimum distance to the center is moved out of the class, and the center Z of each class is updated according to (3)1,…,Zm,…,ZK；
(4d) Repeating (4c) until η users are moved out of the class;
(4e) repeating (4b) - (4d) until all K classes are selected.
And step 5, executing user adding operation:
(5a) setting the number of users added to the class each time as lambda;
(5b) selecting one user from the users to be added, and calculating the distance from the user to all the cluster centers to obtain
Figure BDA0001922424800000054
The method comprises the following specific steps:
(5b1) selecting one user from the users to be added, and adding the user into the mth category to obtain a user group of
Figure BDA0001922424800000055
(5b2) Computing the mth user group
Figure BDA0001922424800000056
The transmission efficiency of the user d occupying the e-th subcarrier is as follows:
Figure BDA0001922424800000057
wherein,
Figure BDA0001922424800000058
is a downward cut-off operation, d is from 1 to
Figure BDA0001922424800000059
Figure BDA00019224248000000510
Is a group of users
Figure BDA00019224248000000511
Number of users, SINR (d)e,m+Is a group of users
Figure BDA00019224248000000512
Signal to interference plus noise ratio (BER) of the e-th subcarrier occupied by user dtarIs the upper bound of the BER constraint for signal transmission;
(5b3) computing the mth user group
Figure BDA00019224248000000513
Resource block group T occupied by user dmThe transmission efficiency of (a) is:
Figure BDA00019224248000000514
wherein,
Figure BDA0001922424800000061
is a group of users
Figure BDA0001922424800000062
The transmission efficiency of the e sub-carrier occupied by the user d;
(5b4) computing the mth user group
Figure BDA0001922424800000063
Occupied resource block group TmThe transmission efficiency of (a) is:
Figure BDA0001922424800000064
(5b5) calculating the distance from the selected user to the mth clustering center as follows:
Figure BDA0001922424800000065
wherein Z ismIs the center of the mth class.
(5c) Adding the user to the class corresponding to the clustering center with the minimum distance;
(5d) repeating (5b) - (5c) until all λ users are added to the class;
(5e) updating the center Z of each class according to (3)1,…,Zm,…,ZK。
And 6, completing the user pairing process:
and (5) repeating the steps (4) and (5) until all L users are allocated to the class, and finishing the user pairing process.
The effects of the invention can be further illustrated by simulation
1. Simulation conditions are as follows:
the simulation of the invention is carried out in the wireless communication scene of a single base station, the number of resource blocks is 6, and the threshold value of the bit error rate of the system is 10-5And the simulation experiment of the invention sets the detection mode of the signal receiver as minimum mean square error-MMSE-OSIC detection based on sequencing continuous interference cancellation, and assumes that the channel matrix is invariable in a single time slot. The performances of the existing user pairing technology and the method of the invention in the aspects of complexity and system spectrum efficiency are compared.
2. Simulation content and result analysis
Simulation 1, according to the above simulation conditions, the complexity of the method of the present invention and the complexity of the existing method are simulated, and the result is shown in fig. 2. As can be seen from fig. 2, when the number of users is 10, 20, 30, and 40, the method of the present invention has a lower computational complexity.
Simulation 2, according to the simulation conditions, the spectrum efficiency of the method of the present invention and the existing method is simulated, and the result is shown in fig. 3.
It can be seen from fig. 3 that the spectral efficiency of the present invention method and the existing method increases with the increasing of the snr, but the slope of the spectral efficiency curve of the present invention method is significantly larger than that of the existing method, and the performance curve of the present invention method is always above the existing method no matter what the snr is high or low. Therefore, the user pairing method can fully utilize the frequency spectrum resources in the system and improve the frequency spectrum efficiency.

Claims (3)

1. A user pairing method based on clustering in an MU-MIMO system is characterized by comprising the following steps:
(1) generating a complete split set of resource blocks:
splitting all resource blocks in the system by using a splitting method to generate a complete splitting set of the resource blocks, wherein the method is realized as follows:
(1a) all consecutive resource blocks in a MU-MIMO-SC-FDMA system are denoted as { B }1,B2,···,Bi,Bi+1,···,BnSequence of which B isiRepresents the ith resource block, and n represents the total number of resource blocks;
(1b) the resource block sequence is separated by n-1 transverse lines to obtain a separated resource block sequence { B1,_,B2,_,···,Bi,_,Bi+1,···,_,Bn}；
(1c) Randomly inserting digit 0 or digit 1 on n-1 horizontal lines, i.e. decimal numbers 1-2n-1Are all converted into binary sequences of length n-1, 2n-1The binary sequence is inserted into n-1 horizontal lines in sequence to obtain 2n-1Splitting and collecting the seed resource blocks;
(1d) in each resource block split set, judge BiAnd Bi+1The number inserted on the horizontal line between them is 0 or 1, and if it is 0, BiAnd Bi+1Form a resource block group, if the number is 1, BiAnd Bi+1Forming two different resource block groups;
(1e) will 2n-1The seed resource block split sets form a set, and a complete resource block split set is generated;
the resource block complete splitting set comprises a plurality of resource block splitting sets, and each resource block splitting set comprises a plurality of resource block groups;
(2) generating cluster number and initializing resource block group and user in each cluster:
defining the number of resource block groups in each resource block splitting set as the number K of classes, and defining the resource block groups in the K classes as T in turn1,···,Tm,···,TKSequentially putting each resource block group in the T1,···,Tm,···,TKIn (3), sequentially defining the user groups in K classes as omega1,···,Ωm,···,ΩKDefining the total number of users as L, selecting K users from the L users in sequence and putting the K users into omega1,···,Ωm,···,ΩKPerforming the following steps;
(3) calculating the center Z of each class according to the minimum mean square error-ordering-based continuous interference cancellation MMSE-OSIC detection method and the bit error rate constraint adaptive modulation method1,···,Zm,···,ZK；
(4) And executing user removal operation:
(4a) setting the number of users moved out of a class each time as eta;
(4b) selecting a class from the unselected classes, judging whether the number of users in the class is greater than eta, if so, executing (4c), otherwise, executing (4 e);
(4c) the user group omega in the mth classmResource block group T occupied by user umIs defined as a transmission efficiency of
Figure FDA0002970726400000021
Calculating the distance from each user in the cluster to the cluster center as
Figure FDA0002970726400000022
Then, the user with the minimum distance to the center is moved out of the class, and the center Z of each class is updated according to (3)1,···,Zm,···,ZK；
(4d) Repeating (4c) until η users are moved out of the class;
(4e) repeating (4b) - (4d) until all K classes are selected;
(5) and executing user adding operation:
(5a) setting the number of users added to the class each time as lambda;
(5b) selecting one user from the users to be added, and calculating the distance from the user to all the cluster centers to obtain
Figure FDA0002970726400000023
Adding the user to the class corresponding to the clustering center with the minimum distance;
(5c) repeating (5b) until λ users are all added to the class;
(5d) updating the center Z of each class according to (3)1,···,Zm,···,ZK；
(6) And (5) repeating the steps (4) and (5) until all L users are allocated to the class, and finishing the user pairing process.
2. The method of claim 1, wherein the step (3) calculates the center of each class according to the minimum mean square error-ordering-based successive interference cancellation (MMSE) -OSIC detection method and the bit error rate constrained adaptive modulation method, and the method is implemented as follows:
(3a) computing user group ΩmThe signal-to-interference-and-noise ratio of the sub-carrier e occupied by the user u is as follows:
Figure FDA0002970726400000024
wherein,
Figure FDA0002970726400000025
is a user group omegamThe detected vector of user u on the e-th subcarrier,
Figure FDA0002970726400000026
is a user group omegamAnd the vector corresponding to the u-th column of the channel matrix between the e-th subcarriers, | Ωm| is the user group ΩmNumber of users, σ2Is the gaussian noise variance;
(3b) computing user group ΩmThe transmission efficiency of the user u occupying the e-th subcarrier is as follows:
Figure FDA0002970726400000031
wherein,
Figure FDA0002970726400000032
is a downward cut operation, BERtarIs the upper bound of the BER constraint for signal transmission;
(3c) computing user group ΩmResource block group T occupied by user umThe transmission efficiency of (a) is:
Figure FDA0002970726400000033
(3d) computing user group ΩmOccupied resource block group TmThe transmission efficiency of (a) is:
Figure FDA0002970726400000034
(3e) the center of the mth class is calculated as:
Zm＝Rm。
3. the method of claim 1, wherein in step (5b), one user is selected from the users to be added, and the distances from the user to all cluster centers are calculated as follows:
(5b1) selecting one user from the users to be added, and adding the user into the mth category to obtain a user group of
Figure FDA0002970726400000035
(5b2) Computing user groups
Figure FDA0002970726400000036
The transmission efficiency of the user u occupying the e-th subcarrier is as follows:
Figure FDA0002970726400000037
wherein,
Figure FDA0002970726400000038
is a downward trade-off operation, SINR (u)e,m+Is a group of users
Figure FDA0002970726400000039
Signal to interference plus noise ratio (BER) of the e-th subcarrier occupied by user utarIs the upper bound of the BER constraint for signal transmission;
(5b3) computing user groups
Figure FDA00029707264000000310
Resource block group T occupied by user umThe transmission efficiency of (a) is:
Figure FDA00029707264000000311
wherein,
Figure FDA00029707264000000312
is a group of users
Figure FDA00029707264000000313
The transmission efficiency of the e sub-carrier occupied by the user u;
(5b4) computing user groups
Figure FDA00029707264000000314
Occupied resource block group TmThe transmission efficiency of (a) is:
Figure FDA00029707264000000315
(5b5) calculating the distance from the selected user to the mth clustering center as follows:
Figure FDA0002970726400000041
wherein Z ismIs the center of the mth class.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101958770A (en) * 2010-08-30 2011-01-26 北京邮电大学 Uplink multiuser multi-access interference elimination method and device of multi-input multi-output (MIMO) system
CN103024920A (en) * 2012-12-21 2013-04-03 中国科学技术大学 Method for pairing users in virtual multiple-input-multiple-output system
CN106793120A (en) * 2016-12-23 2017-05-31 西安电子科技大学 Fast joint resource allocation methods based on partition number in virtual MIMO system
CN108282888A (en) * 2018-03-05 2018-07-13 吉林大学 A kind of D2D resource allocation methods based on improvement fuzzy clustering

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10045352B2 (en) * 2016-01-13 2018-08-07 Futurewei Technologies, Inc. Channel estimation in large scale MIMO systems using iterative location based spatial filtering
US10374772B2 (en) * 2017-01-25 2019-08-06 Georgia Tech Research Corporation Method for slicing K-best detection in multiple-input multiple-output wireless communications system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101958770A (en) * 2010-08-30 2011-01-26 北京邮电大学 Uplink multiuser multi-access interference elimination method and device of multi-input multi-output (MIMO) system
CN103024920A (en) * 2012-12-21 2013-04-03 中国科学技术大学 Method for pairing users in virtual multiple-input-multiple-output system
CN106793120A (en) * 2016-12-23 2017-05-31 西安电子科技大学 Fast joint resource allocation methods based on partition number in virtual MIMO system
CN108282888A (en) * 2018-03-05 2018-07-13 吉林大学 A kind of D2D resource allocation methods based on improvement fuzzy clustering

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
LTE上行虚拟MIMO用户配对算法研究;马松;《信息科技辑》;20160331;全文 *
Soft-output MMSE OSIC MIMO detector with reduced-complexity approximations;Jun Wang;《2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications》;20071212;全文 *

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