CN108934027B - Clustering method of MIMO multi-cell base station caching system - Google Patents

Clustering method of MIMO multi-cell base station caching system Download PDF

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CN108934027B
CN108934027B CN201810722877.XA CN201810722877A CN108934027B CN 108934027 B CN108934027 B CN 108934027B CN 201810722877 A CN201810722877 A CN 201810722877A CN 108934027 B CN108934027 B CN 108934027B
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傅友华
冉君尧
王海荣
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a clustering method of a buffer system of an MIMO multi-cell base station, belonging to the technical field of wireless communication. The method comprises the steps of modeling according to a more practical cacheable base station system, considering a single-user cell downlink model, pre-clustering cells by using a graph division clustering method of soft cluster size constraint, then carrying out a cluster size balancing process according to set judgment conditions, and measuring the cluster quality through an effective rate; and the final clustering scheme is more consistent with the interference alignment feasibility condition. Compared with the prior art, the clustering method provided by the invention can obtain a clustering scheme with more balanced cluster size and better system performance under the condition that the base station can cache the system model which can adapt to higher-speed transmission.

Description

Clustering method of MIMO multi-cell base station caching system
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a clustering method for removing interference in a multi-cell cacheable system.
Background
With the arrival of 5G technology, data transmission is higher and higher, and the factors for limiting the throughput of a multi-cell multi-input multi-output (MIMO) system mainly include two factors of backhaul link capacity and wireless link interference. For the limitation of the capacity of a backhaul link, the base station end can pre-cache contents by designing a proper cache (cache) model, so that the system can reach the reachable rate of a wireless link with a certain probability, and the performance of the system is effectively improved; the limitation of the wireless link is mainly caused by Interference among multiple users, and an Interference Alignment (IA) technique is widely used as an Interference management scheme to eliminate Interference. Implementing IA applications requires Channel State Information (CSI) sharing and the constraint of feasibility conditions to be met. When the number of cells in an IA system exceeds the feasibility limit, not only the interference cannot be eliminated in alignment, but also the CSI overhead is large. Therefore, cluster interference alignment is proposed, interference alignment is carried out in clusters, inter-cluster interference is ignored, and the overall performance of the system is improved.
In the existing Clustering Interference Alignment technology, S J CHEN and R S CHENG, "Clustering for Interference Alignment in Multiuser Interference networkk, "IEEE Transactions on Vehicular Technology,2014, vol.63, No.6, pp.2613-2624. the proposed graph partitioning clustering method for soft cluster size constraints has low complexity and can yield good clustering results. The main steps are to define the cell as the point in the graph, and firstly, the cluster number N is determined by IA feasibility conditionA：
Figure BDA0001718941610000011
Where K is the number of cells, LmaxFor the maximum size of the IA system under the system parameters such as the number of antennas of the current base station and user,
Figure BDA0001718941610000012
the representation takes the upper bound. Defining the effective loss rate between every two cells:
Figure BDA0001718941610000013
where p represents the large scale fading from base station to cell, pji＝rji -α，rjiIs the distance from the base station of the ith cell to the user of the jth cell, alpha is a large-scale fading factor, PjThe base station transmit power of the jth cell. Then pass through
Figure BDA0001718941610000014
Determining the weight of the edge in the graph, solving the eigenvector by the weight matrix to obtain a membership matrix X of each cell belonging to all clusters, and finally dividing the matrix into N by using a K-means clustering methodAAnd (4) clustering. The intra-cluster cell joint design IA sends a precoding matrix and a receiving matrix to achieve intra-cluster interference alignment, so that the average throughput of the system is improved.
However, the graph partitioning and clustering method with the constraint of soft cluster size mainly has the following disadvantages:
1. the scheme does not consider the base station end cache, but the base station cache is a development trend along with the increase of the data transmission rate requirement, and under the condition of limited backhaul link capacity, no clear scheme is provided for aligning the cluster interference of the cache base station system;
2. according to the scheme, clustering is performed by defining the cluster number, but whether the size of each cluster (namely the number of cells contained in the cluster) after clustering strictly meets the IA feasibility condition is uncertain, and the clustering result does not strictly meet the interference alignment feasibility condition possibly.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the prior art, the invention provides a clustering method of a buffer system of an MIMO multi-cell base station, which pre-clusters cells by utilizing a soft cluster size constrained graph partitioning clustering method according to a more practical buffer base station system modeling, and enables a finally obtained clustering scheme to better accord with an interference alignment feasibility condition through a cluster size balancing process.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a clustering method for a buffer system of a MIMO multi-cell base station, comprising the following steps:
step 1, according to the parameters of the system which can be cached by the MIMO multi-cell base station, a pre-clustering scheme is obtained by using a soft cluster size constraint graph division clustering method
Figure BDA0001718941610000021
Wherein
Figure BDA0001718941610000022
Represents the ith cluster, NAObtaining the number of clusters; dividing K single-user subdistricts into NAClustering;
step 2, obtaining the cluster size exceeding the maximum cluster size according to the result of the pre-clustering scheme
Figure BDA0001718941610000023
Set of clusters of
Figure BDA0001718941610000024
The set of clusters smaller than the maximum cluster size is
Figure BDA0001718941610000025
Setting a decision condition for a cluster size balancing procedure
Figure BDA0001718941610000026
And 3, carrying out a cluster size balancing process on the result of the pre-clustering scheme according to the judgment condition to generate a final clustering scheme.
Preferably, in step 1, the following normalized effective loss rates are used in the graph partition clustering for the soft cluster size constraint of the cacheable base station system:
Figure BDA0001718941610000027
wherein, DeltajiRepresenting the effective loss rate, ρ, from cell i to cell jjiRepresents the large-scale fading, ρ, from the base station in cell i to the users in cell jjjRepresents the large-scale fading, P, from the base station in cell j to the users in cell jjIs the transmission power, P, of the base station in cell jiIs the transmission power, P, of the base station in cell ihitProbability of requesting data in cache for user, PmissProbability of requesting data not in cache for user, CdCapacity for data transmission in the backhaul link.
Further, the method further comprises: defining an effective rate c as an evaluation index for judging the quality of the clustering scheme, wherein the evaluation index is in the form of:
Figure BDA0001718941610000031
Figure BDA0001718941610000032
wherein
Figure BDA0001718941610000033
Represents the cluster containing cell j,
Figure BDA0001718941610000034
indicates that cell k is not in cluster
Figure BDA0001718941610000035
In ρjkRepresenting the large scale fading, P, from base station in cell k to user in cell jkRepresenting the transmit power of the base station of cell k,
Figure BDA0001718941610000036
representing a cluster
Figure BDA0001718941610000037
Cluster of a size larger than the maximum cluster size in the clustering scheme
Figure BDA0001718941610000038
The size of (c).
Further, in step 3, a balancing process is performed on the pre-clustering scheme, and the balancing process is performed according to the following steps:
step 31), obtaining a pre-clustering scheme by a soft cluster size clustering algorithm
Figure BDA0001718941610000039
Cluster collection
Figure BDA00017189416100000310
Row normalized membership matrix X*And an effective rate c;
step 32), removing the cluster
Figure BDA00017189416100000311
Corresponding matrix X*Before the a-th column
Figure BDA00017189416100000312
The sequence number of each row with the minimum membership degree is recorded as
Figure BDA00017189416100000313
Cluster
Figure BDA00017189416100000314
The sequence number b is taken out and recorded as a set
Figure BDA00017189416100000315
Will matrix X*In (1)
Figure BDA00017189416100000316
The rows of the image data are, in turn,
Figure BDA00017189416100000317
taking out all intersection values to form a new matrix and recording the new matrix as X degrees;
step 33), judgment
Figure BDA00017189416100000318
Number of elements of both sets, if
Figure BDA00017189416100000319
To the row of X degree of matrix
Figure BDA00017189416100000320
Clustering like K-means to obtain a new clustering scheme
Figure BDA00017189416100000321
And calculates the effective rate c at that time*If c is a*<c, output clustering scheme
Figure BDA00017189416100000322
If c is*C, performing step 35;
step 34) if
Figure BDA00017189416100000323
Taking the column sequence number i corresponding to the maximum value of the jth row in the X degree of the matrix to obtain a new clustering scheme
Figure BDA00017189416100000324
And effective rate c at that time*(ii) a If c is*<c, output clustering scheme
Figure BDA00017189416100000325
If c is*C, performing step 35;
step 35) updating the clustering plan
Figure BDA00017189416100000326
And cluster assembly
Figure BDA00017189416100000327
Let c be c*And returns to step 32.
Has the advantages that: the invention provides a method for designing a pre-clustering scheme by using redefined effective loss rate under the scene of a cacheable base station, then increasing a cluster size balancing process, and enabling the clustering scheme to be more easily in line with interference alignment feasibility conditions on the premise of improving system performance by using the effective rate as an index for measuring the quality of the clustering scheme. Compared with the prior art, the invention can obtain a clustering scheme with more balanced cluster size and better system performance under the system model which can be suitable for higher-speed transmission and can be cached by the base station.
Drawings
Fig. 1 is a flowchart of a clustering method of a MIMO multi-cell base station cacheable system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a cluster size balancing process according to an embodiment of the present invention;
FIG. 3 shows a random cell distribution (2X 2,1) according to an embodiment of the present invention6Clustering effects of the two schemes under the system;
FIG. 4 shows a random cell distribution (2X 2,1) according to an embodiment of the present invention12Clustering effects of the two schemes under the system;
fig. 5 is a graph comparing the effective rates c of the two schemes in the two systems shown in fig. 3 and 4.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
According to the clustering method of the MIMO multi-cell base station caching system, a single-user cell downlink model is considered according to more practical modeling of the caching base station system, cells are pre-clustered by using a soft cluster size constrained clustering interference alignment method, then a cluster size balancing process is carried out, and meanwhile an effective rate index is constructed to measure the cluster quality, so that the final output clustering scheme is more in line with the interference alignment feasibility condition. As shown in fig. 1, in one embodiment, the method comprises the steps of:
step 101: under the model of K MIMO multi-cell cacheable base stations, clustering is carried out on the system by using a soft cluster size constraint graph partitioning method according to the MIMO multi-cell base station cacheable system parameters, and the K single-user cells are divided into NAAnd (4) clustering.
In the clustering process, the normalized effective loss rate as the determining factor of the edge weight matrix is defined as:
Figure BDA0001718941610000041
in the above formula, the denominator is a normalization factor, the first term of the numerator represents the reachable rate of the radio link of the cell j when no other interfering cell exists, the second term represents the reachable rate of the cell j when the other cell i exists, and the subtraction of the two terms represents the loss of the reachable rate caused by the interference of the other cell. Meanwhile, the min [ ] operation in the second term indicates that when the signal-to-noise ratio is low, the wireless link rate cannot reach the limited capacity of the return link, and the system rate is only determined by the smaller wireless link rate; and when the signal-to-noise ratio is high, the wireless link rate is higher than the backhaul link capacity limit, and the system rate is limited by the wireless link rate and the backhaul link capacity together in a probability weighting mode through a buffer.
Wherein ΔjiRepresenting the effective loss rate, ρ, from cell i to cell jjiRepresents the large-scale fading, ρ, from the base station in cell i to the users in cell jjjRepresenting large scale fading from base station in cell j to user in cell j，PjIs the transmission power, P, of the base station in cell jiIs the transmit power of the base station in cell i. The system assumes that a single-user cell is downlink, a transmitting end is a base station, and a receiving end is a user, so that a corner mark ji (default rear is the transmitting end, and front is the receiving end) represents the base station of a cell i and the user of a cell j. CdFor capacity in the backhaul link for data transmission, total backhaul link capacity CbLimited, except for a portion for data transmission and the remainder for CSI sharing denoted as Cc。PhitProbability of requesting data in cache for user, PmissThe probability that the data is not in the cache for the user request can be obtained through a cache model with the content popularity being exponentially distributed, which belongs to the prior art and is not described herein again.
Obtaining a weight matrix from the effective loss rate, then obtaining a membership matrix X of each cell belonging to all clusters by solving eigenvectors, and dividing the matrix into N by using a K-means clustering methodAClustering to obtain a pre-clustering scheme
Figure BDA0001718941610000051
Step 102: using the result of the pre-clustering scheme, the set of clusters whose cluster size exceeds the maximum cluster size is obtained as:
Figure BDA0001718941610000052
the set of clusters smaller than the maximum cluster size is:
Figure BDA0001718941610000053
obtaining a decision condition requiring an increased cluster size balancing process:
Figure BDA0001718941610000054
i.e. there are clusters that exceed the maximum size of the cluster.
Step 103: according to the judgment conditions obtained in the step 102, a cluster size balancing process is added to the pre-clustering scheme, and the effective speed c after the cluster size balancing process is ensured*Is greater than the effective rate c before the balancing process to obtain the final clustering scheme
Figure BDA0001718941610000055
The effective rate calculation formula under the clustering scheme is as follows:
Figure BDA0001718941610000056
Figure BDA0001718941610000057
wherein
Figure BDA0001718941610000058
Represents the cluster containing cell j,
Figure BDA0001718941610000059
indicates that cell k is not in cluster
Figure BDA00017189416100000510
In ρjkRepresenting large scale fading, P, from cell k base station to cell j userskRepresenting the transmit power of the base station of cell k,
Figure BDA00017189416100000511
representing a cluster
Figure BDA00017189416100000512
Cluster of a size larger than the maximum cluster size in the clustering scheme
Figure BDA00017189416100000513
The size of (c). The effective rate c is in the form of a system reachable rate only considering large-scale fading interference alignment, wherein a numerator represents the power of a desired signal, and a first term of a denominator represents normalized noise power; the second term represents a cluster
Figure BDA00017189416100000514
By intra-cluster interference as its rulerWhen the size exceeds the maximum cluster size limit, the intra-cluster interference is the sum of the interference of other cells in the cluster to the cell j, and when the maximum cluster size limit is met, the intra-cluster interference does not exist; the third term is inter-cluster interference, which is expressed as the sum of cell interference powers of other clusters.
And comparing the effective rates before and after the balancing process for one time is to judge whether the balancing rate is acceptable or not, if the effective rate after the balancing is smaller than that before the balancing, discarding the clustering scheme after the balancing, and outputting the clustering scheme before the balancing. Iterate until the updated scheme fails to increase the effective rate. Referring to fig. 2, the step 103 is implemented as follows:
step 301: according to a pre-clustering scheme
Figure BDA0001718941610000061
Obtaining a cluster set
Figure BDA0001718941610000062
Row normalization matrix X of membership matrix X*And the effective rate of the current scheme c;
step 302: removing the cluster
Figure BDA0001718941610000063
Corresponding matrix X*In the a-th column wherein
Figure BDA0001718941610000064
The sequence number of each row with the minimum membership degree is recorded as
Figure BDA0001718941610000065
All clusters are formed
Figure BDA0001718941610000066
The sequence number b is taken out and recorded as a set
Figure BDA0001718941610000067
Will matrix X*In (1)
Figure BDA0001718941610000068
The rows of the image data are, in turn,
Figure BDA0001718941610000069
taking out all intersection values to form a new matrix and recording the new matrix as X degrees;
step 303: judgment of
Figure BDA00017189416100000610
Two set sizes;
step 304: if it is not
Figure BDA00017189416100000611
For matrix X°The rows of (A) are clustered by K-means, into
Figure BDA00017189416100000612
Class, according to this clustering result, will
Figure BDA00017189416100000613
Moving to the cell with medium sequence number
Figure BDA00017189416100000614
The target cluster of medium sequence number. Novel clustering scheme
Figure BDA00017189416100000615
And effective rate c at that time*；
Step 305: if it is not
Figure BDA00017189416100000616
Taking the column number corresponding to the maximum value of the jth row in the X degree matrix
Figure BDA00017189416100000617
And moves cell j to the target cluster
Figure BDA00017189416100000618
Novel clustering scheme
Figure BDA00017189416100000619
And effective rate c at that time*；
Step 306: comparing the effective rate c before balance with the effective rate c after balance*；
Step 307: if c is*>c, receiving the balancing process and updating the clustering scheme
Figure BDA00017189416100000620
Cluster collection
Figure BDA00017189416100000621
And effective rate c ═ c*And re-executing step 302;
step 308: output clustering scheme
Figure BDA00017189416100000622
The experimental results are as follows: FIG. 3 shows 30Km by 30Km (1 unit in the figure is 1Km) (2X 2,1)6The system has 2 receiving and transmitting antennas, 1 transmitting data flow, 6 single user cell clustering results, a coefficient alpha for determining large-scale fading equal to 3.76 and total backhaul link capacity Cb300Mbps, where capacity for data transmission is Cd260Mbps and buffering the determined content hit probability Phit＝Pmiss0.5. Fig. 4 shows a 12-subscriber cell system under the same parameters. The same legends in the figures represent the same cluster, and the two closest same legends represent the base station and the users in the cell. The maximum cluster size limit for both systems is calculated to be
Figure BDA00017189416100000623
It can thus be seen that the cluster size balanced clustering scheme works better in balance and meets the maximum cluster size (implements interference alignment) constraint.
Fig. 5 shows effective rates obtained by respectively implementing the soft cluster size constraint scheme and the cluster size balance scheme by the two systems, and it can be seen that the cluster size balance scheme is significantly better than the soft cluster size constraint scheme under the condition of high signal-to-noise ratio, and the more the number of cells in a certain area is, the better the cluster balancing is (for example, under the condition of 12 cells, the soft cluster size constraint clustering scheme makes some clusters not meet the maximum cluster size constraint, there is intra-cluster interference, and the system performance is very poor), the more excellent the cluster size balance scheme is.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (4)

1. A clustering method of a buffer system of a MIMO multi-cell base station is characterized by comprising the following steps:
step 1, according to the parameters of the system which can be cached by the MIMO multi-cell base station, a pre-clustering scheme is obtained by using a soft cluster size constraint graph division clustering method
Figure FDA0002940157190000011
Wherein
Figure FDA0002940157190000012
Represents the ith cluster, NAObtaining the number of clusters; dividing K single-user subdistricts into NACluster, number of clusters NAThe determination of (2) is as follows:
Figure FDA0002940157190000013
where K is the number of cells, LmaxFor the maximum size of the IA system under current system parameters,
Figure FDA0002940157190000014
representing the upper bound;
step 2, obtaining the cluster size exceeding the maximum cluster size according to the result of the pre-clustering scheme
Figure FDA0002940157190000015
Set of clusters of
Figure FDA0002940157190000016
The set of clusters smaller than the maximum cluster size is
Figure FDA0002940157190000017
Setting a decision condition for a cluster size balancing procedure
Figure FDA0002940157190000018
Step 3, performing a cluster size balancing process on the result of the pre-clustering scheme according to the judgment condition to generate a final clustering scheme, which specifically comprises the following steps:
step 31), obtaining a pre-clustering scheme by a soft cluster size clustering algorithm
Figure FDA0002940157190000019
Cluster collection
Figure FDA00029401571900000110
Row normalized membership matrix X*And effective rate
Figure FDA00029401571900000111
Step 32), removing the cluster
Figure FDA00029401571900000112
Corresponding matrix X*Before the a-th column
Figure FDA00029401571900000113
The sequence number of each row with the minimum membership degree is recorded as
Figure FDA00029401571900000114
Cluster
Figure FDA00029401571900000115
The sequence number b is taken out and recorded as a set
Figure FDA00029401571900000116
Will matrix X*In (1)
Figure FDA00029401571900000117
The rows of the image data are, in turn,
Figure FDA00029401571900000118
taking out all intersection values to form a new matrix and recording the new matrix as X degrees;
step 33), judgment
Figure FDA00029401571900000119
Number of elements of both sets, if
Figure FDA00029401571900000120
To the row of X degree of matrix
Figure FDA00029401571900000121
Clustering like K-means to obtain a new clustering scheme
Figure FDA00029401571900000122
And calculates the effective rate at that time
Figure FDA00029401571900000123
If it is not
Figure FDA00029401571900000124
Output clustering scheme
Figure FDA00029401571900000125
If it is not
Figure FDA00029401571900000126
Performing step 35;
step 34) if
Figure FDA00029401571900000127
Taking the column sequence number i corresponding to the maximum value of the jth row in the X degree of the matrix to obtain a new clustering scheme
Figure FDA00029401571900000128
And effective rate at that time
Figure FDA00029401571900000129
If it is not
Figure FDA00029401571900000130
Output clustering scheme
Figure FDA00029401571900000131
If it is not
Figure FDA00029401571900000132
Performing step 35;
step 35) updating the clustering plan
Figure FDA00029401571900000133
And cluster assembly
Figure FDA00029401571900000134
Order to
Figure FDA00029401571900000135
Returning to step 32.
2. The method for clustering a buffer system of a MIMO multi-cell base station according to claim 1, wherein in the step 1, for the graph of soft cluster size constraint of the buffer system, the following normalized effective loss rate is used:
Figure FDA0002940157190000021
wherein, DeltajiRepresenting the effective loss rate, ρ, from cell i to cell jjiRepresents the large-scale fading, ρ, from the base station in cell i to the users in cell jjjRepresents the large-scale fading, P, from the base station in cell j to the users in cell jjIs the transmission power, P, of cell j base stationiFor the transmission power of cell i base station, PhitProbability of requesting data in cache for user, PmissProbability of requesting data not in cache for user, CdCapacity for data transmission in the backhaul link.
3. The method of claim 1, wherein the method further comprises: defining an effective rate c as an evaluation index for judging the quality of the clustering scheme, wherein the evaluation index is in the form of:
Figure FDA0002940157190000022
Figure FDA0002940157190000023
wherein
Figure FDA0002940157190000024
Represents the cluster containing cell j,
Figure FDA0002940157190000025
indicates that cell k is not in cluster
Figure FDA0002940157190000026
In ρjkRepresenting the large scale fading, ρ, from cell k base station to cell j usersjjRepresents the large-scale fading, rho, from the base station in cell j to the users in cell jjiRepresenting the large scale fading, P, from base station in cell i to user in cell jjIs the transmission power, P, of cell j base stationiFor the transmission power of cell i base station, PkRepresenting the transmit power of the base station of cell k,
Figure FDA0002940157190000027
representing a cluster
Figure FDA0002940157190000028
Cluster of a size larger than the maximum cluster size in the clustering scheme
Figure FDA0002940157190000029
The size of (c).
4. The clustering method of the buffer system of the MIMO multi-cell base station according to claim 3, wherein when the balancing process is performed to the pre-clustering scheme in the step 3, the effective rates before and after each balancing process are compared, and if the effective rate after balancing is greater than the effective rate before balancing, the clustering scheme after this balancing is output; if the effective rate after the balance is less than or equal to the effective rate before the balance, the clustering scheme after the balance is abandoned, and the clustering scheme before the balance is output.
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