CN110177340B - User-centered ultra-dense network resource allocation method - Google Patents

User-centered ultra-dense network resource allocation method Download PDF

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CN110177340B
CN110177340B CN201910634521.5A CN201910634521A CN110177340B CN 110177340 B CN110177340 B CN 110177340B CN 201910634521 A CN201910634521 A CN 201910634521A CN 110177340 B CN110177340 B CN 110177340B
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users
rrh
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rrhs
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CN110177340A (en
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黄晓燕
沈秋彤
吴凡
冷甦鹏
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/046—Wireless resource allocation based on the type of the allocated resource the resource being in the space domain, e.g. beams
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/56—Allocation or scheduling criteria for wireless resources based on priority criteria

Abstract

The invention discloses a user-centered ultra-dense network resource allocation method, which comprises the following steps: s1, distributing the RRHs by adopting a bidirectional selection overlapping RRH clustering algorithm based on the geographic position; s2, adopting a system subcarrier allocation algorithm based on priority to allocate subcarriers to all RRHs; and S3, distributing the minimum rate lower limit constraint of each user to the subcarriers according to the distribution result design of the previous two steps, and solving the power distribution and the beam forming vector by using a WSMSE minimization method. The invention adopts a method with lower complexity to realize RRH allocation and subcarrier allocation to each user in the system, distributes rate constraint to each subcarrier, simultaneously improves the resource utilization rate to the maximum extent, reduces the interference among users and can effectively improve the transmission efficiency of the system.

Description

User-centered ultra-dense network resource allocation method
Technical Field
The invention belongs to the field of 5G wireless communication, and particularly relates to a user-centered ultra-dense network resource allocation method.
Background
In recent years, mobile data services have become the dominant service of wireless communication networks and continue to evolve rapidly at an unexpected rate. As the amount of mobile data continues to rise, mobile network operators must increase network capacity in order to meet the ever-increasing traffic demands of users. The MIMO technology can serve multiple users and provide multiplexing gain at the same time by introducing additional spatial dimensions, and can effectively improve the spectral efficiency of the system. In a new generation of wireless communication systems, a combination of technologies has become a trend. It should be noted that the combination and application of these new technologies make the network structure and interference environment of the next generation wireless network become very complicated, and the interference problem becomes more severe, which becomes a key factor restricting the performance improvement of the system.
An Ultra Dense Network (UDN) is one of the key technologies of 5G mobile communication, and increases the deployment density of low-power sites to make the Network dense, so that nodes are closer to users, thereby improving the system capacity and improving the spectrum efficiency and power efficiency. Due to the reduction of the node distance, the transmission loss difference of adjacent nodes is not large, and a plurality of interference sources with similar strength are possible around a user, so that the user is subjected to more serious interference than that of a traditional cellular network. How to solve the performance loss caused by multiple interference sources and how to improve the user performance by using network coordination and interference management become key problems in the research of 5G UDN interference suppression.
In conclusion, the interference coordination technology based on the dynamic cooperation area of the UDN architecture is of great practical value, and can meet the increasing traffic demand of non-uniformly distributed users. However, the research and use of the related art are still in the initial stage, how to combine different network environment characteristics to meet the requirements of users, and a better antenna and subcarrier allocation and beamforming vector design scheme is provided, which is still a question worth of discussion.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a user-centered ultra-dense network resource allocation method which adopts a method with lower complexity to realize RRH allocation and subcarrier allocation to each user in a system, distributes rate constraint to each subcarrier, improves the resource utilization rate to the maximum extent and reduces the interference among users.
The purpose of the invention is realized by the following technical scheme: a user-centered ultra-dense network resource allocation method comprises the following steps:
s1, distributing the RRHs by adopting a bidirectional selection overlapping RRH clustering algorithm based on the geographic position;
s2, adopting a system subcarrier allocation algorithm based on priority to allocate subcarriers to all RRHs;
and S3, distributing the minimum rate lower limit constraint of each user to the subcarriers according to the distribution result design of the previous two steps, and solving the power distribution and the beam forming vector by using a WSMSE minimization method.
Further, the step S1 includes the following sub-steps:
s11, initializing clustering distance constraint D0；
S12, for user k, screening out the distance not exceeding D from the user k0All RRHs, noted as set
Figure BDA0002129769100000021
Will be provided with
Figure BDA0002129769100000022
RRHs in (a) are ordered from near to far according to distance to k; user k gives front
Figure BDA0002129769100000023
The RRH sends an association request and sends the RRH which has sent the association request to the slave
Figure BDA0002129769100000024
Deleting;
Figure BDA0002129769100000025
representing the number of RRHs which can be included in the cluster of the user k at most;
s13, counting whether the number of users of the association request received by the mth RRH exceeds the maximum number of users capable of being served by the mth RRH
Figure BDA0002129769100000026
If yes, sorting the users from near to far according to the distance to m, and taking the top
Figure BDA0002129769100000027
The users add the mth RRH into the cooperation clusters of the users; otherwise, directly adding the mth RRH into the cooperation clusters of the users;
s14, judging whether the number of the collaboration cluster elements of the user k' does not reach the upper limit
Figure BDA0002129769100000028
And is
Figure BDA0002129769100000029
Is not thatIf the cluster element is empty, the request is continuously and sequentially sent to the RRHs in the cluster element until the collaborative cluster elements of all the users reach the upper limit or
Figure BDA00021297691000000210
Empty, go to step S15; otherwise, directly executing step S15;
s15, checking whether the user still has no RRH service, if yes, D0And 5% is added, the steps S12-S14 are repeated, and if not, the operation is ended.
Further, the specific implementation method of step S2 is as follows: recording the total bandwidth of the system as B, and dividing the total bandwidth into N equal-width orthogonal sub-channels, assuming that the frequency reuse factor is 1, that is, each sub-channel can only be allocated to one user served by the sub-channel;
establishing a priority for all users, and continuously and dynamically updating the priority of the user in the sub-channel distribution process; the sub-channel allocation scheme requires that users sharing the RRHs select different sub-channels on the RRHs for signal transmission, so that sub-channel allocation is selected to be performed on each active RRH;
each user node has a list of alternative sub-channels, and the list is recorded as
Figure BDA00021297691000000211
Next, the following steps are performed for all RRHs in sequence:
s21, initializing the mth RRH assignable sub-channel list
Figure BDA00021297691000000212
S22, establishing a priority order for all connected users of the mth RRH;
s23, traversing all user nodes k of the mth RRH service according to the priority given by the step S22, and selecting the user nodes k
Figure BDA00021297691000000213
The medium available channel is
Figure BDA00021297691000000214
Has a value of 1 and is in
Figure BDA00021297691000000215
Channel n with highest priority1Coloring the dots to record
Figure BDA00021297691000000216
Channel n to select1In that
Figure BDA00021297691000000217
Is set to zero, i.e.
Figure BDA00021297691000000218
S24, repeating the steps S22 and S23, namely
Figure BDA00021297691000000219
Under the condition of saving, reordering the users of the m services and sequentially selecting and distributing the sub-channels; up to
Figure BDA0002129769100000031
No longer changing, obtaining the sub-channel distribution result of the RRH
Figure BDA0002129769100000032
Further, the step S22 includes the following sub-steps:
s221, setting a number for all users in a random sequence;
s222, calculating the degree of each user node, namely the number of clustered RRHs;
s223, for all user nodes, first, according to whether the sub-channels have been allocated, dividing the users into two groups: the priority of the user which has not been allocated with the sub-channel is higher, and the priority of the user which has been allocated with more sub-channels in each group is lower; the user nodes with the obtained channel number have higher priority lower than the lower priority, and have the same degree and lower priority with the same label; in this way a priority order is established for the user.
Further, the specific implementation method of step S3 is as follows: establishing an allocation weight matrix using an allocation method with respect to rate constraints
Figure BDA0002129769100000033
Wherein the expression of each term value is:
Figure BDA0002129769100000034
wherein the content of the first and second substances,
Figure BDA0002129769100000035
representing the channel gain vectors for users k through RRHm on subcarrier n,
Figure BDA0002129769100000036
represents a vector 2 norm;
then, rate constraints for each subcarrier are determined as
Figure BDA0002129769100000037
Figure BDA0002129769100000038
Represents the minimum rate requirement for user k;
the model is reduced to a form that is solvable with the WEMSE method as follows:
Figure BDA0002129769100000039
wherein the content of the first and second substances,
Figure BDA00021297691000000310
a beamforming vector representing power allocation of users k to the mth RRH on a subcarrier n;
Figure BDA00021297691000000311
represents a power ceiling constraint for the mth RRH;
Figure BDA00021297691000000312
is the rate of user k on subcarrier n, and its expression is:
Figure BDA00021297691000000313
Figure BDA0002129769100000041
represents the set of subchannel assignments for user k, σ represents the additive white noise average power of the channel,
Figure BDA0002129769100000042
the cluster representing user k can include at most the number of RRHs.
The invention has the beneficial effects that: the invention provides a user-centered overlapping RRH clustering method based on geographic positions; further, designing a subcarrier allocation scheme aiming at eliminating the same frequency interference of overlapping clustering; and finally, allocating the rate constraint of each user to an active channel and calculating a beamforming vector to realize resource allocation and interference coordination in the whole network. The invention adopts a method with lower complexity to realize RRH allocation and subcarrier allocation to each user in the system, distributes rate constraint to each subcarrier, simultaneously improves the resource utilization rate to the maximum extent, reduces the interference among users and can effectively improve the transmission efficiency of the system.
Drawings
FIG. 1 is a schematic diagram of ultra-dense network clustering;
FIG. 2 is a flow chart of a user-centric ultra-dense network resource allocation method of the present invention;
FIG. 3 is a flow chart of the allocation of RRHs using the bi-directional selection overlap RRH clustering algorithm based on geographical location of the present invention;
fig. 4 is a flowchart of subcarrier allocation to all RRHs by the priority-based system subcarrier allocation algorithm of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The method is suitable for MIMO (multiple input multiple output) ultra-dense network scenes taking users as centers. In the scene, the user nodes are close to each other, the RRHs are also densely distributed, and data is transmitted to the user through a certain beam forming scheme; the system performs unified scheduling of signal transmission through a Base Band Unit (BBU) pool, and any RRH can provide service for any user on any channel. The scenario of the present invention is shown in fig. 1.
In such cellular networks, user equipments at close distances cause very large interference to each other, and the transmission efficiency of the system is seriously affected. Therefore, the invention firstly provides an overlapping RRH clustering method based on the geographical position by taking the user as the center; further, designing a subcarrier allocation scheme aiming at eliminating the same frequency interference of overlapping clustering; and finally, allocating the rate constraint of each user to an active channel and calculating a beamforming vector to realize resource allocation and interference coordination in the whole network.
Wherein, the beamforming can directly adopt the mentioned WSMSE (weighted sum mean-square-error) minimization method to carry out convex optimization solution.
Consider a downstream UDN system with M RRHs, each RRH having NtAn antenna; the system is an ultra-dense network, so the system is considered to have K < M users, and each user is a single antenna. The geographical locations of both the users and the RRHs follow a uniform distribution.
Figure BDA0002129769100000051
The problem can be expressed using the model described above, wherein,
Figure BDA0002129769100000052
is the rate of user k on subcarrier n, expressed as
Figure BDA0002129769100000053
Figure BDA0002129769100000054
A set of subchannel assignments representing user k;
Figure BDA0002129769100000055
representing the number of RRHs which can be included in the cluster of the user k at most;
Figure BDA0002129769100000056
indicating whether RRHm allocates subchannel n to user k.
The traditional wireless communication system based on fixed cell planning is difficult to effectively adapt to the communication requirements of users, and cannot well adapt to the non-uniformity and dynamic variability of the users in time domain and space domain distribution and service types. Based on the reasons, the user service groups are divided by taking the user as the center, and the service requirements of the user can be more effectively met. Meanwhile, the existing subcarrier allocation scheme often assumes that RRHs serving the same user select the same subcarrier to perform channel transmission on the user, and even allocates only one subcarrier to each user, which is simpler and easier to implement in an allocation algorithm and also easier to perform subsequent calculation, but actually causes waste of some available frequency bands. Thus, the present invention contemplates independent subcarrier allocation selection on each RRH; compared with the average rate constraint or the weight distribution according to the transmission channel quality, the load balance can be more effectively realized by considering the influence of the signal transmission channel and the interference transmission channel, and the overall frequency band utilization rate of the system is the highest.
As shown in fig. 2, a user-centric ultra-dense network resource allocation method of the present invention includes the following steps:
s1, in order to meet the requirements of centering on users and low overhead, an RRH is distributed by adopting a bidirectional selection overlapping RRH clustering algorithm based on the geographic position; the specific implementation process is shown in fig. 3, and includes the following sub-steps:
s11, initializing clustering distance constraint D0；
S12, for user k, screening out the distance not exceeding D from the user k0All RRHs, noted as set
Figure BDA0002129769100000061
Will be provided with
Figure BDA0002129769100000062
RRHs in (a) are ordered from near to far according to distance to k; user k gives front
Figure BDA0002129769100000063
The RRH sends an association request and sends the RRH which has sent the association request to the slave
Figure BDA0002129769100000064
Deleting;
Figure BDA0002129769100000065
representing the number of RRHs which can be included in the cluster of the user k at most;
s13, counting whether the number of users of the association request received by the mth RRH exceeds the maximum number of users capable of being served by the mth RRH
Figure BDA0002129769100000066
If the users are sorted from near to far according to the distance to m, the users are taken before
Figure BDA0002129769100000067
The users add the mth RRH into the cooperation clusters of the users; otherwise, directly adding the mth RRH into the cooperation clusters of the users;
s14, judging whether the number of the collaboration cluster elements of the user k' does not reach the upper limit
Figure BDA0002129769100000068
And is
Figure BDA0002129769100000069
If not, continuing to sequentially send requests to RRHs in the network, and if so, continuing to send requests to RRHs in the network until all users cooperate with cluster elementsThe element reaches an upper limit of
Figure BDA00021297691000000610
Empty, go to step S15; otherwise, directly executing step S15;
s15, checking whether the user still has no RRH service, if yes, D0And 5% is added, the steps S12-S14 are repeated, and if not, the operation is ended.
S2, adopting a system subcarrier allocation algorithm based on priority to allocate subcarriers to all RRHs;
since the clustering result discussed in step S1 is that different cooperating clusters of users may contain the same RRH, if these users all transmit on the same sub-channel on the overlapped RRHs, very large co-channel interference will be generated. There is a need for a resource block allocation method for the purpose of eliminating close range co-channel interference.
As shown in fig. 4, the specific implementation method of step S2 is as follows:
recording the total bandwidth of the system as B, and dividing the total bandwidth into N equal-width orthogonal sub-channels, assuming that the frequency reuse factor is 1, that is, each sub-channel can only be allocated to one user served by the sub-channel;
establishing a priority for all users, and continuously and dynamically updating the priority of the user in the sub-channel distribution process; the sub-channel allocation scheme requires that users sharing the RRHs select different sub-channels on the RRHs for signal transmission, so that sub-channel allocation is selected to be performed on each active RRH;
each user node has a list of alternative sub-channels, and the list is recorded as
Figure BDA00021297691000000611
Next, the following steps are performed for all RRHs in sequence:
s21, initializing the mth RRH assignable sub-channel list
Figure BDA00021297691000000612
S22, establishing a priority order for all connected users of the mth RRH; the method comprises the following substeps:
s221, setting a number for all users in a random sequence;
s222, calculating the degree of each user node, namely the number of clustered RRHs;
s223, for all user nodes, first, according to whether the sub-channels have been allocated, dividing the users into two groups: the priority of the user which has not been allocated with the sub-channel is higher, and the priority of the user which has been allocated with more sub-channels in each group is lower; the user nodes with the obtained channel number have higher priority lower than the lower priority, and have the same degree and lower priority with the same label; in this way a priority order is established for the user.
S23, traversing all user nodes k of the mth RRH service according to the priority given by the step S22, and selecting the user nodes k
Figure BDA0002129769100000071
The medium available channel is
Figure BDA0002129769100000072
Has a value of 1 and is in
Figure BDA0002129769100000073
Channel n with highest priority1Coloring the dots to record
Figure BDA0002129769100000074
Channel n to select1In that
Figure BDA0002129769100000075
Is set to zero, i.e.
Figure BDA0002129769100000076
S24, repeating the steps S22 and S23, namely
Figure BDA0002129769100000077
Re-serving m if savedThe users in the system sort and sequentially select and distribute the sub-channels; up to
Figure BDA0002129769100000078
No longer changing, obtaining the sub-channel distribution result of the RRH
Figure BDA0002129769100000079
S3, according to the distribution result design of the previous two steps, the minimum rate lower limit constraint of each user is distributed to the sub-carriers, and a WSMSE minimization method is used for solving power distribution and beam forming vectors;
and finally, calculating power distribution and a beamforming vector to obtain a final system total throughput and energy utilization expression, wherein the existing WEMSE minimization method is adopted for solving. According to the characteristics of the solving method, rate constraint is required to be distributed to each subcarrier to simplify the model into a convex optimization model. According to the expression of the speed, the quality of an information transmission channel of a user on a certain subcarrier plays a positive role in the final speed, and the transmission strength of other channels on the same subcarrier directly influences the interference strength of the user. The specific implementation method comprises the following steps: establishing an allocation weight matrix using an allocation method with respect to rate constraints
Figure BDA00021297691000000710
Wherein the expression of each term value is:
Figure BDA00021297691000000711
wherein the content of the first and second substances,
Figure BDA00021297691000000712
representing the channel gain vectors for users k through RRHm on subcarrier n,
Figure BDA00021297691000000713
represents a vector 2 norm;
then, rate constraints for each subcarrier are determined as
Figure BDA00021297691000000714
Figure BDA00021297691000000715
Represents the minimum rate requirement for user k;
the model is reduced to a form that is solvable with the WEMSE method as follows:
Figure BDA0002129769100000081
wherein the content of the first and second substances,
Figure BDA0002129769100000082
a beamforming vector representing power allocation of users k to the mth RRH on a subcarrier n;
Figure BDA0002129769100000083
represents a power ceiling constraint for the mth RRH;
Figure BDA0002129769100000084
is the rate of user k on subcarrier n, and its expression is:
Figure BDA0002129769100000085
Figure BDA0002129769100000086
represents the set of subchannel assignments for user k, σ represents the additive white noise average power of the channel,
Figure BDA0002129769100000087
the cluster representing user k can include at most the number of RRHs.
Figure BDA0002129769100000088
Two parameters of power distribution and beam forming vectors can be solved, and the module value of the non-unitized vector solved by the model is the power distribution; then vector unitAfter the quantization, the beamforming vector is obtained.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (4)

1. A method for allocating user-centered ultra-dense network resources is characterized by comprising the following steps:
s1, distributing the RRHs by adopting a bidirectional selection overlapping RRH clustering algorithm based on the geographic position;
s2, adopting a system subcarrier allocation algorithm based on priority to allocate subcarriers to all RRHs; the specific implementation method comprises the following steps: recording the total bandwidth of the system as B, and dividing the total bandwidth into N equal-width orthogonal sub-channels, assuming that the frequency reuse factor is 1, that is, each sub-channel can only be allocated to one user served by the sub-channel;
establishing a priority for all users, and continuously and dynamically updating the priority of the user in the sub-channel distribution process; the sub-channel allocation scheme requires that users sharing the RRHs select different sub-channels on the RRHs for signal transmission, so that sub-channel allocation is selected to be performed on each active RRH;
each user node has a list of alternative sub-channels, and the list is recorded as
Figure FDA0002648517790000011
Next, the following steps are performed for all RRHs in sequence:
s21, initializing the mth RRH assignable sub-channel list
Figure FDA0002648517790000012
S22, establishing a priority order for all connected users of the mth RRH;
s23, traversing all user nodes k of the mth RRH service according to the priority given by the step S22, and selecting the user nodes k
Figure FDA0002648517790000013
The medium available channel is
Figure FDA0002648517790000014
Has a value of 1 and is in
Figure FDA0002648517790000015
Channel n with highest priority1Coloring the channel, recording coloring
Figure FDA0002648517790000016
Channel n to select1In that
Figure FDA0002648517790000017
Is set to zero, i.e.
Figure FDA0002648517790000018
S24, repeating the steps S22 and S23, namely
Figure FDA0002648517790000019
Under the condition of saving, reordering the users of the m services and sequentially selecting and distributing the sub-channels; up to
Figure FDA00026485177900000110
No longer changing, obtaining the sub-channel distribution result of the RRH
Figure FDA00026485177900000111
And S3, distributing the minimum rate lower limit constraint of each user to the subcarriers according to the distribution result design of the previous two steps, and solving the power distribution and the beam forming vector by using a WSMSE minimization method.
2. The method of claim 1, wherein the step S1 includes the following sub-steps:
s11, initializing clustering distance constraint D0；
S12, for user k, screening out the distance not exceeding D from the user k0All RRHs, noted as set
Figure FDA00026485177900000112
Will be provided with
Figure FDA00026485177900000113
RRHs in (a) are ordered from near to far according to distance to k; user k gives front
Figure FDA00026485177900000114
The RRH sends an association request and sends the RRH which has sent the association request to the slave
Figure FDA00026485177900000115
Deleting;
Figure FDA00026485177900000116
representing the number of RRHs which can be included in the cluster of the user k at most;
s13, counting whether the number of users of the association request received by the mth RRH exceeds the maximum number of users capable of being served by the mth RRH
Figure FDA0002648517790000021
If the users are sorted from near to far according to the distance to m, the users are taken before
Figure FDA0002648517790000022
The users add the mth RRH into the cooperation clusters of the users; otherwise, directly adding the mth RRH into the sameA collaborative cluster of users;
s14, judging whether the number of the collaboration cluster elements of the user k does not reach the upper limit
Figure FDA0002648517790000023
And is
Figure FDA0002648517790000024
If not, continuing to sequentially send requests to the RRHs in the network, and if so, continuing to send the requests to the RRHs in the network until the cooperative cluster elements of all the users reach the upper limit or
Figure FDA0002648517790000025
Empty, go to step S15; otherwise, directly executing step S15;
s15, checking whether the user still has no RRH service, if yes, D0And 5% is added, the steps S12-S14 are repeated, and if not, the operation is ended.
3. The method of claim 1, wherein the step S22 includes the following sub-steps:
s221, setting a number for all users in a random sequence;
s222, calculating the degree of each user node, namely the number of clustered RRHs;
s223, for all user nodes, first, according to whether the sub-channels have been allocated, dividing the users into two groups: the priority of the user which has not been allocated with the sub-channel is higher, and the priority of the user which has been allocated with more sub-channels in each group is lower; the user nodes with the obtained channel number have higher priority than lower priority and the user nodes with the same degree and the same number have lower priority; in this way a priority order is established for the user.
4. The method for allocating user-centric ultra-dense network resources according to claim 2, wherein the step S3 is implemented by: establishing allocation rights using an allocation method with respect to rate constraintsWeight matrix
Figure FDA0002648517790000026
Wherein the expression of each term value is:
Figure FDA0002648517790000027
wherein the content of the first and second substances,
Figure FDA0002648517790000028
representing the channel gain vectors for users k through RRH m on subcarrier n,
Figure FDA0002648517790000029
representing the vector 2 norm, K representing the number of users, M representing the number of RRHs;
then, rate constraints for each subcarrier are determined as
Figure FDA00026485177900000210
Figure FDA00026485177900000211
Represents the minimum rate requirement for user k;
the model is reduced to a form that is solvable with the WSMSE method as follows:
Figure FDA0002648517790000031
Figure FDA0002648517790000032
Figure FDA0002648517790000033
wherein the content of the first and second substances,
Figure FDA0002648517790000034
representing a subcarrierBeamforming vectors with power allocation for users k to the mth RRH on the wave n;
Figure FDA0002648517790000035
represents a power ceiling constraint for the mth RRH;
Figure FDA0002648517790000036
is the rate of user k on subcarrier n, and its expression is:
Figure FDA0002648517790000037
Figure FDA0002648517790000038
represents the set of subchannel assignments for user k, σ represents the additive white noise average power of the channel,
Figure FDA0002648517790000039
the cluster representing user k can include at most the number of RRHs.
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