CN113286310A - Ultra-dense network user number and micro base station number matching method based on dual-connection technology - Google Patents

Ultra-dense network user number and micro base station number matching method based on dual-connection technology Download PDF

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CN113286310A
CN113286310A CN202110578200.5A CN202110578200A CN113286310A CN 113286310 A CN113286310 A CN 113286310A CN 202110578200 A CN202110578200 A CN 202110578200A CN 113286310 A CN113286310 A CN 113286310A
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base station
micro base
user
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macro
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CN113286310B (en
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肖海林
周梦
刘红霖
汪鹏君
李嘉
杨婧雷
沈君凤
曾张帆
刘海龙
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Yunbao Big Data Industry Development Co ltd
Hubei University
Wenzhou University
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Hubei University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10—Dynamic resource partitioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • 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

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Abstract

The invention discloses a method for matching the number of ultra-dense network users with the number of micro base stations based on a dual-connection technology, which comprises the following steps: 1) constructing a dual-connection-based ultra-dense network communication model: 2) respectively calculating signal to interference and noise ratios (SINRs) from the micro base station and the macro base station to the user; 3) setting up an incidence matrix XM*N(ii) a 4) Calculating the total communication capacity of the system; 5) calculating the actual grid-connected energy consumption of the system; 6) the Dinkelbach algorithm iteratively solves the maximum energy efficiency; 7) a most energy efficient association mode with priority; 8) calculating the overlapping number of the coverage area of the micro base station; 9) and calculating the proportional range lambda of the number of the micro base stations and the number of the users. The method can provide data for the user by adopting a dual-connection technology under the constraint condition of ensuring the user connection and combining the maximum energy efficiency association mode with priority by reasonably configuring the number of the micro base stations, thereby controlling the user from the sourceThe number of the micro base stations is controlled, system interference and switching rate are reduced, and system energy efficiency is provided, so that network performance is improved.

Description

Ultra-dense network user number and micro base station number matching method based on dual-connection technology
Technical Field
The invention relates to the technical field of communication, in particular to a method for matching the number of ultra-dense network users with the number of micro base stations based on a dual-connection technology.
Background
The mass of intelligent terminal devices presents an increasing situation, heterogeneous ultra-dense networks are widely applied in the 4G era, with the arrival of the 5G era, macro base stations and multiplied micro base stations are deployed in a single cell by the ultra-dense networks to meet emerging communication services of specific requirements of users, the dual-connection technology is the cooperation of macro base stations and micro base stations, data and control are separated, the micro base stations introduce an energy capture technology to provide green energy, the requirements of 5G communication on high speed, large connection and low time delay are met, and the green communication concept is also met.
However, a large number of micro base stations are still inevitably invested in a super-dense network communication model based on double connection, so that the cost is high, the management and maintenance difficulty is increased, the energy consumption is huge, and too many micro base stations can cause interference and switching to a large extent, and the communication quality is influenced. However, in the prior art, an ultra-dense network communication model based on dual connectivity only relates to the influence of the number of micro base stations in a dense network on energy efficiency, and the problem of the ratio of the number of users to the micro base stations in the dual connectivity technology is not further researched at present.
Therefore, in the ultra-dense network communication model based on the dual connectivity, the capacity and energy efficiency of the system need to be considered, the number of micro base stations with a proper proportion is deployed according to the change of the number of users, a maximum energy efficiency association mode with priority is combined, the micro base stations are preferentially selected to provide data for the users as far as possible, when the micro base stations cannot provide data for some users, the macro base stations provide data for the users, in the process, the macro base stations are always used as main base stations to play a role in macro regulation and control for the actual association of all users, so that the users can still switch associated objects at will when being in an overlapping area of the coverage area of the micro base stations, and the overall performance of the network is improved.
Disclosure of Invention
The invention aims to provide a method for matching the number of ultra-dense network users and the number of micro base stations based on a dual-connection technology aiming at the defects of the prior art. The method can adopt a dual-connection technology under the constraint condition of ensuring user connection, provide data for the user by reasonably configuring the number of the micro base stations and combining the maximum energy efficiency association mode with priority, control the number of the micro base stations from the source, reduce system interference and switching rate, and provide system energy efficiency so as to improve network performance.
The technical scheme for realizing the purpose of the invention is as follows:
a method for matching the number of ultra-dense network users with the number of micro base stations based on a dual-connection technology comprises the following steps:
1) constructing a dual-connection-based ultra-dense network communication model: assuming that a dual-connection super-dense network is composed of a single macro base station MBS, M micro base stations SBS and N users UE, the micro base stations and the users are all subject to poisson distribution, the set of base stations is represented by I ═ 1,2 …, M }, where I < M denotes a micro base station, I ═ M denotes a macro base station, the set of users is represented by J ═ 1,2.. N }, BU ═ 1,2 …, B }, BU ∈ J, BU denotes a user provided with data by the micro base station, CU ═ 1,2 …, C }, CU ∈ J, CU denotes a user provided with data by the macro base station;
2) respectively calculating signal to interference and noise ratio (SINR) from the micro base station and the macro base station to the user: the wireless channel is subject to quasi-static rayleigh flat fading, and the signal-to-interference-and-noise ratios from a single micro base station and a macro base station to users respectively are set as shown in formulas (1) and (2):
Figure BDA0003085084160000021
Figure BDA0003085084160000022
wherein p isi,bAnd pM,cThe transmission power g of the micro base station i and the macro base station M to the micro user b and the macro user c respectivelyi,bAnd gM,cChannel gains, σ, from the micro base station i and the macro base station M to the micro user b and the macro user c, respectively2Is the noise power, gi,b＝|hi,b|2*di,b -α，gM,c＝|hM,c|2*dM,c -α，|hi,b|2And | hM,c|2Channel gains, d, for the micro base station i and macro base station M, respectively, for the links of the micro user b and macro user ci,bAnd dM,cRespectively setting the distances between the micro base station i and the macro base station M to the links of the micro user b and the macro user c, wherein alpha is a path loss factor;
3) setting up an incidence matrix XM*N: m base stations and N users in a dual-connection ultra-dense network form a correlation matrix XM*N，XM*N＝{X(M-1)*B,X1,C}, matrix X(M-1)*BRepresentation matrix XM*NThe first M-1 row of (A) is the physical connection condition of the micro base station i and the micro user b, and the matrix X1*CRepresentation matrix XM*NThe physical connection condition of the macro base station M and the macro user c is 1 if the physical connection condition is correlated, or 0 if the physical connection condition is not correlated, as shown in formula (3) and formula (4):
Figure BDA0003085084160000023
Figure BDA0003085084160000024
wherein x isi,bAnd xM,cAre respectively a matrix X(M-1)*BAnd matrix X1*COne element of (1), xi,b1 denotes the data association provided by the micro base station for the user in practice, x M,c1 means that the macro base station actually provides data association for the user;
4) calculating the total communication capacity of the system: according to shannon's theorem, when a micro user is associated with a micro base station and a macro user is associated with a macro base station, data rates are respectively shown in formula (5) and formula (6):
Figure BDA0003085084160000025
cM,c＝Wlog(1+rM,c) (6)，
wherein W is the channel bandwidth, and x is the associated elementi,b、xM,cData rate c when associated with micro base station and macro base station respectivelyi,b、cM,cMultiplying and then adding up to obtain the total communication capacity as shown in formula (7):
Figure BDA0003085084160000031
5) calculating the actual grid-connected energy consumption of the system: the power consumption of the base station consists of static power consumption and dynamic power consumption if
Figure BDA0003085084160000032
And
Figure BDA0003085084160000033
respectively representing static power consumption of the micro base station and the macro base station, and considering the associated element xi,b、xM,cWhile the dynamic power, beta, is variediAnd betaMLinear coefficients reflecting the relationship between the traffic load and the dynamic power in the micro base station and the macro base station are respectively expressed, so that when the micro base station and the macro base station provide data for a user, the total power consumption is respectively shown as a formula (8) and a formula (9):
Figure BDA0003085084160000034
Figure BDA0003085084160000035
the micro base station provides a part of energy for the micro base station by adopting an energy capture technology, and supposing that the micro base station can use the energy of solar energy in a unit time slot as EmTherefore, the sum of the green energy used by all micro base stations in a unit time slot is shown in equation (10):
Figure BDA0003085084160000036
the micro base station adopts hybrid energy supply, on one hand, clean energy in the environment, namely solar energy and wind energy, is collected by utilizing an energy capture technology to supply energy to the system, on the other hand, grid-connected power supply is combined, and the macro base station always adopts grid-connected power supply, so that the total actual grid-connected power consumption of the micro base station and the macro base station in the system is shown as a formula (11):
Ptotal＝Pi+PM-Pi green (11)；
6) the Dinkelbach algorithm iteratively solves the maximum energy efficiency: energy efficiency is the ratio of total rate to total power, and energy efficiency expressions can be obtained by substituting equation (7) and equation (11), as shown in equation (12):
Figure BDA0003085084160000037
the formula (12) is expressed by a structural function F (gamma) by using a Dinkelbach method
Figure BDA0003085084160000041
The equation is reduced, as shown in equation (13):
F(γ)＝Rtotal(Xi,j)-γPtotal(Xi,j) (13)，
when the optimum γ value is found so that F (γ) is 0, γ is made γ ═ γ*＝ηEEAs shown in equation (14):
Figure BDA0003085084160000042
for solving the optimal energy efficiency value etaEEBy adopting an iterative method, firstly, arbitrarily initiating a gamma0Calculating a user association matrix Xi,jThe correlation matrix Xi,jIs unknown and takes on special values-only 0 or 1, Xi,jSubstituting into formula (7) and formula (11) to solve RtotalAnd PtotalWill be gamma0、RtotalAnd PtotalTogether into equation (13), if F (. gamma.) is not equal to 0, R is substitutedtotalAnd PtotalSubstituting equation (14), solving a new intermediate variable gamma, and repeating the above process until the iteration reaches F (gamma) 0, taking gamma*＝γ＝ηEEObtaining the optimal energy efficiency value;
7) most energy-efficient association pattern with priority: extracting energy efficiency when the ith micro base station is associated with the b-th micro user from formula (13), as shown in formula (15):
Figure BDA0003085084160000043
to maximize equation (13), I traverses from I ═ {1,2 …, M-1}, solving in turn max (O (X) for a single micro base station I and a single user bi,b) ) and arranged in sequence, selected according to maximum energy efficiencySelecting a condition for association, and if the user cannot be connected to the micro base station, actually associating the user to the macro base station, as shown in formula (16):
Figure BDA0003085084160000044
wherein,
Figure BDA0003085084160000045
meaning that the user is provided with data by only one micro base station,
Figure BDA0003085084160000046
indicating that the number of users associated with the micro base station is not larger than the maximum connectable number of the micro base station
Figure BDA0003085084160000047
Figure BDA0003085084160000048
Indicating that the number of users associated with the macro base station is not more than the maximum connectable number of the macro base station
Figure BDA0003085084160000049
8) Calculating the overlapping number of the coverage area of the micro base station: the micro base stations obey Poisson distribution, and when the position of the micro base station is Di＝(xi,yi) And if the coverage radius is R, calculating the distance between every two micro base stations by adopting an Euclidean distance, as shown in a formula (17):
Figure BDA0003085084160000051
wherein I belongs to I, I is not equal to M, t belongs to I, t is not equal to M, t is not equal to I, and when d is not more than 2R, namely, a user exists in an overlapping area of the micro base station I and the micro base station t;
9) calculating the proportion range lambda of the number of the micro base stations and the number of the users: when the number of users is certain, the optimal ratio is shown in formula (18), and only the micro base station provides data for the users, so that the connection requirement of the users is met when the optimal energy efficiency is achieved, the working efficiency of the base station is improved, and the number of coverage areas is limited, but the connection requirement of the users has obvious tidal effect, and the users are few and distributed unevenly, or the users have the emergency situation that the number of the excessive micro base stations cannot bear at present, the dual-connection technology provides data for the users at the same time, and the micro base station and the macro base station provide data for the users at the same time, as shown in formula (19), although some energy consumption is sacrificed, the access requirement of the users can be guaranteed, and the system capacity can be maximized:
Figure BDA0003085084160000052
wherein,
Figure BDA0003085084160000053
is the maximum number of connections of the micro base station,
Figure BDA0003085084160000054
and when K is the maximum connectable number of the macro base station and λ is 1, the actual number of the micro base stations provides data for the user.
The technical scheme adopts a dual-connection technology to be applied to the ultra-dense heterogeneous network, and provides data for the user through a maximum energy efficiency association mode with priority, the mode firstly meets the connection requirement of the user, obtains the maximum communication capacity, secondly reduces the coverage ratio of the micro base station by controlling the ratio of the number of the user to the number of the micro base station, introduces green energy supply, reduces the energy consumption of the system, improves the energy efficiency of the system, and accordingly ensures the user experience quality and improves the energy efficiency of the system.
The method can control the number of the micro base stations, ensure that the connection requirement of a user can be met when the started micro base stations are not in an idle state, reduce interference and switching, and enable the system to achieve higher system capacity and energy efficiency so as to improve network performance.
Drawings
FIG. 1 is a diagram of an embodiment of a dual-connectivity ultra-dense network model;
FIG. 2 is a flowchart of an algorithm of Dinkelbach and a prioritized energy efficiency maximum association mode in an embodiment;
FIG. 3 is a schematic flow chart of an example method;
FIG. 4 is a graph comparing user number with increasing micro base station number for the embodiment method;
FIG. 5 is a graph comparing the increase of the number of micro base stations to the system capacity in two different connection modes according to the method of the embodiment;
FIG. 6 is a schematic diagram illustrating a comparison of graphs of energy efficiency of the system according to the embodiment under the condition that the number of micro base stations increases in two different connection modes;
FIG. 7 is a graph comparing the increase of the number of micro base stations to the overlapping number of coverage for different coverage radii of the micro base stations according to the method of the embodiment;
fig. 8 is a diagram illustrating the comparison of the column diagram of the idle micro base station with the requirement of the embodiment when the number of users increases.
Detailed Description
The invention will be further elucidated with reference to the drawings and examples, without however being limited thereto.
Example (b):
referring to fig. 3, a method for matching the number of users in an ultra-dense network and the number of micro base stations based on a dual connectivity technology includes the following steps:
1) constructing a dual-connection-based ultra-dense network communication model: as shown in fig. 1, it is assumed that a dual-connection super-dense network is composed of a single macro base station MBS, M micro base stations SBS and N users UE, each of the micro base stations and the users obey poisson distribution, the set of base stations is represented by I ═ 1,2.. and M }, where I < M denotes a micro base station, I ═ M denotes a macro base station, the set of users is represented by J ═ 1,2.. N }, BU ═ 1,2 …, B }, BU ∈ J, BU denotes a user provided with data by the micro base station, CU ═ {1,2 …, C }, CU ∈ J, and CU denotes a user provided with data by the macro base station;
2) respectively calculating signal to interference and noise ratio (SINR) from the micro base station and the macro base station to the user: the wireless channel is subject to quasi-static rayleigh flat fading, and the signal-to-interference-and-noise ratios from a single micro base station and a macro base station to users respectively are set as shown in formulas (1) and (2):
Figure BDA0003085084160000061
Figure BDA0003085084160000062
wherein p isi,bAnd pM,cThe transmission power g of the micro base station i and the macro base station M to the micro user b and the macro user c respectivelyi,bAnd gM,cChannel gains, σ, from the micro base station i and the macro base station M to the micro user b and the macro user c, respectively2Is the noise power, gi,b＝|hi,b|2*di,b -α，gM,c＝|hM,c|2*dM,c -α，|hi,b|2And | hM,c|2Channel gains, d, for the micro base station i and macro base station M, respectively, for the links of the micro user b and macro user ci,bAnd dM,cRespectively setting the distances between the micro base station i and the macro base station M to the links of the micro user b and the macro user c, wherein alpha is a path loss factor;
3) setting up an incidence matrix XM*N: m base stations and N users in a dual-connection ultra-dense network form a correlation matrix XM*N，XM*N＝{X(M-1)*B,X1,C}, matrix X(M-1)*BRepresentation matrix XM*NThe first M-1 row of (A) is the physical connection condition of the micro base station i and the micro user b, and the matrix X1*CRepresentation matrix XM*NThe physical connection condition of the macro base station M and the macro user c is 1 if the physical connection condition is correlated, or 0 if the physical connection condition is not correlated, as shown in formula (3) and formula (4):
Figure BDA0003085084160000063
Figure BDA0003085084160000071
wherein x isi,bAnd xM,cAre respectively a matrix X(M-1)*BAnd matrix X1*COne element of (1), xi,b1 denotes the data association provided by the micro base station for the user in practice, x M,c1 means that the macro base station actually provides data association for the user;
4) calculating the total communication capacity of the system: according to shannon's theorem, when a micro user is associated with a micro base station and a macro user is associated with a macro base station, data rates are respectively shown in formula (5) and formula (6):
Figure BDA0003085084160000072
cM,c＝Wlog(1+rM,c) (6), where W is the channel bandwidth, will associate element xi,b、xM,cData rate c when associated with micro base station and macro base station respectivelyi,b、cM,cMultiplying and then adding up to obtain the total communication capacity as shown in formula (7):
Figure BDA0003085084160000073
5) calculating the actual grid-connected energy consumption of the system: the power consumption of the base station consists of static power consumption and dynamic power consumption if
Figure BDA0003085084160000074
And
Figure BDA0003085084160000075
respectively representing static power consumption of the micro base station and the macro base station, and considering the associated element xi,b、xM,cWhile the dynamic power, beta, is variediAnd betaMLinear coefficients reflecting the relationship between the traffic load and the dynamic power in the micro base station and the macro base station are respectively expressed, so that when the micro base station and the macro base station provide data for a user, the total power consumption is respectively shown as a formula (8) and a formula (9):
Figure BDA0003085084160000076
Figure BDA0003085084160000077
the micro base station provides a part of energy for the micro base station by adopting an energy capture technology, and supposing that the micro base station can use the energy of solar energy in a unit time slot as EmTherefore, the sum of the green energy used by all micro base stations in a unit time slot is shown in equation (10):
Figure BDA0003085084160000078
the micro base station adopts hybrid energy supply, on one hand, clean energy in the environment, namely solar energy and wind energy, is collected by utilizing an energy capture technology to supply energy to the system, on the other hand, grid-connected power supply is combined, and the macro base station always adopts grid-connected power supply, so that the total actual grid-connected power consumption of the micro base station and the macro base station in the system is shown as a formula (11):
Ptotal＝Pi+PM-Pi green (11)；
6) the Dinkelbach algorithm iteratively solves the maximum energy efficiency: energy efficiency is the ratio of total rate to total power, and energy efficiency expressions can be obtained by substituting equation (7) and equation (11), as shown in equation (12):
Figure BDA0003085084160000081
the formula (12) is expressed by a structural function F (gamma) by using a Dinkelbach method
Figure BDA0003085084160000082
The equation is reduced, as shown in equation (13):
F(γ)＝Rtotal(Xi,j)-γPtotal(Xi,j) (13)，
when the optimum γ value is found so that F (γ) is 0, γ is made γ ═ γ*＝ηEEAs shown in equation (14):
Figure BDA0003085084160000083
for solving the optimal energy efficiency value etaEEAs shown in FIG. 2, an iterative method is adopted, and a gamma is arbitrarily initialized0Calculating a user association matrix Xi,jThe correlation matrix Xi,jIs unknown and takes on special values-only 0 or 1, Xi,jSubstituting into formula (7) and formula (11) to solve RtotalAnd PtotalWill be gamma0、RtotalAnd PtotalTogether into equation (13), if F (. gamma.) is not equal to 0, R is substitutedtotalAnd PtotalSubstituting equation (14), solving a new intermediate variable gamma, and repeating the above process until the iteration reaches F (gamma) 0, taking gamma*＝γ＝ηEEObtaining the optimal energy efficiency value;
7) most energy-efficient association pattern with priority: extracting energy efficiency when the ith micro base station is associated with the b-th micro user from formula (13), as shown in formula (15):
Figure BDA0003085084160000084
to maximize equation (13), I is traversed from I ═ {1,2.., M-1}, and max (O (X) of a single micro base station I and a single user b are solved in turni,b) And arranged in order, associated according to the maximum energy efficiency selection condition, and if the user cannot be connected to the micro base station, the user is actually associated to the macro base station, as shown in formula (16):
Figure BDA0003085084160000091
wherein,
Figure BDA0003085084160000092
meaning that the user is provided with data by only one micro base station,
Figure BDA0003085084160000093
indicating that the number of users associated with the micro base station is not larger than the maximum connectable number of the micro base station
Figure BDA0003085084160000094
Figure BDA0003085084160000095
Indicating that the number of users associated with the macro base station is not more than the maximum connectable number of the macro base station
Figure BDA0003085084160000096
8) Calculating the overlapping number of the coverage area of the micro base station: the micro base stations obey Poisson distribution, and when the position of the micro base station is Di＝(xi,yi) And if the coverage radius is R, calculating the distance between every two micro base stations by adopting an Euclidean distance, as shown in a formula (17):
Figure BDA0003085084160000097
wherein I belongs to I, I is not equal to M, t belongs to I, t is not equal to M, t is not equal to I, and when d is not more than 2R, namely, a user exists in an overlapping area of the micro base station I and the micro base station t;
9) calculating the proportion range lambda of the number of the micro base stations and the number of the users: when the number of users is certain, the optimal ratio is shown in formula (18), and only the micro base station provides data for the users, so that the connection requirement of the users is met when the optimal energy efficiency is achieved, the working efficiency of the base station is improved, and the number of coverage areas is limited, but the connection requirement of the users has obvious tidal effect, and the users are few and distributed unevenly, or the users have the emergency situation that the number of the excessive micro base stations cannot bear at present, the dual-connection technology provides data for the users at the same time, and the micro base station and the macro base station provide data for the users at the same time, as shown in formula (19), although some energy consumption is sacrificed, the access requirement of the users can be guaranteed, and the system capacity can be maximized:
Figure BDA0003085084160000098
wherein,
Figure BDA0003085084160000099
is the maximum number of connections of the micro base station,
Figure BDA00030850841600000910
and when K is the maximum connectable number of the macro base station and λ is 1, the actual number of the micro base stations provides data for the user.
Through simulation experiments, simulation results prove that compared with the traditional scheme, the method of the embodiment can coordinate the number of the micro base stations according to users, thereby ensuring the connection requirement of a dense network, improving the system capacity, ensuring the energy efficiency of the system and reducing the overlapping area of the coverage:
as can be seen from fig. 4, when a user is certain, along with the increase of the number of micro base stations, the total number of user connections increases first and then does not change, the number of users providing data by the micro base stations gradually increases, and the number of users providing data by the macro base station does not change first and then gradually decreases to zero, and theoretically, compared with the actual situation, the number of users connected by the micro base stations does not strictly increase according to the slope of the maximum number of connections of the micro base stations, which indicates that the number of micro base stations which are deployed to just provide data connections for all users cannot completely connect all users, and more micro base stations are needed to enable the macro base station to unload the task of data connection, and only the control task is executed;
as shown in fig. 5, in different connection modes, when a user is certain, and the system capacity increases slowly after the number of micro base stations increases, a dual connection mode has a higher system capacity starting point, because the capability of the macro base station for providing data for the user is far greater than that of a single micro base station, and the micro base station cannot provide data for the user, the macro base station carries a task of transmitting data, the proportioning formula (18) is verified, the final system capacity of the two modes is the same, because when the micro base stations are enough, the macro base station only performs a macro regulation function, the micro base station is enough to provide data for all users, and the proportioning formula (19) is verified to be in the same system, the larger the number of users is, and the larger the total system capacity is;
as shown in fig. 6, under different connection modes, system energy efficiency is increased and then decreased, because an excessive micro base station does not increase too much capacity, but causes huge energy consumption, it is obvious that using only the micro base station to provide data for a user has a higher energy efficiency starting point than that of a dual connection mode, because the micro base station has low power consumption and is equipped with an energy capture device, and it can also have higher energy efficiency under a very small system capacity, but the disadvantage is that the number of micro base stations at this time cannot meet a large number of user connection requirements, a proportioning formula (18) is verified, when the number of micro base stations is increased, the two modes finally have the same energy efficiency, a proportioning formula (19) is verified, and a stable state of separating a data plane and a control plane is achieved;
as shown in fig. 7, as the number of micro base stations increases, more coverage overlapping areas exist between the micro base stations, and when the coverage radius of the micro base station is larger, more micro base stations are deployed to cause more overlapping areas, and more micro base stations coverage areas overlap at the same position, when a user does not move or moves in a small range, the user is not easily handed over by using the dual connectivity technology, and when the user moves, although the user switches over when the coverage area of one micro base station moves to the coverage area of another micro base station, the more coverage overlapping areas, the smaller difference between parameters used by the macro base station for macro regulation and control selection is caused, and finally the handover rate increases;
as shown in fig. 8, when the number of users is fixed, compared with the case where the micro base station provides data for the users alone, the dual connection method requires more micro base stations, when the number of deployed micro base stations is the same as the number of users, the actual demand is much higher than the theoretical demand, and a large number of idle micro base stations also exist, so that the number of deployed micro base stations needs to be larger than the value of the theoretical demand to satisfy the user connection demand, and meanwhile, the idle micro base stations also need to be avoided, which causes unnecessary energy consumption and interference.

Claims (1)

1. A method for matching the number of ultra-dense network users with the number of micro base stations based on a dual-connection technology is characterized by comprising the following steps:
1) constructing a dual-connection-based ultra-dense network communication model: assuming that a dual-connection super-dense network is composed of a single macro base station MBS, M micro base stations SBS and N users UE, the micro base stations and the users are all subject to poisson distribution, the base station set is represented by I ═ {1,2.., M }, where I < M denotes a micro base station, I ═ M denotes a macro base station, the user set is represented by J ═ {1,2.. N }, BU ═ 1,2 …, B }, BU ∈ J, BU denotes a user provided with data by the micro base station, CU ═ {1,2 …, C }, CU ∈ J, CU denotes a user provided with data by the macro base station;
2) respectively calculating signal to interference and noise ratio (SINR) from the micro base station and the macro base station to the user: the wireless channel is subject to quasi-static rayleigh flat fading, and the signal-to-interference-and-noise ratios from a single micro base station and a macro base station to users respectively are set as shown in formulas (1) and (2):
Figure FDA0003085084150000011
Figure FDA0003085084150000012
wherein p isi，bAnd pM，cThe transmission power g of the micro base station i and the macro base station M to the micro user b and the macro user c respectivelyi，bAnd gM，cChannel gains, σ, from the micro base station i and the macro base station M to the micro user b and the macro user c, respectively2Is the noise power, gi，b＝|hi，b|2*di，b -α，gM，c＝|hM，c|2*dM，c -α，|hi，b|2And | hM，c|2Channel gains, d, for the micro base station i and macro base station M, respectively, for the links of the micro user b and macro user ci，bAnd dM，cRespectively setting the distances between the micro base station i and the macro base station M to the links of the micro user b and the macro user c, wherein alpha is a path loss factor;
3)setting up an incidence matrix XM*N: m base stations and N users in a dual-connection ultra-dense network form a correlation matrix XM*N，XM*N＝{X(M-1)*B，X1，C}, matrix X(M-1)*BRepresentation matrix XM*NThe first M-1 row of (A) is the physical connection condition of the micro base station i and the micro user b, and the matrix X1*cRepresentation matrix XM*NThe physical connection condition of the macro base station M and the macro user c is 1 if the physical connection condition is correlated, or 0 if the physical connection condition is not correlated, as shown in formula (3) and formula (4):
Figure FDA0003085084150000013
Figure FDA0003085084150000014
wherein x isi，bAnd xM，cAre respectively a matrix X(M-1)*BAnd matrix X1*COne element of (1), xi，b1 denotes the data association provided by the micro base station for the user in practice, xM，c1 means that the macro base station actually provides data association for the user;
4) calculating the total communication capacity of the system: according to shannon's theorem, when a micro user is associated with a micro base station and a macro user is associated with a macro base station, data rates are respectively shown in formula (5) and formula (6):
Figure FDA0003085084150000021
cM，c＝W log(1+rM，c)(6)，
wherein W is the channel bandwidth, and x is the associated elementi，b、xM，cData rate c when associated with micro base station and macro base station respectivelyi，b、cM，cMultiplying and then adding up to obtain the total communication capacity as shown in formula (7):
Figure FDA0003085084150000022
5) calculating the actual grid-connected energy consumption of the system: the power consumption of the base station consists of static power consumption and dynamic power consumption if
Figure FDA0003085084150000023
And
Figure FDA0003085084150000024
respectively representing static power consumption of the micro base station and the macro base station, and considering the associated element xi，b、xM，cWhile the dynamic power, beta, is variediAnd betaMLinear coefficients reflecting the relationship between the traffic load and the dynamic power in the micro base station and the macro base station are respectively expressed, so that when the micro base station and the macro base station provide data for a user, the total power consumption is respectively shown as a formula (8) and a formula (9):
Figure FDA0003085084150000025
Figure FDA0003085084150000026
the micro base station provides a part of energy for the micro base station by adopting an energy capture technology, and supposing that the micro base station can use the energy of solar energy in a unit time slot as EmTherefore, the sum of the green energy used by all micro base stations in a unit time slot is shown in equation (10):
Figure FDA0003085084150000027
the micro base station adopts hybrid energy supply, on one hand, clean energy in the environment, namely solar energy and wind energy, is collected by utilizing an energy capture technology to supply energy to the system, on the other hand, grid-connected power supply is combined, and the macro base station always adopts grid-connected power supply, so that the total actual grid-connected power consumption of the micro base station and the macro base station in the system is shown as a formula (11):
Ptotal＝Pi+PM-Pi green (11)；
6) the Dinkelbach algorithm iteratively solves the maximum energy efficiency: energy efficiency is the ratio of total rate to total power, and energy efficiency expressions can be obtained by substituting equation (7) and equation (11), as shown in equation (12):
Figure FDA0003085084150000031
the formula (12) is expressed by a structural function F (gamma) by using a Dinkelbach method
Figure FDA0003085084150000032
The equation is reduced, as shown in equation (13):
F(γ)＝Rtotal(Xi，j)-γPtotal(Xi，j)(13)，
when the optimum γ value is found so that F (γ) is 0, γ is made γ ═ γ*＝ηEEAs shown in equation (14):
Figure FDA0003085084150000033
for solving the optimal energy efficiency value etaEEBy adopting an iterative method, firstly, arbitrarily initiating a gamma0Calculating a user association matrix Xi，jThe correlation matrix Xi，jIs unknown and takes on special values-only 0 or 1, Xi，jSubstituting into formula (7) and formula (11) to solve RtotalAnd PtotalWill be gamma0、RtotalAnd PtotalTogether into equation (13), if F (. gamma.) is not equal to 0, R is substitutedtotalAnd PtotalSubstituting equation (14), solving a new intermediate variable gamma, and repeating the above process until the iteration reaches F (gamma) 0, taking gamma*＝γ＝ηEEBy obtainingAn optimal energy efficiency value;
7) most energy-efficient association pattern with priority: extracting energy efficiency when the ith micro base station is associated with the b-th micro user from formula (13), as shown in formula (15):
Figure FDA0003085084150000034
to maximize equation (13), I is traversed from I ═ {1,2.., M-1}, and max (O (X) of a single micro base station I and a single user b are solved in turni，b) And arranged in order, associated according to the maximum energy efficiency selection condition, and if the user cannot be connected to the micro base station, the user is actually associated to the macro base station, as shown in formula (16):
Figure FDA0003085084150000041
wherein,
Figure FDA0003085084150000042
meaning that the user is provided with data by only one micro base station,
Figure FDA0003085084150000043
indicating that the number of users associated with the micro base station is not larger than the maximum connectable number of the micro base station
Figure FDA0003085084150000044
Figure FDA0003085084150000045
Indicating that the number of users associated with the macro base station is not more than the maximum connectable number of the macro base station
Figure FDA0003085084150000046
8) Calculating the overlapping number of the coverage area of the micro base station: the micro base stations obey a Poisson distribution when the micro base stationsPosition Di＝(xi，yi) And if the coverage radius is R, calculating the distance between every two micro base stations by adopting an Euclidean distance, as shown in a formula (17):
Figure FDA0003085084150000047
wherein I belongs to I, I is not equal to M, t belongs to I, t is not equal to M, t is not equal to I, and when d is not more than 2R, namely, a user exists in an overlapping area of the micro base station I and the micro base station t;
9) calculating the proportion range lambda of the number of the micro base stations and the number of the users: when the number of users is certain, the optimal ratio is shown in formula (18), only the micro base station provides data for the users, but the user connection requirements have obvious tidal effects, and there are emergencies that the users are few and the distribution is not concentrated, or the number of the micro base stations too many users cannot bear at present, the dual connection technology provides data for the users at the same time, and the micro base station and the macro base station provide data for the users at the same time, as shown in formula (19), the system capacity is maximized:
Figure FDA0003085084150000048
wherein,
Figure FDA0003085084150000049
is the maximum number of connections of the micro base station,
Figure FDA00030850841500000410
and when K is the maximum connectable number of the macro base station and λ is 1, the actual number of the micro base stations provides data for the user.
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