CN108965034A - Small-cell base station super-intensive deployment under user-association to network method - Google Patents

Small-cell base station super-intensive deployment under user-association to network method Download PDF

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CN108965034A
CN108965034A CN201810996150.0A CN201810996150A CN108965034A CN 108965034 A CN108965034 A CN 108965034A CN 201810996150 A CN201810996150 A CN 201810996150A CN 108965034 A CN108965034 A CN 108965034A
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base station
user
priority
energy
users
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CN108965034B (en
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尼俊红
郭浩然
张烁
尹光辉
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North China Electric Power University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • 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
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • 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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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The present invention provides a kind of small-cell base station super-intensives to dispose lower user-association to the method for network, and the energy efficiency of lower energy mix heterogeneous network is disposed for improving super-intensive.The method includes base station is arranged according to itself power consumption situation and sends base station weighted value and priority first；Secondly, user perceives the service condition of green energy resource, the base station of priority access green energy resource abundance in association process.The present embodiment considers the energy mix super-intensive heterogeneous network scene that electric power energy and green energy resource are powered simultaneously, large scale channel fading is modeled using the diclinic rate path loss model in 5G, user is adaptively adjusted its associating policy according to the acquisition situation and service condition of base station green energy resource, the present embodiment in combination with matching theory in association process resource allocation and base station transmitting power optimize, effectively reduce electric power energy consumption, improve efficiency.

Description

Method for associating user to network under ultra-dense deployment of small cell base station
Technical Field
The invention relates to the technical field of communication networks, in particular to a method for associating users to a network under ultra-dense deployment of a small cell base station.
Background
With the development of computer and electronic technologies, communication networks are ubiquitous and carry enormous data traffic. Presumably, data traffic in 2020 will be 250 times that in 2010, which poses a significant challenge to existing communication systems. The fifth generation (5G) communication system has attracted attention from the information communication industry, and IMT-2020 has advanced the research of key technologies in 5G with emphasis, and high capacity, low energy consumption, and low latency are communication demands in 5G. In order to realize 5G communication, ultra-dense deployment needs to be carried out on a small cell base station in a macro base station, the ultra-dense deployment of the small cell base station inevitably increases the consumption of network energy, and at the moment, a hybrid energy ultra-dense heterogeneous network which simultaneously supplies power through electric energy and green energy is constructed. The super-dense heterogeneous network is one of the key technologies for realizing the 5G communication requirement.
The annual increase rate of energy consumption brought by information communication technology reaches 15-20%. The huge energy consumption generated by the ultra-dense deployment of scbss (small Cell Base states) becomes a challenging problem and should be properly solved to effectively develop the potential of the ultra-dense heterogeneous network. The increase of the energy consumption of the base station causes the emission of a large amount of greenhouse gases such as carbon dioxide and the like, and causes severe influence on the environment. In order to comply with the strategic goal of green sustainable development, "green communication" is recognized by academic circles at home and abroad and is one of the core goals in 5G. The main objective of the 5G green cellular network is to continuously improve the energy efficiency of the network, reduce the energy consumption of the system, and achieve the purpose of reducing global carbon emission while satisfying the increasing data traffic of users.
In the prior art, most research focuses on reducing the energy consumption of a system by taking user association and power optimization into consideration, but the effect of reducing the energy consumption by only power optimization is not obvious, and sustainable development needs to be realized by a hybrid energy mode of simultaneously supplying power by traditional electric energy (hereinafter, referred to as electric energy). Meanwhile, most of the existing heterogeneous networks adopt a single-slope path loss model to represent the path loss situation between the user and the base station. Although the study and analysis of the single path loss model is relatively easy, the conventional single slope path loss model is no longer accurate with the diversification of base station types and the densification of base stations. Prior work has begun to investigate dual slope path loss models.
Under the hybrid energy heterogeneous network, how to realize the saving of electric power energy consumption and the full utilization of green energy by associating users in the dual-slope path loss model to the network does not have a feasible scheme.
Disclosure of Invention
In order to improve the energy efficiency of a hybrid energy heterogeneous network under ultra-dense deployment, the invention provides a method for self-adaptive user association to the network based on green energy perception under ultra-dense deployment of a small cell base station.
In order to achieve the purpose, the invention adopts the following technical scheme.
The embodiment of the invention provides a method for associating users to a network under ultra-dense deployment of a small cell base station, which comprises the following steps:
step S1, the base station sets and sends the weight value and priority of the base station according to the power consumption condition of the base station;
and step S2, the user perceives the use condition of the green energy according to the weight value and the priority of the base station in the association process, determines the utility function of the user, and preferentially applies for accessing the base station with sufficient green energy.
Further, the method further comprises: step S01, adopting an FI dual-slope path loss model, wherein the model is as follows:
in the formula (1), d represents the distance from the base station to the user, dthAt critical distance, β is the floating intercept, α1Represents d < dthPath loss of timeRate of depletion α2Represents d > dthThe path loss slope of time;
the energy consumption problem model is constructed as follows:
the constraint conditions are as follows:
among them, the third restrictionIs the maximum transmit power limit of base station k, with an optimization variable of ankAnd tnk。
Further, in step S1, the setting the priority of the base station further includes:
setting different priorities for base stations of different energy sourcesWherein,
first priority χ1A base station: setting small cell base stations with green energy surplus as a first priority x1A base station;
second priority χ2A base station: the small cell base station (grey base station) without green energy remaining is set to the second priority χ2；
Third priority χ3A base station: setting the macro base station as a third priority χ3A base station; when no user accesses the macro base station, the macro base station is a dormant node and no longer provides service for the user.
Further, in the step S1, the weight value of the base station is set, and the weight value is setThe weighting factor of the base station is αk：
Wherein, CkRepresenting the power consumption of base station k, GkRepresents the green energy generation rate of base station k;
adjusting the value of η according to the energy consumption of the base stations and the green energy generation rate by an adjustment coefficient η, wherein the adjustment rule is to ensure that the minimum value of the weight factors of all the base stations is a positive number.
Further, the step S2 further includes:
step S201, initializing a set of users to be accessedAll users are selected; the power consumption of the base station at the initial moment is static power consumption; initialization AN×K＝{0}；
Step S202, a user i checks whether the utility functions of all base stations are 0, if yes, the user i does not submit the association application in the association process; if not, the user i calculates the utility obtained from each base station according to the received information;
step S203, determining whether there is a first priority χ1A base station; if yes, go to step S204; if not, go to step S205;
step S204, detecting χ1Whether the utility of the base station to the user i is all 0, if yes, the step S205 is carried out; if not, go to step S206;
step S205, determine all χ2Whether the utility of the base station to the user i is all 0, if not, the step S207 is switched to; if yes, go to step S212;
step S206, obtaining the utility functionGet the user i to x1Preference vector of base station, and applying for relation to χ which is ranked first according to preference vector1A base station; step S208 is executed;
step S207, obtaining a user X according to the utility function2Preference vector of base station, and applying for relation to χ which is ranked first according to preference vector2A base station; step S208 is executed;
step S208, the base station k forms preference vectors of all users applying for service according to the utility function of the base station k, and selects the user n with the first rank;
step S209, judge whether user n can access base station k; if not, go to step S210; if so, go to step S211;
step S210, the base station k refuses to provide service for the user, the information of access failure is fed back to the user, and the user sets the utility function provided by the base station k to 0; step S202 is executed;
step S211, the user n is selected fromRemoving the user n and all the users accessed to the base station k before, and re-executing the resource allocation process to update the actual transmitting power of the base station k; the base station k updates the weight factor according to the power consumption condition of the base station k, updates the priority according to the green energy remaining condition, and then shifts to the step S218;
step S212, a user i submits an access application to a macro base station;
step S213, the macro base station forms preference vectors of all users applying for service according to the utility function of the macro base station, and selects a user n with the first rank;
step S214, judging whether the user n can access the macro base station, if yes, turning to step S217, and if not, turning to step S215;
step S215, detectingWhether the utilities obtained by all the users from all the base stations are 0 or not is judged, and if yes, the step S216 is carried out; if not, go to step S202;
step S216, updateThe interference suffered by the user is the actual interference, and the reset is carried outThe utility function of the user is transferred to step S202;
step S217, the user n accesses the macro base station, the resource allocation is carried out on the user n and the user which is accessed to the macro base station before, the power consumption of the macro base station is updated, and the user n is selected from the user nRemoving;
step S218, judging the setWhether the air is empty or not, if not, the step S202 is carried out; if so, the process is ended.
Further, in step S202, the user n calculates the utility obtained from each small cell base station according to the received information, and calculates according to the following formula:
Unk＝μ·γk·SINRnk(13)
if a user n submits an access application to a base station k (including a macro base station) but cannot access successfully, setting Unk0; determining a preference vector of the user according to the utility function of the user; if and only if Unm＞UnkBase station mfnBase station k, indicating that user n prefers to select base station m for which it provides higher utility, obtains preference vector ψ for user nn；
The utility function of a base station (including a macro base station) is defined as:
Rnk＝SINRnk(14)
if user n cannot successfully access, setting Rnk0; referring to the determination method of the user preference vector, the base station k forms a preference vector psi of the base station side for the user applying for accessk。
Further, the decision rule of whether the user can access the base station in steps S209 and S214 is: assuming the base station is served with maximum transmit power, according to equation snk＝log2(1+SINRnk) And calculating the unit bandwidth data rate of the user, and judging whether the residual resource blocks of the base station can meet the requirements of the user according to the data rate requirement so as to judge whether the user can access the base station.
According to the technical scheme provided by the embodiment of the invention, the method for self-adaptive user association to the network based on green energy perception under the condition of ultra-dense deployment of the small cell base station in the embodiment of the invention considers the hybrid energy ultra-dense heterogeneous network scene in which the electric energy and the green energy supply power simultaneously, a dual-slope path loss model in 5G is adopted to model large-scale channel fading, and the user self-adaptively adjusts the self association strategy according to the acquisition condition and the use condition of the green energy of the base station. The simulation result verifies the good performance of the algorithm in the aspects of system energy consumption, energy efficiency and load balance.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for associating a user with a network according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a network resource optimal allocation process according to an embodiment of the present invention;
fig. 3 is a schematic flowchart illustrating a specific process of the step S2 of the embodiment of the present invention, in which the user perceives the usage of the green energy and preferentially accesses the base station with sufficient green energy;
FIG. 4 is a schematic diagram showing the variation of power consumption of a system with time in simulation;
FIG. 5 is a schematic diagram showing the relationship between the power consumption of the system and the number of users in the simulation;
FIG. 6 is a schematic diagram of a relationship between system energy efficiency and the number of users in simulation;
FIG. 7 is a schematic diagram of a variation relationship of macro base station power consumption with the number of users in simulation;
fig. 8 is a schematic diagram of the relationship between the load balancing performance of the GAAUAA, max-SINR, and POPAA schemes and the change of the number of users in the simulation.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The energy consumption of the base station is closely related to the associated load, so that an appropriate user association scheme plays an important role in improving the system energy efficiency. In a hybrid energy heterogeneous network in which electric energy and green energy are supplied simultaneously, a user association scheme for green energy perception is designed, so that more users are served by the green energy, and the method has important significance for energy conservation and resource optimization. According to the invention, under a hybrid energy supply model and a dual-slope path loss model, the existing user association scheme is improved, a weighted value and a priority are set for the base station according to the power consumption condition of the base station, and a user can sense the use condition of green energy in the association process and preferentially accesses the base station with sufficient green energy.
The present invention will be described in further detail below with reference to specific embodiments and with reference to the accompanying drawings.
Examples
The embodiment provides a method for associating users to a network under ultra-dense deployment of small cell base stations. The present embodiment is based on a hybrid energy-supplied downlink ultra-dense heterogeneous cellular communication system in which macro base stations and small cell base stations coexist, and in the system, the macro base stations are supplied with electric energy, and the small cell base stations are distributed in an ultra-dense manner and are simultaneously supplied with green energy and electric energy, wherein preferably, the green energy is generated by a solar panel.
The method for associating users with a network under ultra-dense deployment of a small cell base station in this embodiment is a Green-energy Aware Adaptive User association method (GAAUAA). Fig. 1 is a schematic flow chart of a method for associating a user with a network according to this embodiment. As shown in fig. 1, the method for associating the user to the network includes the following steps:
step S1, the base station sets and sends the weight value and priority of the base station according to the power consumption condition of the base station;
and step S2, the user perceives the use condition of the green energy according to the weight value and the priority of the base station in the association process, determines the utility function of the user, and preferentially applies for accessing the base station with sufficient green energy.
Preferably, the method for associating the user to the network may further include:
and step S01, constructing a system model and modeling the problem.
Specifically, a system model is constructed, and a command set is constructedAndrespectively representing a user set and a small cell base station set, wherein N represents the total number of users, and N represents one of the users; k represents the total number of small cell base stations and macro base stations, and K represents one of the base stations.Respectively, base station index and user index, wherein when k is equal to 1, macro base station is represented.
In this embodiment, an FI dual-slope path loss model is adopted, and the model is:
in the formula (1), d represents the distance from the base station to the user, dthAt critical distance, β is the floating intercept, α1Represents d < dthSlope of time path loss α2Represents d > dthThe slope of the path loss in time.
On the basis of constructing a system model, problem modeling is carried out, and the method specifically comprises the following steps:
firstly, the power consumption of the electric energy of the base station under the dual-slope path loss model is calculated.
The signal-to-interference-and-noise ratio between the user n and the base station k is:
wherein p isnkIndicating that base station k to base station k assume that user n occupies the full bandwidth resource of base station kThe total transmit power of user n and the actual transmit power of base station k to user n istnkDenotes the number of resource blocks allocated to user n by base station k, and T denotes the total number of resource blocks of the system. Sigma2Is gaussian white noise power; is provided withIs the sum of the interference suffered by the user n; to simplify the interference calculation process, the embodiment uses the maximum transmission power of the base station jSubstitution of actual transmission power p in equation (2)njWhen the data rate of the user calculated under the condition of maximum interference meets the requirement of the user, the actual rate can meet the requirement of the user. gnkRepresenting the channel gain between user n and base station k, including path loss and shadowing.
According to the shannon formula, the unit bandwidth data rate obtained by the user n from the base station k is:
snk＝log2(1+SINRnk) (3)
then, the actual data rate obtained by user n from base station k is:
w represents the total bandwidth. The data rate requirements of all users are rnAnd the transmit power of the base station is optimized on the condition that the actual rate of the user just meets its requirements.
Meanwhile, the hybrid energy model of the embodiment adopts a green energy collection model, and the green energy generation rate G of the base station kkIs defined as:
Gk＝Qk×Sk×yk,k≠1 (5)
wherein Q iskAnd the solar energy collection value of the solar panel configured for the base station k in unit time and unit area. Q at different times of daykIs different. SkArea of solar panel configured for base station k. y iskThe efficiency of converting solar energy into electric energy. To simplify the calculation process, it is assumed that the values are the same for all base stations.
From equations (2) and (4), the following can be obtained:
wherein, the binary variable ankIndicating a user association index, which is 1 when user n is associated to base station k, and 0 otherwise. Defining an incidence matrix AN×K＝{ank}. The total actual transmit power of base station k is therefore:
the power consumption of base station k is defined as:
Ck＝P0k+Δkpk(8)
wherein, P0kFixing the power consumption, Δ, for base station kkIs the slope of the base station k power consumption model.
Therefore, the power consumption of the electric energy of the base station k is as follows:
secondly, under the power consumption model of the electric energy of the base station, user association and resource optimization are considered jointly to carry out problem modeling.
The power consumption of the base station is closely related to the distribution of the load and bandwidth resources associated with the base station, and the energy power consumption optimization problem formulated in this embodiment is as follows:
the constraint conditions are as follows:
among the third constraint conditionsIs the maximum transmit power limit of base station k, with an optimization variable of ankAnd tnkThe two variables affect each other.
Preferably, the method for associating the user to the network in this embodiment may further include:
and step S02, optimizing and distributing network resources on the basis of the system model and the problem modeling.
Since in step S01 the problem is modeled, two interacting variables are involved, namely the optimization variable ankAnd tnkTherefore, a resource optimization sub-problem and a user association sub-problem are separately presented.
The resource optimization sub-problem optimizes the transmitting power of the base station by optimizing the bandwidth resource allocation of the base station, so that the power consumption of the base station is the lowest; when the user is associated, the utility function information of the user comprises the power consumption of the base station, and the user association process is completed under the condition of lowest power consumption. Specifically, the resource optimization sub-problem is solved by the following steps:
energy consumption is directly dependent on the total transmit power of the base station, so the energy consumption optimization problem when given a user association schemeConversion to:
by substituting equation (4) for equation (12), the above equation is equivalent to solving:
constraint conditions are as follows:
for the target function with respect to tnkSolving a second partial derivative with a value larger than 0, so that the target function is a convex function and can be solved through a Lagrangian dual function, and the corresponding Lagrangian function is
Therefore, the lagrange dual function is:
equation (16) is equivalent to solving the optimization problem:
using KKT conditions (Karush-Kuhn-Tucker), it is possible to obtain
Wherein λ is*Is aboutSo for a given λ*There is a unique correspondenceThe bandwidth resource block i divided by the user n is minimized, then
And satisfy
According toThe maximum value of lambda corresponding to user n is obtained, the maximum value of lambda corresponding to all users related to the base station k is obtained, and lambda is adjustedmaxOf which is the minimum value. The maximum number of resource blocks obtained by user n is
Will be provided withFormula (19) is substituted to solve the minimum value of lambda corresponding to user n and all the related functions of base station kThe minimum value of lambda corresponding to the user is taken as the maximum value of lambdamin。
Preferably, the present embodiment adopts a dichotomy to solve the resource optimization sub-problem. Fig. 2 is a schematic diagram illustrating a flow of performing network resource optimization allocation by using a bisection method according to this embodiment. As shown in fig. 2, the network resource optimized allocation comprises the following steps:
step S021, determining lambdaminAnd λmax；
Step S022, let λ*＝(λmin+λmax)/2，
Step S023, solving according to equation (19)
Step S024, ifThen λ*If the value is larger, the step S025 is carried out; if it is notThen λ*The value is small, and the step S026 is carried out; if it is notStep S027 is carried out;
step S025, let λmax＝λ*Step S022;
step S026, let λmin＝λ*Step S022;
there is a step of 027 which is to be performed,and allocating the optimal solution for the bandwidth resources, and ending.
Further, in the method for associating the user to the network according to the embodiment, the step S1 further includes the following steps:
in order to lead the user to preferentially select the base stations with the green energy sources for access, different priorities are set for the base stations with different energy sources
First priority χ1A base station: setting small cell base stations with green energy surplus as a first priority x1And a base station.
Second priority χ2A base station: the small cell base station (grey base station) without green energy remaining is set to the second priority χ2。
Third priority χ3A base station: the power consumption of the macro base station is set as a third priority x because the power consumption of the macro base station accounts for a larger proportion of the power consumption of the whole system3And a base station. Suppose that when no user accesses the macro base station, the macro base station becomes a sleeping node and no longer provides service for the user.
The priority setting can unload more users to the small cell base station layer, and has important effects on reducing the power consumption of the macro base station and improving the utilization rate of green energy.
γk(K ∈ K, K ≠ 1) is a weight factor of the small cell base station K, and is defined as follows:
when the power consumption of the small cell base station k is less than the green energy collection rate thereof, the smaller the power consumption, γkThe larger the value of (a), the more the utility obtained by the user increases, and the more the user tends to access the base station; when the power consumption of the small cell base station k is greater than the green energy collection rate, the greater the power consumption is, the greater gamma iskThe smaller the value of (c) is, the less the user gets the utility, and the less the user has the chance to access the base station. In order to avoidThe weight factor is negative, and an adjustment coefficient η is introduced, the value of η is adjusted according to the energy consumption of the base station and the green energy generation rate, and the adjustment rule is to ensure that the minimum value of the weight factors of all the base stations is positive.
Further, in the method for associating the user with the network in this embodiment, in the step S2, in the associating process, the user proposes an association application according to the utility obtained by the user. In order to reduce the system power consumption, the green energy is required to be fully utilized, so that the utility function obtained by the user n from the small cell base station k is defined as
Unk＝μ·γk·SINRnk,k∈K,k≠1 (23)
If a user n submits an access application to a base station k (including a macro base station) but cannot access successfully, setting Unk＝0。
The utility function defined in equation (23) considers the influence of factors such as channel quality, base station power consumption and green energy on the user association, and the user can adaptively adjust the association policy according to the utility function in the association process.
Where μ is a fixed offset value of the small cell base station.
A preference vector for the user is determined from the utility function of the user. If and only if Unm＞UnkBase station mfnBase station k, indicating that user n prefers to select base station m for which it provides higher utility, obtains preference vector ψ for user nn。
When the user submits the access application to the base station, the base station selects the user of service according to the utility function of the base station side, and the utility function of the base station k is defined as
Rnk＝SINRnk(25)
If user n cannot successfully access, setting Rnk0. Referring to the determination method of the user preference vector, the base station k forms a preference vector psi of the base station side for the user applying for accessk。
Further, fig. 3 is a specific flowchart illustrating that the user in step S2 perceives the usage of the green energy according to the weight value and priority of the base station during the association process, and preferentially accesses the base station with sufficient green energy. The user i refers to all users applying for access, the user i is different from the user n, and the user n refers to the user n with the maximum utility selected by the base station from all the users applying for access. As shown in fig. 2, the step S2 includes the following steps:
step S201, initializing a set of users to be accessedFor all users. The power consumption of the base station at the initial moment is static power consumption. Initialization AN×K＝{0}。
Step S202, a user i checks whether the utility functions of all base stations are 0, if yes, the user i does not submit the association application in the association process; if not, the user i calculates the utility obtained from each base station based on the received information and according to equations (23), (24).
Step S203, determining whether there is a first priority χ1A base station; if yes, go to step S204; if not, go to step S205;
step S204, detecting χ1Whether the utility of the base station to the user i is all 0, if yes, the step S205 is carried out; if not, go to step S206;
step S205, determine all χ2Whether the utility of the base station to the user i is all 0, if not, the step S207 is carried out; if yes, go to step S212;
step S206, according to the utility function, obtaining the user i diagonal chi1Preference vector of base station, and applying for relation to χ which is ranked first according to preference vector1A base station; step S208 is executed;
step S207, obtaining the utility according to the utility functionFamily X2Preference vector of base station, and applying for relation to χ which is ranked first according to preference vector2A base station; step S208 is executed;
step S208, the base station k forms preference vectors of all users applying for service according to the utility function of the base station k, and selects the user n with the first rank;
step S209, judge whether user n can access base station k; if not, go to step S210; if so, go to step S211;
step S210, the base station k refuses to provide service for the user, the information of access failure is fed back to the user, and the user sets the utility function provided by the base station k to 0; step S202 is executed;
step S211, the user n is selected fromRemoving the user n and all the users accessed to the base station k before, and re-executing the resource allocation process to update the actual transmitting power of the base station k; the base station k updates the weight factor according to the power consumption condition of the base station k, updates the priority according to the green energy remaining condition, and then shifts to the step S218;
in this step, the decision rule of whether the user can access the base station is as follows: on the premise of assuming that the base station serves with the maximum transmitting power, the unit bandwidth data rate of the user is calculated according to the formula (3), and whether the residual resource blocks of the base station can meet the requirements of the user is judged according to the data rate requirements, so that whether the user can access the base station is judged.
Step S212, a user i submits an access application to a macro base station;
step S213, the macro base station forms preference vectors of all users applying for service according to the utility function of the macro base station, and selects a user n with the first rank;
step S214, judging whether the user n can access the macro base station, if yes, turning to step S217, and if not, turning to step S215;
step S215, detectingWhether the utilities obtained by all the users from all the base stations are 0 or not is judged, and if yes, the step S216 is carried out; if not, go to step S202;
step S216, updateThe interference suffered by the user is the actual interference, and the reset is carried outThe utility function of the user is transferred to step S202;
step S217, the user n accesses the macro base station, the resource allocation is carried out on the user n and the user which is accessed to the macro base station before, the power consumption of the macro base station is updated, and the user n is selected from the user nRemoving;
step S218, judging the setWhether the air is empty or not, if not, the step S202 is carried out; if so, the process is ended. The method for associating the users with the network for the ultra-dense deployment of the small cell base station in this embodiment is simulated, and the effect of associating the users with the network is tested.
In simulation, a coverage area of a macro base station is selected, the coverage radius is 500m, the number of small cell base stations is 80, the positions of the small cell base stations and users are randomly deployed, the maximum transmitting power of the macro base station and the small cell base stations is 46dBm and 35dBm respectively, the total number of resource blocks of each base station is 50, the bandwidth of each resource block is 180kHz, the data rate requirement of a user with noise power of-174 dBm/Hz. is 1Mbps, the bias factors mu of the macro base station and the small cell base stations are 1 and 4 respectively, the adjustment factor η is 1, and macro base stations are randomly deployedCritical distances of the station and the small cell base station are 350m and 15m respectively, shadow fading is 6.9dB, parameters of a double-slope model are set, β is 42.1dB, α dB1Take 2.7, α2Take 3.9, the area of the solar panel is 0.75m2The efficiency of converting solar energy into electric energy is 0.46. The fixed power consumption of the macro base station is 130W, and the slope of a power consumption model is 4.7. The fixed power consumption of the small cell base station is 13.6W, and the slope of the power consumption model is 4.
Analyzing a simulation result:
the three comparison schemes include ① max-SINR scheme without power optimization and the traditional user association scheme based on the maximum signal-to-interference-and-noise ratio, ② POPAA scheme, namely power optimization algorithm, combined with a matching access algorithm, and ③ NGUAA scheme, which is the same as the GAAUAA scheme provided by the embodiment but has no algorithm for green energy supply.
FIG. 4 is a schematic diagram of the variation of power consumption of the system with time in the simulation. The number of users is 90. Since NGUAA does not have a supply of green energy, only the GAAUAA scheme of this example is compared with max-SINR, POPAA. Since the green energy generation rate of the green energy collection model is slow before 10 hours, in order to ensure that the weighting factor of each base station is a positive value at all times before 10 hours, the adjustment factor takes a value of 2 in the time interval. The green energy generation rate is faster during the time period from 11 hours to 14 hours, so the adjustment factor for this time period is set to 1. As shown in fig. 4, in the period between 8 hours and 11 hours, the green energy generation rate is low, the green energy of the base station can only support the consumption of the internal components of the base station, so the green energy perception has little influence on the power consumption of the system, and therefore, the GAAUAA scheme and the POPAA scheme have similar performance but are superior to the max-SINR scheme. The rate of green energy production increases dramatically over time, so the GAAUAA approach has a growing advantage in energy consumption.
Fig. 5, 6, 7 and 8 are simulated by using a solar energy collection model with 11 points. The adjustment factor then takes a value of 1.
FIG. 5 is a schematic diagram of the variation of power consumption of the system with the number of users in the simulation. As shown in fig. 5, the power consumption of GAAUAA is the lowest of several algorithms. When the number of users increases, more users access to the green energy base station with a longer distance, so the power consumption of the GAAUAA system tends to increase, the distance between the users and the base station is reduced due to the increase of the number of the users, the base station closer to the users can provide services for the users with smaller transmission power, and the power consumption of the POPAA system is reduced. POPAA is power optimized, so the power consumption is lower than max-SINR without power optimization. The system power consumption of the ngaua without green energy supply is the highest of the four algorithms. The system energy efficiency is the ratio of the system throughput to the power consumption.
Fig. 6 is a schematic diagram illustrating a relationship between system energy efficiency and the number of users in simulation. As shown in fig. 6, the GAAUAA scheme is significantly more energy efficient than the three comparison algorithms. In the GAAUAA scheme, because the base station resources are limited, when the number of users increases, the system throughput is reduced, and the energy efficiency of the algorithm is reduced with the increase of the number of users, but still is obviously higher than the other three comparison algorithms.
Fig. 7 is a schematic diagram of a variation relationship of macro base station power consumption with the number of users in simulation. As shown in fig. 7, since the GAAUAA scheme of this embodiment considers setting of priorities, the number of users associated with the macro base station is reduced, and thus, compared with a comparison algorithm, power consumption of the macro base station is greatly reduced. The POPAA optimizes the power consumption of the macro base station, but does not consider offloading the load of the macro base station, so the power consumption of the macro base station is in the second place. In max-SINR, the base station does not perform power optimization, so that the power consumption of the macro base station is highest.
FIG. 8 is a diagram illustrating the relationship between the load balancing performance of the GAAUAA, max-SINR, and POPAA schemes and the variation of the number of users in the simulation, as shown in FIG. 8, the GAAUAA scheme passes through the weight value αkIn the setting of (3), the user can select the base station with low power consumption in the access process, so the load of the base stationThe equalization performance is significantly better than max-SINR and POPAA. The POPAA is optimized in resources, and when the residual resources of the base station cannot meet the requirements of the user, the user can access other base stations, so the POPAA is superior to the traditional max-SINR scheme.
As can be seen from the above, the user association network method for ultra-dense deployment of small cell base stations according to the embodiment researches energy consumption and energy efficiency problems in a user association process under a dual slope path loss model aiming at characteristics of an ultra-dense heterogeneous network supplied by hybrid energy, establishes an energy consumption optimization model, provides a green energy perception adaptive user association algorithm, simulates algorithm performance through matlab, and compares the algorithm performance with other algorithms. Simulation results show that the performance of the user association algorithm based on green energy perception in the ultra-dense heterogeneous network is obviously superior to that of other comparison algorithms in the aspects of system power consumption, energy efficiency, macro base station power consumption, load balance and the like.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of ordinary skill in the art will understand that: the components in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be correspondingly changed in one or more devices different from the embodiments. The components of the above embodiments may be combined into one component, or may be further divided into a plurality of sub-components.
The above description is only for the preferred embodiment 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. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (7)

1. A method for associating users to a network under ultra-dense deployment of small cell base stations is characterized by comprising the following steps:
step S1, the base station sets and sends the weight value and priority of the base station according to the power consumption condition of the base station;
and step S2, the user perceives the use condition of the green energy according to the weight value and the priority of the base station in the association process, determines the utility function of the user, and preferentially applies for accessing the base station with sufficient green energy.
2. The method of associating a user to a network of claim 1, the method further comprising: step S01, adopting an FI dual-slope path loss model, wherein the model is as follows:
in the formula (1), d represents the distance from the base station to the user, dthAt critical distance, β is the floating intercept, α1Represents d < dthSlope of time path loss α2Represents d > dthThe path loss slope of time;
the energy consumption problem model is constructed as follows:
the constraint conditions are as follows:
among them, the third restrictionIs the maximum transmit power limit of base station k, with an optimization variable of ankAnd tnk。
3. The method for associating a user to a network according to claim 1, wherein in step S1, setting the priority of the base station further comprises:
setting different priorities for base stations of different energy sourcesWherein,
first priority χ1A base station: setting small cell base stations with green energy surplus as a first priority x1A base station;
second priority χ2A base station: the small cell base station (grey base station) without green energy remaining is set to the second priority χ2；
Third priority χ3A base station: setting the macro base station as a third priority χ3A base station; when no user accesses the macro base station, the macro base station is a dormant node and no longer provides service for the user.
4. The method for associating users to the network as claimed in claim 1, wherein in step S1, the weight value of the base station is set by setting the weight factor of the base station, and the weight factor of the small cell base station k is αk：
Wherein, CkRepresenting the power consumption of base station k, GkRepresents the green energy generation rate of base station k;
adjusting the value of η according to the energy consumption of the base stations and the green energy generation rate by an adjustment coefficient η, wherein the adjustment rule is to ensure that the minimum value of the weight factors of all the base stations is a positive number.
5. The method for associating a user to a network according to claim 1, wherein the step S2 further comprises:
step S201, initializing a set of users to be accessedAll users are selected; the power consumption of the base station at the initial moment is static power consumption; initialization AN×K＝{0}；
Step S202, a user i checks whether the utility functions of all base stations are 0, if yes, the user i does not submit the association application in the association process; if not, the user i calculates the utility obtained from each base station according to the received information;
step S203, judging whether a first priority existsχ1A base station; if yes, go to step S204; if not, go to step S205;
step S204, detecting χ1Whether the utility of the base station to the user i is all 0, if yes, the step S205 is carried out; if not, go to step S206;
step S205, determine all χ2Whether the utility of the base station to the user i is all 0, if not, the step S207 is switched to; if yes, go to step S212;
step S206, according to the utility function, obtaining the user i diagonal chi1Preference vector of base station, and applying for relation to χ which is ranked first according to preference vector1A base station; step S208 is executed;
step S207, obtaining a user X according to the utility function2Preference vector of base station, and applying for relation to χ which is ranked first according to preference vector2A base station; step S208 is executed;
step S208, the base station k forms preference vectors of all users applying for service according to the utility function of the base station k, and selects the user n with the first rank;
step S209, judge whether user n can access base station k; if not, go to step S210; if so, go to step S211;
step S210, the base station k refuses to provide service for the user, the information of access failure is fed back to the user, and the user sets the utility function provided by the base station k to 0; step S202 is executed;
step S211, the user n is selected fromRemoving the user n and all the users accessed to the base station k before, and re-executing the resource allocation process to update the actual transmitting power of the base station k; the base station k updates the weight factor according to the power consumption condition of the base station k, updates the priority according to the green energy remaining condition, and then shifts to the step S218;
step S212, a user i submits an access application to a macro base station;
step S213, the macro base station forms preference vectors of all users applying for service according to the utility function of the macro base station, and selects a user n with the first rank;
step S214, judging whether the user n can access the macro base station, if yes, turning to step S217, and if not, turning to step S215;
step S215, detectingWhether the utilities obtained by all the users from all the base stations are 0 or not is judged, and if yes, the step S216 is carried out; if not, go to step S202;
step S216, updateThe interference suffered by the user is the actual interference, and the reset is carried outThe utility function of the user is transferred to step S202;
step S217, the user n accesses the macro base station, the resource allocation is carried out on the user n and the user which is accessed to the macro base station before, the power consumption of the macro base station is updated, and the user n is selected from the user nRemoving;
step S218, judging the setWhether the air is empty or not, if not, the step S202 is carried out; if so, the process is ended.
6. The method according to claim 5, wherein the user n in step S202 calculates the utility obtained from each small cell base station according to the received information, and the calculation is performed according to the following formula:
Unk＝μ·γk·SINRnk(13)
if the user n submits an access application to the base station k (including the macro base station), the access application cannot be successfully accessedWhen it is time, set Unk0; determining a preference vector of the user according to the utility function of the user; if and only if Unm＞UnkBase station mfnBase station k, indicating that user n prefers to select base station m for which it provides higher utility, obtains preference vector ψ for user nn；
The utility function of a base station (including a macro base station) is defined as:
Rnk＝SINRnk(14)
if user n cannot successfully access, setting Rnk0; referring to the determination method of the user preference vector, the base station k forms a preference vector psi of the base station side for the user applying for accessk。
7. The method according to claim 5, wherein the decision rule of whether the user can access the base station in steps S209 and S214 is: assuming the base station is served with maximum transmit power, according to equation snk＝log2(1+SINRnk) And calculating the unit bandwidth data rate of the user, and judging whether the residual resource blocks of the base station can meet the requirements of the user according to the data rate requirement so as to judge whether the user can access the base station.
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