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

Abstract

The present invention provides a method for users to associate with a network under ultra-dense deployment of small cell base stations, which is used to improve the energy efficiency of a hybrid energy heterogeneous network under ultra-dense deployment. The method includes: first, the base station sets and sends the weight value and priority of the base station according to its own power consumption; secondly, the user perceives the usage of green energy during the association process, and preferentially accesses the base station with sufficient green energy. This embodiment considers the hybrid energy super-dense heterogeneous network scenario where electric energy and green energy are simultaneously powered, and uses the dual-slope path loss model in 5G to model large-scale channel fading. The association strategy is adaptively adjusted, and the present embodiment combines matching theory to optimize resource allocation and base station transmission power in the association process, effectively reducing power consumption and improving energy efficiency.

Description

Method for user association to network under ultra-dense deployment of small cell base stations

technical field

The present invention relates to the technical field of communication networks, in particular to a method for associating users with a network under ultra-dense deployment of small cell base stations.

Background technique

With the development of computer and electronic technology, communication networks have become ubiquitous, and they are also loaded with huge data traffic. According to speculation, the data traffic in 2020 will be 250 times that of 2010, which poses a huge challenge to the existing communication system. The fifth generation (5G) communication system has attracted the attention of the information and communication industry. The IMT-2020 promotion group focuses on promoting the research of key technologies in 5G. High capacity, low energy consumption, and low latency are the communication requirements in 5G. In order to realize 5G communication, ultra-dense deployment of small cell base stations in macro base stations is required. Ultra-dense deployment of small cell base stations will inevitably increase network energy consumption. Heterogeneous Network. Ultra-dense heterogeneous network is one of the key technologies to realize 5G communication requirements.

The annual growth rate of energy consumption brought by information and communication technology has reached 15%-20%. The huge energy consumption generated by ultra-densely deployed SCBSs (Small Cell Base Stations) becomes a challenging issue that should be properly addressed to effectively exploit the potential of ultra-dense heterogeneous networks. The increase in energy consumption of base stations has led to a large amount of greenhouse gas emissions such as carbon dioxide, which has a bad impact on the environment. In order to comply with the strategic goal of green and sustainable development, "green communication" has been recognized by academic circles at home and abroad, and is one of the core goals of 5G. The main goal of the 5G green cellular network is to continuously improve the energy efficiency of the network while meeting the increasing data traffic of users, reduce the energy consumption of the system, and achieve the purpose of reducing global carbon emissions.

In the prior art, most of the research focuses on combining user association and power optimization to reduce system energy consumption, but the effect of reducing energy consumption only through power optimization is not obvious, and traditional electric energy (hereinafter referred to as The mixed energy mode of simultaneously supplying electric energy) and green energy realizes sustainable development. At the same time, most of the existing heterogeneous networks use a single-slope path loss model to represent the path loss between the user and the base station. Although the research and analysis of the single path loss model is relatively easy, with the diversification of base station types and densification of base stations, the traditional single slope path loss model is no longer accurate. Existing work has begun to study the dual-slope path loss model.

Under the hybrid energy heterogeneous network, there is no feasible solution for how to realize the saving of power consumption and the full utilization of green energy for the user-to-network connection in the double-slope path loss model.

Contents of the invention

In order to improve the energy efficiency of hybrid energy heterogeneous networks under ultra-dense deployment, the present invention provides a method for adaptive user association to the network based on green energy perception under ultra-dense deployment of small cell base stations.

In order to achieve the above object, the present invention adopts the following technical solutions.

An embodiment of the present invention provides a method for associating a user with a network under ultra-dense deployment of small cell base stations, the method including the following steps:

Step S1, the base station sets and sends the weight value and priority of the base station according to its own power consumption;

Step S2, during the association process, the user perceives the usage of green energy according to the weight value and priority of the base station, determines the utility function of the user, and preferentially applies for access to the base station with sufficient green energy.

Further, the method further includes: step S01, adopting the FI double-slope path loss model, the model of which is:

In formula (1), d represents the distance from the base station to the user, d _th is the critical distance, β is the floating intercept, α ₁ represents the path loss slope when d<d _th ; α ₂ represents the path loss when d>d _th slope;

Construct the energy consumption problem model as:

The constraints are:

Among them, the third constraint is the maximum transmit power limit of base station k, and the optimization variables are a _nk and t _nk .

Further, in the step S1, setting the priority of the base station further includes:

Set different priorities for base stations with different energy sources in,

The first priority χ ₁ base station: the small cell base station with remaining green energy is set as the first priority χ ₁ base station;

The second priority χ ₂ base station: the small cell base station (gray base station) without green energy remaining is set as the second priority χ ₂ ;

The third priority χ ₃ base station: set the macro base station as the third priority χ ₃ base station; when no user accesses the macro base station, the macro base station is a dormant node and no longer provides services for users.

Further, in the step S1, the weight value of the base station is set, which is realized by setting the weight factor of the base station, and the weight factor of the small cell base station k is α _k :

Among them, C _k represents the power consumption of base station k, and G _k represents the green energy generation rate of base station k;

Adjust the coefficient η; adjust the value of η 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 factor of all base stations is a positive number.

Further, the step S2 further includes:

Step S201, initialize the user set to be accessed For all users; the power consumption of the base station at the initial moment is static power consumption; initialization A _N×K = {0};

Step S202, user i checks whether the utility function of each base station is 0, if yes, user i does not submit an association application in this association process; if not, user i calculates the utility function obtained from each base station according to the received information utility;

Step S203, judging whether there is a first priority x ₁ base station; if yes, then proceed to step S204; if not, then proceed to step S205;

Step S204, detect whether the utility of x ₁ base station to user i is all 0, if yes, then proceed to step S205; if not, then proceed to step S206;

Step S205, judge whether all χ ₂ base stations are all 0 to the utility of user i, if not, then proceed to step S207; if yes, then proceed to step S212;

Step S206, according to the utility function, obtain the preference vector of user i to χ1 base station, and apply for association with the _{first -} ranked χ1 base station according to the preference vector; go to step S208;

Step S207, according to the utility function, obtain the preference vector of the user to the χ ₂ base station, and apply for association with the first-ranked χ ₂ base station according to the preference vector; go to step S208;

Step S208, base station k forms preference vectors of all users applying for the service according to its own utility function, and selects user n ranked first;

Step S209, judging whether user n can access base station k; if not, proceed to step S210; if yes, proceed to step S211;

In step S210, base station k refuses to provide services for the user, and feeds back information about access failure to the user, and the user sets the utility function provided by base station k to 0; go to step S202;

Step S211, user n from remove the resource allocation process of user n and all users who previously accessed base station k, and update the actual transmission power of base station k; base station k updates the weight factor according to its own power consumption, and updates it according to the remaining green energy Priority, proceed to step S218;

Step S212, user i submits an access application to the macro base station;

Step S213, the macro base station forms preference vectors of all users applying for the service according to its own utility function, and selects the user n ranked first;

Step S214, judging whether user n can access the macro base station, if yes, then proceed to step S217, if not then proceed to step S215;

Step S215, detecting Whether the utility obtained by all users from all base stations in is 0, if yes, then proceed to step S216; if not, then proceed to step S202;

Step S216, update The interference received by the user is the actual interference, reset In the user's utility function, turn to step S202;

Step S217, user n accesses the macro base station, re-allocates resources between user n and users who have previously accessed the macro base station, updates the power consumption of the macro base station, and transfers user n from remove from

Step S218, judging the set Whether it is empty, if not, go to step S202; if yes, end.

Further, in the 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:

U _nk ＝ μ · γ _k · SINR _nk (13)

If user n submits an access application to base station k (including macro base station), but fails to access successfully, set U _nk =0; determine the user’s preference vector according to the user’s utility function; if and only if U _nm >U _nk , base station mf _n and base station k, which means that user n is more inclined to choose the base station m that provides higher utility for it, so as to obtain the preference vector ψ _{n of user n} ;

The utility function of the base station (including the macro base station) is defined as:

R _nk = SINR _nk (14)

If user n fails to access successfully, set R _nk =0; referring to the determination method of user preference vector, base station k forms a base station-side preference vector ψ _k for users who apply for access.

Further, in the steps S209 and S214, the determination rule for whether the user can access the base station is as follows: on the premise that the base station serves with the maximum transmission power, calculate the unit of the user according to the formula s _nk =log ₂ (1+SINR _nk ) Bandwidth data rate. According to the data rate requirement, it is judged whether the remaining resource blocks of the base station can meet the needs of the user, so as to determine whether the user can access the base station.

It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the green energy-aware-based adaptive user association method for the network under the ultra-dense deployment of small cell base stations in the embodiments of the present invention considers the mixed power supply of electric energy and green energy at the same time. In the energy-intensive heterogeneous network scenario, the dual-slope path loss model in 5G is used to model large-scale channel fading, and users adaptively adjust their own association strategies according to the collection and use of green energy in the base station. Combining matching theory to optimize resource allocation and base station transmission power in the association process, effectively reducing power consumption and improving energy efficiency. The simulation results verify the good performance of the algorithm in terms 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 will become apparent from the description, or may be learned by practice of the invention.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

FIG. 1 is a schematic flow chart of a method for a user to associate with a network according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an optimal allocation process of network resources according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a specific flow of the user's perception of the use of green energy in step S2 of the embodiment of the present invention to preferentially access a base station with sufficient green energy;

Fig. 4 is a schematic diagram of the relationship of system power consumption with time in the simulation;

Figure 5 is a schematic diagram of the relationship between system power consumption and the number of users in the simulation;

Figure 6 is a schematic diagram of the relationship between system energy efficiency and the number of users in the simulation;

Figure 7 is a schematic diagram of the relationship between the power consumption of the macro base station and the number of users in the simulation;

Figure 8 is a schematic diagram of the relationship between the load balancing performance and the number of users of the GAAUAA, max-SINR, and POPAA schemes in the simulation.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and/or components, but does not exclude 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. Additionally, "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.

Those skilled in the art can understand 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 should also be understood that terms such as those defined in commonly used dictionaries should be understood to have a meaning consistent with the meaning in the context of the prior art, and unless defined as herein, are not to be interpreted in an idealized or overly formal sense explain.

In order to facilitate the understanding of the embodiments of the present invention, several specific embodiments will be taken as examples for further explanation below in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

The energy consumption of a base station is closely related to its associated load, so an appropriate user association scheme plays an important role in improving system energy efficiency. In a hybrid energy heterogeneous network powered by electric energy and green energy at the same time, designing a green energy-aware user association scheme to enable more users to be served by green energy is of great significance for energy conservation and resource optimization. The present invention improves the existing user association scheme under the mixed energy supply model and the double-slope path loss model, and sets the weight value and priority for it according to the power consumption of the base station, so that the user can perceive the green energy in the association process In terms of usage, priority is given to accessing base stations with sufficient green energy.

The present invention will be described in further detail below through specific embodiments and in conjunction with the accompanying drawings.

Example

This embodiment provides a method for associating a user with a network under ultra-dense deployment of small cell base stations. This embodiment is based on a downlink ultra-dense heterogeneous cellular communication system supplied by mixed energy in which macro base stations and small cell base stations coexist. Energy is supplied at the same time, wherein, preferably, green energy is generated by solar panels.

In this embodiment, the method for user association to the network under ultra-dense deployment of small cell base stations is a green-energy-aware adaptive user association method (GAAUAA, Green-energy Aware Adaptive User Association Algorithm). FIG. 1 is a schematic flowchart of a method for associating a user with a network according to this embodiment. As shown in Figure 1, the method for the user to associate with 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 its own power consumption;

Step S2, during the association process, the user perceives the usage of green energy according to the weight value and priority of the base station, determines the utility function of the user, and preferentially applies for access to the base station with sufficient green energy.

Preferably, the method for the user to associate with the network may also include:

Step S01, constructing a system model and performing problem modeling.

Specifically, construct the system model, let the set and Represents the user set and the small cell base station set respectively, 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. Denote the base station index and the user index respectively, where when k is equal to 1, it represents the macro base station.

This embodiment adopts the FI double-slope path loss model, and its model is:

In formula (1), d represents the distance from the base station to the user, d _th is the critical distance, β is the floating intercept, α ₁ represents the path loss slope when d<d _th ; α ₂ represents the path loss when d>d _th slope.

On the basis of constructing the system model, carry out problem modeling, the specific steps are as follows:

First, calculate the power energy consumption of the base station under the dual-slope path loss model.

The SINR between user n and base station k is:

Among them, p _nk represents the total transmission power from base station k to user n when user n occupies all the bandwidth resources of base station k, and the actual transmission power from base station k to user n is t _nk represents the number of resource blocks allocated by base station k to user n, and T represents the total number of resource blocks in the system. σ ² is Gaussian white noise power; let is the sum of interference received by user n; in order to simplify the interference calculation process, this embodiment uses the maximum transmission power of base station j Instead of the actual transmit power p _nj in formula (2), when the calculated data rate of the user meets the user's requirement under the maximum interference condition, the actual rate must meet the user's requirement. g _nk represents the channel gain between user n and base station k, including path loss and shadow fading.

According to the Shannon formula, the unit bandwidth data rate obtained by user n from base station k is:

s _nk =log ₂ (1+SINR _nk ) (3)

Then, the actual data rate obtained by user n from base station k is:

W represents the total bandwidth. The data rate requirement of all users is r _n , and the transmit power of the base station is optimized under the condition that the user's actual rate just meets its requirement.

At the same time, the mixed energy model of this embodiment adopts the green energy acquisition model, and the green energy generation rate Gk of the base station _k is defined as:

G _k ＝Q _k ×S _k ×y _k , k≠1 (5)

Among them, Q _k is the solar energy collection value per unit time and unit area of the solar panel configured by base station k. Q _k is different at different times of the day. S _k is the area of the solar panel configured by the base station k. y _k is the efficiency of converting solar energy into electrical energy. To simplify the calculation process, it is assumed that the above values are the same for all base stations.

According to formula (2) and formula (4), it can be obtained:

Among them, the binary variable a _nk represents a user association index, and its value is 1 when user n is associated with base station k, otherwise it is 0. Define an association matrix A _N×K ={a _nk }. So the total actual transmit power of base station k is:

The power consumption of base station k is defined as:

C _k ＝ P _0k +Δ _k p _k (8)

Among them, P _0k is the fixed power consumption of base station k, and Δ _k is the slope of the power consumption model of base station k.

Therefore, the power consumption of base station k is:

Secondly, under the power consumption model of the base station, user association and resource optimization are jointly considered to model the problem.

The power consumption of the base station is closely related to its associated load and allocation of bandwidth resources. The energy consumption optimization problem formulated in this embodiment is:

The constraints are:

Among them, in the third constraint is the maximum transmission power limit of base station k, the optimization variables are a _nk and t _nk , and the two variables affect each other.

Preferably, the method for a user to associate with a network in this embodiment may also include:

Step S02, based on the system model and problem modeling, optimize the allocation of network resources.

Since the modeling of the problem in step S01 involves two mutually influencing variables, ie optimization variables a _nk and t _nk , a resource optimization sub-problem and a user association sub-problem are proposed respectively.

The resource optimization sub-problem optimizes the transmit power of the base station by optimizing the bandwidth resource allocation of the base station, so that the power consumption of the base station reaches the minimum; when users are associated, the utility function information of the user includes the power consumption of the base station, and the power consumption of the base station is the lowest to complete the user association process. Specifically, the steps to solve the resource optimization sub-problem are as follows:

Energy consumption directly depends on the total transmit power of the base station, so when a user association scheme is given, the energy consumption optimization problem transform into:

Substituting formula (4) into formula (12), the above formula is equivalent to solving:

Restrictions:

Calculate the second partial derivative of the objective function with respect to t _nk , its value is greater than 0, so the objective function is a convex function, which can be solved by the Lagrangian dual function, and the corresponding Lagrangian function is

So, the Lagrangian dual function is:

Equation (16) is equivalent to solving the optimization problem:

Using KKT conditions (Karush-Kuhn-Tucker), we can get

where λ ^* is about A monotonically decreasing function of , so for a given λ ^* , there exists a unique corresponding Make the bandwidth resource block i allocated by user n the smallest, then

and satisfied

according to Find the maximum value of λ corresponding to user n, find the maximum value of λ corresponding to all users associated with base station k, and make λ _max the minimum value among them. The maximum number of resource blocks obtained by user n is

Will Substitute into equation (19) to find the minimum value of λ corresponding to user n, find the minimum value of λ corresponding to all users associated with base station k, and take the maximum value as λ _min .

Preferably, this embodiment uses a dichotomy method to solve the resource optimization sub-problem. FIG. 2 is a schematic diagram of a process for optimally allocating network resources using the dichotomy method in this embodiment. As shown in Figure 2, the optimal allocation of network resources includes the following steps:

Step S021, determining λ _min and λ _max ;

Step S022, set λ ^* = (λ _min + λ _max )/2,

Step S023, solve according to formula (19)

Step S024, if Then the value of λ ^* is larger, turn to step S025; if Then the value of λ ^* is smaller, and then go to step S026; if Then proceed to step S027;

Step S025, set λ _max = λ ^* , turn to step S022;

Step S026, set λ _min = λ ^* , turn to step S022;

Step to 027, Allocate the optimal solution for bandwidth resources, end.

Further, in the method for linking a user to a network in this embodiment, the step S1 further includes the following steps:

In order to allow users to preferentially select base stations with remaining green energy for access, different priorities are set for base stations with different energy sources

The first priority χ ₁ base station: the small cell base station with remaining green energy is set as the first priority χ ₁ base station.

The second priority χ ₂ base station: the small cell base station (gray base station) with no remaining green energy is set as the second priority χ ₂ .

The third priority χ ₃ base station: Since the power consumption of the macro base station accounts for a large proportion in the power consumption of the entire system, it is set as the third priority χ ₃ base station. It is assumed that when no user accesses the macro base station, the macro base station will become a dormant node and no longer provide services for users.

The priority setting can offload more users to the small cell base station layer, which plays an important role in reducing the power consumption of the macro base station and improving the utilization rate of green energy.

γ _k (k∈K, k≠1) is the weight factor of small cell base station k, defined as follows:

When the power consumption of the small cell base station k is less than its green energy collection rate, the smaller the power consumption, the greater the value of γ _k , and the utility obtained by the user will increase accordingly, and the user is more inclined to access this base station; when When the power consumption of the small cell base station k is greater than the green energy collection rate, the greater the power consumption, the smaller the value of γ _k will be, and the utility obtained by the user will decrease, and the chance of the user to access this base station will also decrease. In order to prevent the weight factor from being negative, 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 factor of all base stations is a positive number.

Further, in the method for associating a user with a network in this embodiment, in the step S2, during the associating process, the user submits an associating application according to the utility obtained by the user. In order to reduce the power consumption of the system, it is necessary to make full use of green energy, so the utility function obtained by user n from small cell base station k is defined as

U _nk ＝μ·γ _k SINR _nk ,k∈K,k≠1 (23)

If the user n submits an access application to the base station k (including the macro base station) but fails to access successfully, U _nk =0 is set.

The utility function defined in Equation (23) takes into account the influence of factors such as channel quality, base station power consumption, and green energy on user association, and users can adaptively adjust the association strategy according to the utility function during the association process.

Wherein, μ is a fixed offset value of the small cell base station.

The user's preference vector is determined according to the user's utility function. If and only if Un _nm >U _nk , base station mf _nbase station k means that user n is more inclined to choose the base station m that provides higher utility for it, thus obtaining the preference vector ψ _{n of user n} .

When a user submits an access application to the base station, the base station will select the serving user according to the utility function of the base station side, and the utility function of the base station k is defined as

R _nk = SINR _nk (25)

If user n cannot successfully access, set R _nk =0. Referring to the method for determining the user preference vector, the base station k forms a base station-side preference vector ψ _k for users who apply for access.

Further, FIG. 3 is a schematic diagram of the specific flow of the user in step S2 to perceive the usage of green energy according to the weight value and priority of the base station during the association process, and preferentially access the base station with sufficient green energy. Wherein, user i refers to all users applying for access, user i is different from user n, and user n refers to user n selected by the base station with the greatest utility from all users applying for access. As shown in Figure 2, the step S2 includes the following steps:

Step S201, initialize the user set to be accessed for all users. The power consumption of the base station at the initial moment is static power consumption. Initialize A _N×K ={0}.

Step S202, user i checks whether the utility function of each base station is 0, if yes, then user i will not submit an association application in this association process; if not, user i according to the received information and according to formula (23) , (24) Calculate utility obtained from each base station.

Step S203, judging whether there is a first priority x ₁ base station; if yes, then proceed to step S204; if not, then proceed to step S205;

Step S204, detect whether the utility of x ₁ base station to user i is all 0, if yes, then proceed to step S205; if not, then proceed to step S206;

Step S205, judge whether the utility of all x ₂ base stations to user i is all 0, if otherwise proceed to step S207; if yes, then proceed to step S212;

Step S206, according to the utility function, obtain the preference vector of user i to χ1 base station, and apply for association with the _{first -} ranked χ1 base station according to the preference vector; go to step S208;

Step S207, according to the utility function, obtain the preference vector of the user to the χ ₂ base station, and apply for association with the first-ranked χ ₂ base station according to the preference vector; go to step S208;

Step S208, base station k forms preference vectors of all users applying for the service according to its own utility function, and selects user n ranked first;

Step S209, judging whether user n can access base station k; if not, proceed to step S210; if yes, proceed to step S211;

In step S210, base station k refuses to provide services for the user, and feeds back information about access failure to the user, and the user sets the utility function provided by base station k to 0; go to step S202;

Step S211, user n from remove the resource allocation process of user n and all users who previously accessed base station k, and update the actual transmission power of base station k; base station k updates the weight factor according to its own power consumption, and updates it according to the remaining green energy Priority, proceed to step S218;

In this step, the determination rule for whether a user can access the base station is as follows: on the premise that the base station serves with the maximum transmission power, calculate the unit bandwidth data rate of the user according to formula (3), and judge the remaining bandwidth of the base station according to the data rate requirement Whether the resource block can meet the needs of the user is used to determine whether the user can access the base station.

Step S212, user i submits an access application to the macro base station;

Step S213, the macro base station forms preference vectors of all users applying for the service according to its own utility function, and selects the user n ranked first;

Step S214, judging whether user n can access the macro base station, if yes, then proceed to step S217, if not then proceed to step S215;

Step S215, detecting Whether the utility obtained by all users from all base stations in is 0, if yes, then proceed to step S216; if not, then proceed to step S202;

Step S216, update The interference received by the user is the actual interference, reset In the user's utility function, turn to step S202;

Step S217, user n accesses the macro base station, re-allocates resources between user n and users who have previously accessed the macro base station, updates the power consumption of the macro base station, and transfers user n from remove from

Step S218, judging the set Whether it is empty, if not, go to step S202; if yes, end. A simulation is performed on the method for user association to the network in the ultra-dense deployment of small cell base stations in this embodiment to test the effect of the user association network.

In the simulation, the coverage area of a macro base station is selected, and the coverage radius is 500m. The number of small cell base stations is 80, and the locations of small cell base stations and users are randomly deployed. The maximum transmit power of the macro base station and the small cell base station are 46dBm and 35dBm respectively. Each base station has 50 resource blocks in total, and the bandwidth of each resource block is 180 kHz. The noise power is -174dBm/Hz. The user's data rate requirement is 1Mbps. The offset factors μ of the macro base station and the small cell base station are 1 and 4 respectively, and the adjustment factor η is 1. The critical distances of macro base stations and small cell base stations are 350m and 15m respectively. Shadow fade is 6.9dB. Set the parameters of the double-slope model, β is 42.1dB, α ₁ is 2.7, α ₂ is 3.9, the area of the solar panel is 0.75m ² , and the 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 the 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.

Analyze the simulation results:

In the performance simulation part, the user association network method GAAUAA in this embodiment is simulated and compared with three comparison schemes. Three comparison schemes include: ①max-SINR scheme, no power optimization, traditional user association scheme based on maximum SINR; ②POPAA scheme, power optimization algorithm combined with matching access algorithm; ③ NGUAA scheme, and this implementation The GAAUAA scheme proposed in the example is related to the same situation, but there is no algorithm for green energy supply.

Fig. 4 is a schematic diagram of the relationship of system power consumption with time in the simulation. The number of users is 90. Since NGUAA has no supply of green energy, only the GAAUAA scheme of this embodiment is compared with max-SINR and POPAA. Since the green energy collection model produces slow green energy before 10:00, in order to ensure that the weight factor of each base station is positive at all times before 10:00, the adjustment factor takes a value of 2 during this period. From 11:00 to 14:00 In , the green energy generation rate is faster, so the adjustment factor during this period is set to 1. As shown in Figure 4, during the period between 8 o'clock and 11 o'clock, the green energy generation rate is low, and 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 impact on the power consumption of the system, so The performance of the GAAUAA scheme and the POPAA scheme is similar, but both are better than the max-SINR scheme. With the passage of time, the rate of green energy generation has increased sharply, so the advantages of the GAAUAA scheme in terms of energy consumption have gradually become prominent.

Figure 5, Figure 6, Figure 7, and Figure 8 all use the 11-point solar energy collection model for simulation. At this time, the adjustment factor takes a value of 1.

Fig. 5 is a schematic diagram of the relationship between the power consumption of the system and the number of users in the simulation. As shown in Fig. 5, the power consumption of GAAUAA is the lowest among several algorithms. When the number of users increases, more users will connect to green energy base stations that are farther away, so the power consumption of the GAAUAA system will increase, and the increase in the number of users will reduce the distance between users and the base station, and the distance between users will be closer. Near base stations can provide services to users with less transmission power, so the system power consumption of POPAA is reduced. POPAA has been optimized for power, so its power consumption is lower than max-SINR without power optimization. The system power consumption of NGUAA without green energy supply is the highest among the four algorithms. System energy efficiency is the ratio of system throughput to electrical energy consumption.

Figure 6 is a schematic diagram of the relationship between system energy efficiency and the number of users in the simulation. As shown in Figure 6, the energy efficiency of the GAAUAA scheme is significantly higher than that of the three comparison algorithms. In the GAAUAA scheme, due to limited base station resources, when the number of users increases, the system throughput decreases, and the energy efficiency of the algorithm decreases with the increase of the number of users, but it is still significantly higher than the other three comparison algorithms.

FIG. 7 is a schematic diagram of the relationship between the power consumption of the macro base station and the number of users in the simulation. As shown in FIG. 7 , since the GAAUAA scheme of this embodiment takes priority setting into account, the number of users associated with the macro base station is reduced, and therefore, compared with the comparison algorithm, the power consumption of the macro base station is greatly reduced. POPAA optimizes the power consumption of the macro base station, but does not consider unloading 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 the power consumption of the macro base station is the highest.

Figure 8 is a schematic diagram of the relationship between the load balancing performance and the number of users of the GAAUAA, max-SINR, and POPAA schemes in the simulation. As shown in Figure 8, the GAAUAA scheme sets the weight value α _k , and the user will choose the base station with low power consumption during the access process, so its load balancing performance is significantly better than max-SINR and POPAA. POPAA has optimized resources. When the remaining resources of the base station cannot meet the user's needs, the user will access other base stations. Therefore, POPAA is superior to the traditional max-SINR scheme.

It can be seen from the above that the user association network method for ultra-dense deployment of small cell base stations in this embodiment, according to the characteristics of ultra-dense heterogeneous networks with hybrid energy supply, studies the user association process under the double-slope path loss model. For energy consumption and energy efficiency issues, an energy consumption optimization model was established, and an adaptive user association algorithm for green energy perception was proposed, and the performance of the algorithm was simulated by matlab, and compared with other algorithms. The simulation results show that the user association algorithm based on green energy perception in the ultra-dense heterogeneous network of this embodiment is significantly better than other comparative algorithms in terms of system power consumption, energy efficiency, macro base station power consumption, and load balance.

Those skilled in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary for implementing the present invention.

Each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for relevant parts, refer to part of the description of the method embodiments. The device and system embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, It can be located in one place, or it can be distributed to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without creative effort.

Those of ordinary skill in the art can understand that: the components in the device in the embodiment can be distributed in the device in the embodiment according to the description in the embodiment, and can also be changed and located in one or more devices different from the embodiment. The components in the above embodiments can be combined into one component, and can also be further divided into multiple subcomponents.

The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present invention can easily think of changes or Replacement should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by 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, d_thAt critical distance, β is the floating intercept, α₁Represents d < d_thSlope of time path loss α₂Represents d > d_thThe 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 a_nkAnd t_nk。

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 α_k：

Wherein, C_kRepresenting the power consumption of base station k, G_kRepresents 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 A_N×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:

U_nk＝μ·γ_k·SINR_nk(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 U_nk0; determining a preference vector of the user according to the utility function of the user; if and only if U_nm＞U_nkBase station mf_nBase station k, indicating that user n prefers to select base station m for which it provides higher utility, obtains preference vector ψ for user n_n；

The utility function of a base station (including a macro base station) is defined as:

R_nk＝SINR_nk(14)

if user n cannot successfully access, setting R_nk0; 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 access_k。

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 s_nk＝log₂(1+SINR_nk) 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.