CN108365900A - User access method based on energy consumption and pairing in super-intensive heterogeneous network system - Google Patents

Abstract

The invention provides a user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system. The method includes: calculating the utility function value of each base station on the user side for the user according to the SINR value between the user and each base station, and the user sends an access application to the base station with the highest utility function value to the user, and the base station will receive from the base station The SINR value extracted from the access application is used as the utility function value of the user who sends the access request on the base station side. The base station judges whether the user with the highest utility function value can access the network according to the rate request and interference situation of the user with the highest utility function value on the base station side. If so, feed back the information of successful access to the user with the highest utility function value; otherwise, feed back the information of access failure to the user with the highest utility function value. The present invention proposes a user access algorithm used in an ultra-dense deployment scenario, and solves the problem of user access based on energy efficiency optimization under a given target rate condition.

Description

User access method based on energy consumption and pairing in ultra-dense heterogeneous network system

technical field

The invention relates to the technical field of wireless network communication, in particular to a user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system.

Background technique

With the advancement of mobile communication technology, the widespread use of smart phones, the rapid development of mobile Internet, Internet of Things and various new services, the number of UEs (User Equipment, user terminals) and mobile data traffic have experienced explosive growth. Dense deployment of heterogeneous networks will become an inevitable trend in the future development of mobile communications. The ultra-dense deployment of small cells will inevitably lead to increased network energy consumption and unbalanced base station loads. The energy consumption of the base station is closely related to the number of loads associated with the base station, so a suitable user access algorithm plays an important role in reducing the energy consumption of the base station and improving the fairness of user access.

Currently, user access algorithms in the prior art pay more attention to load balancing and system energy consumption. A user access algorithm in the prior art includes: under the condition of meeting user data rate requirements, an algorithm for joint power and resource allocation is proposed with the goal of optimizing base station transmit power. The disadvantage of this user access algorithm is that it does not effectively represent and optimize the total transmission power of the base station.

Contents of the invention

Embodiments of the present invention provide a user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system, so as to overcome the shortcomings of the prior art.

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

A user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system, comprising:

The user measures and calculates the SINR value between the user and each base station;

According to the SINR value between the user and each base station, the utility function value of each base station on the user side is calculated for the user, and the user sends an access application to the base station with the highest utility function value for the user. Channel quality information related to the SINR value between the base stations;

After the base station receives the access application sent by multiple users, it processes the channel quality information provided by each user, and uses the SINR value extracted from the access application as the utility function of the user who sent the access request on the base station side value, according to the utility function value of the user on the base station side, sort multiple users, and the base station judges whether the user with the highest utility function value can access the base station according to the rate request and interference situation of the user with the highest utility function value on the base station side, if it is , feed back information about successful access to the user with the highest value of the utility function; otherwise, feed back information about failed access to the user with the highest value of the utility function.

Further, the base station calculates the transmit power provided by the base station for each user according to the SINR between the user and the base station, including:

The relationship between the SINR between user i and base station j and the transmit power provided by base station j to user i is given by:

P _ij is the total transmit power of base station j when user i occupies all resource blocks for transmission, g _ij is the channel fading coefficient of base station j to user i, g _ik is the channel fading coefficient of base station k to user i, and σ ² is Gaussian white noise power, is the maximum transmit power of base station j.

Further, the calculation of the utility function value of each base station on the user side to the user according to the SINR value between the user and each base station includes:

The calculation formula of the utility function value of base station j on the user side to user i is as follows:

β _t is the offset value corresponding to different types of base stations, SINR _ij is the signal-to-interference and noise ratio of base station j to user i, t=1, 2, and 3 represent macro base stations, pico base stations and home base stations respectively, α is a weighting factor, L _j is the load of the jth base station at present,

Further, the base station judges whether the user with the highest utility function value can access the base station according to the rate request and interference situation of the user with the highest utility function value on the base station side, and if so, feeds back information about successful access to the Users with the highest utility function values, including:

The base station takes the user with the highest utility function value on the base station side as the user to be accessed, and uses the Lagrangian dual method and the half search iteration method to recalculate the bandwidth allocation ratio between the user to be accessed and the original accessed user. rate and base station total power consumption optimization requirements, then judge to allow the user to be accessed to access the base station, and feed back successful access information to the user to be accessed, and at the same time, the base station updates its current load.

Further, the said otherwise, feeding back the access failure information to the user with the highest utility function value includes:

If the user rate and the optimization requirements of the total power consumption of the base station are not satisfied, the base station judges that the user to be accessed cannot access the base station, and feeds back information about the access failure to the user to be accessed, and the user to be accessed After receiving the access failure information, set the utility function value of the base station originally applied by the user side to 0, and the user selects the base station with the largest utility function value among the remaining base stations to submit the access application;

The above process is repeated until all users can access the base station, and the user access algorithm stops.

Further, the recalculation of the bandwidth allocation ratio between the user to be accessed and the original accessed user by using the Lagrangian dual method and the half search iteration method includes:

Suppose the set of users in the ultra-dense heterogeneous network system is Z, the number is Z, the set of all base stations is M, the number is M, the subscript i represents the user index, the subscript j represents the base station index, and the user is a user equipment;

Define the binary access indicator variable as:

Assuming that the resource multiplexing factor of the ultra-dense heterogeneous network system is 1, b _ij is the proportional coefficient of the i-th user occupying the total bandwidth of the j-th base station, and the total number of resource blocks of the j-th base station is 50, and the j-th user The minimum division ratio of base station resources is 1/50=0.02, which satisfies:

0<b _ij ≤1, b _ij =0.02k, k∈N ⁺ (5)

Set the rate requirement of all users in the system to be fixed as R _i , and obtain the transmit power provided by base station j for user i as:

When the access scheme is given, the total transmit power of base station j is:

The Lagrangian dual method and the half-search iterative method are used to solve the power consumption optimization formula shown in the following formula (9), and the proportional coefficient b _ij and the binary access indicator variable x _ij are calculated:

Solving the above formula (9) needs to meet the constraints of formulas (1), (3) and (5) at the same time.

Further, the above-mentioned power consumption optimization algorithm shown in the above formula (9) is solved by using the Lagrangian dual method and the binary search iteration method, and the proportional coefficient b _ij and the binary access indicator variable x _ij are calculated, including :

When the access scheme is given and the binary access indicator variable x _ij is known, formula (9) is transformed into the optimization formula of proportional coefficient b _ij shown in formula (10):

Substituting the transmitted power P _ij obtained from formula (6), the above formula is transformed into the solution:

Among them, Z _j is the set of users accessed by the jth base station, and the above formulas (11) and (12) need to meet the constraints of (1), (3), and (5);

Using the Lagrangian dual function to solve equations (11) and (12), the Lagrangian function of the jth base station is as follows:

Using the KKT condition, calculate the partial derivative of the Lagrangian function and set it to 0, then obtain:

Calculate the first-order derivative of the Lagrangian multiplier of formula (15), f(b _ij )=λ is a monotonously decreasing function of the independent variable within the domain, and use the half-search iterative method to find the optimal λ value, the maximum and minimum values of λ are denoted by λ _max and λ _min respectively, Satisfy:

The value of the independent variable that will satisfy both (16) and (17) Substitute into formula (15) to obtain a series of λ values, namely:

then get

The value of the independent variable obtained by the above formula (19) Substituting into formula (15), a series of λ values are also obtained, then:

in, It is the minimum value of λ calculated by itself for the i-th user accessing the j-th base station. The interval divided by the calculated maximum value and minimum value of λ can be used as the initial calculation range of λ in the half search iteration method;

The process of using the half-search iterative method to find the optimal value of λ is as follows: All users accessing the j-th base station will perform the following verification process according to the calculated bandwidth allocation ratio:

If the sum of the b _ij of all users accessing the j-th base station is less than 1, it means that in this round of iteration process, the value of λ is too large, and subsequent iterations need to make λ _max = λ ⁽ⁿ⁾ ;

If the sum of the b _ij of all users accessing the j-th base station is greater than 1, it means that the value of λ is too small in the iterative process of this round, and the subsequent iteration needs to set λ _min = λ ⁽ⁿ⁾ ;

Repeat the iterative process above, that is, let λ ⁽ⁿ⁺¹⁾ = (λ _max + λ _min )/2, continue to calculate the corresponding bandwidth allocation ratio of each user, until the final sum is exactly 1, the algorithm stops the iterative process, The bandwidth allocation ratio b _ij at this time is the optimal solution that meets the minimum rate requirements of each user.

It can be seen from the technical solutions provided by the above embodiments of the present invention that the embodiments of the present invention study the problem of user access and energy efficiency optimization under the condition of a given target rate. On the basis of improving the total transmission power model of the base station, the Lager The Langerian duality and dichotomy method optimize and adjust the user resource allocation ratio in the access process and the transmit power provided by the base station for each access user, so that the power consumption optimization model can more accurately reflect the actual transmit power and power of the base station. consumption. The present invention proposes a user access algorithm used in an ultra-dense deployment scenario, and solves the problem of user access based on energy efficiency optimization under a given target rate condition.

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 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 diagram of an ultra-dense heterogeneous cellular communication system provided by an embodiment of the present invention;

FIG. 2 is a schematic diagram of a system throughput-to-total power consumption ratio changing with the number of users provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram showing how the throughput Jain fairness factor of each system base station varies with the number of system users according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of Jain fairness factors of the number of final access users in each base station of the system as the number of system users changes according to an embodiment of the present invention.

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 will not be interpreted in an idealized or overly formal sense unless defined as herein 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.

Embodiment one

The embodiment of the present invention improves on the basis of the existing literature, so that the power consumption optimization model can more accurately reflect the actual transmission power and power consumption of the base station, and on this basis, an improved SINR-based and pairing theory is proposed access strategy. Among them, the pairing theory is used to solve the load balancing problem caused by the change of the load quantity in the access process, and the Lagrangian dual and dichotomy method are used to optimize and adjust the resource ratio allocated to each user in the access process and the base station for each user. The transmit power provided.

System Model and Problem Modeling

The embodiment of the present invention mainly considers the ultra-dense deployment of the downlink heterogeneous cellular communication system. FIG. 1 is a schematic diagram of the ultra-dense heterogeneous cellular communication system proposed by the embodiment of the present invention, as shown in FIG. 1 . The following types of base stations exist in the system: macro base stations, picocell base stations (Picocells) and home base stations (Femtocells). In the research area, record the set of all active users as Z, the number is Z, the set of all base stations is M, and the number is M. Subscripts i and j denote active user index and densely deployed base station index, respectively. In this embodiment of the present invention, a user refers to a user equipment.

Define the binary access indicator variable as:

The SINR (Signal to Interference plus Noise Ratio) between user i and base station j is given by the following formula:

P _ij is the total transmit power of base station j when user i occupies all resource blocks for transmission, g _ij is the channel fading coefficient of base station j to user i, g _ik is the channel fading coefficient of base station k to user i, and σ ² is Gaussian white noise power, is the maximum transmit power of base station j.

In the embodiment of the present invention, the maximum value of the interference suffered by the user is used to simplify the calculation of the interference process. is the sum of the maximum interference power received by the corresponding users. According to Shannon's formula, the user's actual achievable communication rate is:

r _i =Bb _ij log ₂ (1+SINR _ij ) (4)

Among them, B is the total bandwidth of the system.

Assume that the resource reuse factor of the system is 1. b _ij is the ratio coefficient of the i-th user occupying the total bandwidth of the j-th base station, and the total number of resource blocks is set to 50, so the minimum division ratio is 1/50=0.02, that is, it needs to satisfy:

0<b _ij ≤1, b _ij =0.02k, k∈N ⁺ (5)

Set the required rate of user communication in the system to be fixed as R _i , for the convenience of calculation, assuming that the required rate of all users is the same, the transmission power provided by base station j for user i is obtained as:

When the access scheme is given, the total transmit power of base station j is:

Similarly, P _j should also satisfy the constraints of (3).

The transmit power of the base station is not the total power consumed by it. The transmit power model of the base station is a linear consumption model, and its main form is as follows:

in, is the final overall power consumption value of the base station, is the fixed power consumption value of the corresponding type of base station, _{and Δj} is the slope of the corresponding type of base station in the linear consumption model.

Since the total power consumption of the base station directly depends on the final total transmit power of the base station, the optimization of power consumption is equivalent to the following optimization problem:

The above formula needs to meet the constraints of (1), (3), and (5) at the same time. Since there are two types of optimization variables, it represents the strong coupling between the user access sub-problem and the bandwidth allocation sub-problem in this optimization problem. Among them, the problem of solving the variable x _ij is the user access sub-problem, and the problem of solving the variable b _ij is the bandwidth allocation sub-problem. The present invention will model and solve the above two sub-problems respectively, and finally propose a user access algorithm based on energy consumption and pairing theory.

User Access Algorithm Based on Energy Consumption and Pairing Theory in Ultra-Dense Heterogeneous Networks

Bandwidth Allocation Subproblem

When the access scheme is given (the binary access indicator variable x _ij is known), the original optimization problem is transformed into:

Substituting the transmitted power P _ij obtained from formula (6), the above formula is transformed into the solution:

Among them, Z _j is the set of users accessed by the jth base station, and the above equations (11) and (12) need to satisfy the constraints of (1), (3) and (5).

The optimization function solved by the embodiment of the present invention adds the product term of the resource allocation ratio b _ij . Since P _ij is the total transmission power after assuming that the user occupies all the frequency resources of the corresponding base station, it cannot represent the final transmission power obtained by the user on the base station.

Therefore, P _ij b _ij can correctly reflect the transmit power obtained by user i after obtaining some resources of base station j. It can be proved that the optimization problem in formula (10) is a convex optimization problem, and there is a unique minimum value point in this sub-problem within the domain of the optimization variable. Take out the part of the jth base station, denote it as S _j , and use the Lagrange dual function to solve it. The Lagrangian function of the jth base station is as follows:

Using the KKT condition to find the partial derivative of the Lagrangian function and set it to 0, we can get:

The KKT condition means that for an optimization problem with equality and inequality constraints, after constructing the corresponding Lagrangian function, the optimal value of the function must meet the following three conditions, namely the KKT condition: 1. The constructed Lagrangian The value of the function at this point after the partial derivative of the daily function to each independent variable is 0; 2. The value of the function brought into the optimal point by the equality constraint function is 0; 3. The new linear combination of the inequality constraint function The function has a function value of 0 at this point.

Specifically in this paper, the optimal independent variable must meet the above three conditions, and for the first one, the corresponding partial derivative function needs to be calculated. For the second one, we don’t need to consider it here, and its dependence is limited by the iterative selection value of λ below. For the third one, there is no inequality constraint in (13), so don't consider it.

Calculate the first order derivative of the Lagrangian function of the above formula, and its first order derivative value is less than 0. Calculations are no longer listed. So f(b _ij )=λ is a monotonically decreasing function with respect to the independent variable within the scope of the definition domain. That is, if the appropriate value of λ can be obtained, the optimal solution of the bandwidth allocation sub-problem can be obtained. The embodiment of the present invention uses a half-search iterative method to find the optimal value of λ. The maximum and minimum values of λ are denoted by λ _max and λ _min respectively. Satisfy:

The value of the independent variable that will satisfy both (16) and (17) Substituting into formula (15), a series of λ values can be obtained, namely:

then you can get

Take the value of the independent variable obtained from the above formula Substituting into formula (15), a series of λ values can also be obtained, then:

in, It is the minimum value of λ calculated by itself for the i-th user accessing the j-th base station. The interval divided by the calculated maximum and minimum values of λ can be used as the initial calculation range of λ in the half-search iterative method.

The embodiment of the present invention adopts the half search iteration method to solve the optimal λ value process as follows: all users accessing the jth base station are verified according to the calculated bandwidth allocation ratio as follows:

If the sum of the additions is less than 1, it means that the value of λ is too large in the iterative process of this round, and the subsequent iteration needs to make λ _max = λ ⁽ⁿ⁾ ;

If the sum is greater than 1, it means that the value of λ is too small during this round of iteration, and λ _min =λ ⁽ⁿ⁾ needs to be set in subsequent iterations.

Repeat the iterative process above, that is, let λ ⁽ⁿ⁺¹⁾ = (λ _max + λ _min )/2, continue to calculate the corresponding bandwidth allocation ratio of each user, until the final sum is exactly 1, the algorithm stops the iterative process, The bandwidth allocation ratio at this time is the optimal solution that satisfies the minimum rate requirements of each user. According to the calculated b _ij of each user accessed by base station j, the total transmit power of base station j is calculated according to formula (7).

What is dealt with above is to determine the bandwidth resource allocation and transmission power calculation of each access user by the base station when the user accesses, and the process of solving x _ij is determined by the subsequent access sub-process.

Determination of Utility Function in User Access Process

The utility function of the user set is:

β _t is the offset value corresponding to different types of base stations, SINR _ij is the signal-to-interference and noise ratio of base station j to user i, t=1, 2, and 3 represent macro base stations, pico base stations and home base stations respectively, α is a weighting factor, L _j is the load of the jth base station at present,

The utility function of the base station set is defined as:

That is, it is determined by formula (22). For a given base station, the utility function of the base station for each user is the actual SINR of the user. This SINR _ij is obtained when user i submits an application to base station j The performance parameters that the base station can obtain at that time. In the subsequent access process, the base station ranks all users who apply for it based on the SINR. Obviously, if it is determined that a user cannot access the base station, the utility function value for it is 0.

The specific implementation process of the user access algorithm

The user measures and calculates the SINR value between the user and each base station;

According to the SINR value between the user and each base station, the utility function value of each base station on the user side is calculated for the user, and the user sends an access application to the base station with the highest utility function value for the user. Channel quality information related to the SINR value between the base stations.

After the base station receives the access application sent by multiple users, it processes the channel quality information provided by each user, and uses the SINR value extracted from the access application as the utility function of the user who sent the access request on the base station side value, according to the utility function value of the user on the base station side, sort multiple users, and the base station judges whether the user with the highest utility function value can access the base station according to the rate request and interference situation of the user with the highest utility function value on the base station side, if it is , feed back information about successful access to the user with the highest value of the utility function; otherwise, feed back information about failed access to the user with the highest value of the utility function.

The base station takes the user with the highest utility function value on the base station side as the user to be accessed, and uses the Lagrangian dual method and the half search iteration method to recalculate the bandwidth allocation ratio between the user to be accessed and the original accessed user. Rate and base station total power consumption optimization requirements, that is, satisfying the bandwidth ratio of each user at this time adds up to 1, then it is judged that the user to be accessed is allowed to access the base station, the corresponding x _ij is updated to 1, and the access is successful information to the user to be accessed, and at the same time, the base station updates its own current load.

If the user rate and the optimization requirements of the total power consumption of the base station are not satisfied, the base station judges that the user to be accessed cannot access the base station, and feeds back information about the access failure to the user to be accessed, and the user to be accessed After receiving the access failure information, the utility function value of the base station originally applied by the user side is set to 0, and the user selects the base station with the largest utility function value among the remaining base stations to submit the access application.

The above process is repeated until all users can access the base station, and the user access algorithm stops.

After receiving the feedback information from the base station, the user decides the action for the next round of access application. If a user's access application is successful, the system will remove the user from the waiting queue; if a user's access application is rejected, the user will remove the original application base station The utility of the base station is set to 0, and then the one with the largest utility function value among the remaining base stations is selected again to submit an access application. Repeat the above process until all users can access the base station, the algorithm stops.

Simulation Results and Analysis

simulation settings

In the simulation system, there is a macro base station with a coverage radius of 500m and a maximum transmission power of 46dBm. 10 Pico base stations and 40 Femto base stations and corresponding users are evenly distributed in the research area, and the maximum transmission power is 35dBm and 20dBm respectively. Some parameters used in the specific simulation are shown in Table 1 below.

Table 1 Simulation parameter settings

In terms of base station power consumption, using the linear power consumption model mentioned above, the power consumption model parameters of different types of base stations are shown in Table 2 below:

Table 2 Base station linear power consumption model parameters

Simulation Performance Indicators

The present invention uses the ratio of rate to power consumption, that is, the power consumption efficiency as the main evaluation index, which is defined as the ratio of the total throughput of the system to the total power consumption of all base stations in the system.

In the fairness part, the Jain fairness factor is used as the main evaluation index, and its calculation method is as follows:

Among them, r _n is the correlation performance of the nth user, and N is the total number of all users in the system. Specifically in the present invention, the users mentioned in the above formula will be replaced by base stations in the system, and the related performance is the total throughput and the number of access users served by each base station for all its access users.

Simulation result analysis

The algorithm proposed in the present invention is an algorithm based on matching theory and power consumption improvement, using the maximum SINR for access (Matching Theory and Power consumption improvement-based, MTPI-SINR), and the comparison algorithm is mainly based on matching theory and bandwidth Matching Theory and Band AllocationAveraging (MTBAA—SINR) is based on pairing theory and power minimization (Matching Theory and PowerMinimization, MTPM—SINR), that is, the inaccurate transmission power minimization combined with pairing theory proposed in the existing literature The access and power adjustment algorithm, and based on the pairing theory and power consumption improvement, the algorithm for access using the maximum channel gain (MTPI-CH). The following is the specific analysis of the relevant simulation results.

Fig. 2 is a schematic diagram of the variation of the ratio of system throughput to total power consumption with the number of users provided by an embodiment of the present invention, that is, the result of the variation of energy consumption efficiency with the number of system users. From the overall trend, the value decreases with the increase of the independent variable. This is due to the limited bandwidth resources of the base station, so as the number of users increases, the final actual total throughput of the system shows a downward trend. From a vertical perspective, the performance of different algorithms is different under the premise of the same number of users in the system. It can be seen that the algorithm proposed in the present invention has excellent performance in terms of power consumption efficiency, which is better than the other three algorithms.

FIG. 3 is a schematic diagram of Jain fairness factors of respective throughputs of system base stations changing as the number of system users changes. From the overall trend, the value increases with the increase of the independent variable. After longitudinal comparison, it is found that, on the basis of the same number of users, whether the MTPI algorithm is based on the maximum SINR or the maximum channel gain, its throughput fairness is better than the other two algorithms. The reason is that after the power consumption is improved, the transmit power of each base station can basically be adjusted to the best, and because the throughput of each base station is not only related to its own transmit power, but also closely related to the transmit power of other base stations, so the throughput of each base station The consistency and fairness of the total throughput in the algorithm of the present invention are higher than those under the conditions of average bandwidth allocation and then power optimization and traditional power optimization conditions (no accurate adjustment of transmission power).

FIG. 4 is a schematic diagram showing how the Jain fairness factor of the number of final access users of each base station of the system changes with the number of system users. Under the four algorithms, with the increase of the independent variable, the value of this item shows an upward trend. Figure 4 shows that whether the MTPI algorithm is based on the maximum SINR or the maximum channel gain, the fairness of the number of base station accesses is better than the other two algorithms. The reason is consistent with Figure 3.

To sum up, the embodiment of the present invention studies the problem of user access and energy efficiency optimization under the condition of a given target rate. First of all, aiming at the deficiency of the power optimization expression in the existing literature, on the basis of improving the total transmission power model of the base station, using Lagrangian duality and dichotomy method to allocate resource ratio of users in the access process and base station for each access user The provided transmit power is optimized and adjusted so that the power consumption optimization model can more accurately reflect the actual transmit power and power consumption of the base station.

The present invention proposes a user access algorithm used in an ultra-dense deployment scenario, and solves the problem of user access based on energy efficiency optimization under a given target rate condition. Simulation results show that the algorithm of the present invention has good fairness, and is superior to other comparative algorithms in terms of power consumption efficiency.

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.

It can be seen from the above description of the implementation manners that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in storage media, such as ROM/RAM, disk , CD, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments of 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.

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 can easily conceive of changes or modifications within the technical scope disclosed in the present invention. 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 user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system is characterized by comprising the following steps:

measuring and calculating the SINR value between the user and each base station by the user;

calculating utility function values of each base station to the user at the user side according to the SINR values between the user and each base station, and sending an access application to the base station with the highest utility function value by the user, wherein the access application carries channel quality information related to the SINR values between the user and the base station;

after receiving access applications sent by a plurality of users, a base station processes channel quality information provided by each user, takes an SINR value extracted from the access applications as a utility function value of the user sending the access request at a base station side, sorts the users according to the utility function value of the user at the base station side, judges whether the user with the highest utility function value can access the base station according to the rate request and the interference condition of the user with the highest utility function value at the base station side, and feeds back information of successful access to the user with the highest utility function value if the user with the highest utility function value can access the base station; otherwise, feeding back the information of access failure to the user with the highest utility function value.

2. The method of claim 1, wherein the base station calculates the transmit power provided by the base station for each user according to the signal to interference plus noise ratio (SINR) between the user and the base station, and comprises:

the relationship between the SINR between user i and base station j and the transmit power provided by base station j for user i is given by:

P_ijtotal transmission power g of base station j when occupying all resource blocks for user i to transmit_ijIs the channel fading coefficient, g, of base station j to user i_ikFor the channel fading coefficient, σ, of base station k to user i²Is the power of white gaussian noise and is,is the maximum transmit power of base station j.

3. The method of claim 1, wherein the calculating a utility function value for each base station on the user side for each user according to the SINR value between the user and each base station comprises:

the calculation formula of the utility function value of the base station j at the user side to the user i is as follows:

β_toffset values, SINR, corresponding to different types of base stations_ijthe signal-to-interference-and-noise ratio of the base station j to the user i is t 1,2,3 respectively represent a macro base station, a pico base station and a home base station, α is a weighting factor, and L is_jFor the load of the current jth base station,

4. the method according to claim 1,2 or 3, wherein the base station determines whether the user with the highest utility function value can access the base station according to the rate request and the interference situation of the user with the highest utility function value at the base station side, and if so, feeds back the information of successful access to the user with the highest utility function value, including:

the base station takes the user with the highest utility function value at the base station side as a user to be accessed, recalculates the bandwidth allocation proportion of the user to be accessed and the original accessed user by adopting a Lagrange duality method and a binary search iteration method, judges that the user to be accessed is allowed to access the base station if the requirements of user rate and base station total power consumption optimization are met, feeds back successful access information to the user to be accessed, and simultaneously updates the current load of the base station.

5. The method of claim 4, wherein the otherwise feeding back the information of access failure to the user with the highest utility function value comprises:

if the requirement of optimizing the user rate and the total power consumption of the base station is not met, the base station judges that the user to be accessed can not access the base station, the information of access failure is fed back to the user to be accessed, after the user to be accessed receives the information of access failure, the utility function value of the original application base station at the user side is set to be 0, and the user selects one base station with the largest utility function value in the rest base stations again to submit access application;

and repeating the process until all the users can access the base station, and stopping the user access algorithm.

6. The method according to claim 4, wherein the recalculating the bandwidth allocation ratio between the user to be accessed and the original accessed user by using the lagrangian dual method and the binary search iteration method comprises:

setting a user set as Z and the number as Z in the ultra-dense heterogeneous network system, setting a set of all base stations as M and the number as M, wherein a subscript i represents a user index, a subscript j represents a base station index, and a user is user equipment;

the binary access indicator variable is defined as:

supposing that the resource reuse factor of the ultra-dense heterogeneous network system is 1, b_ijAnd (3) setting the total resource block number of the jth base station as 50 and the minimum division ratio of the jth base station resource as 1/50 to 0.02 for the proportionality coefficient of the ith user occupying the total bandwidth of the jth base station, and satisfying the following conditions:

0＜b_ij≤1,b_ij＝0.02k,k∈N⁺(5)

setting the speed requirement fixed as R when all users communicate in the system_iAnd solving the transmission power provided by the base station j for the user i as follows:

when an access scheme is given, the total transmission power of the base station j is:

the Lagrange duality method and the binary search iteration method are adopted to solve the power consumption optimization formula shown in the following formula (9), and the proportionality coefficient b is calculated_ijAnd a binary access indicator variable x_ij：

Solving equation (9) above requires satisfying the constraints of equations (1), (3), and (5).

7. The method as claimed in claim 6, wherein the power consumption optimization algorithm shown in the above formula (9) is solved by adopting a Lagrangian dual method and a binary search iteration method to calculate the proportionality coefficient b_ijAnd a binary access indicator variable x_ijThe method comprises the following steps:

given an access scheme, a binary access indicator variable x_ijWhen known, the formula (9) is converted into a proportionality coefficient b shown in the formula (10)_ijThe optimization formula of (2):

substituting the transmission power P obtained by the formula (6)_ijThen the above equation is converted to solve:

wherein Z is_jFor the user set accessed by the jth base station, the above equations (11) and (12) need to satisfy the constraint conditions of the equations (1), (3) and (5);

equations (11) and (12) are solved by using lagrangian dual functions, and the lagrangian function of the jth base station is as follows:

and (3) solving a partial derivative of the Lagrangian function by using a KKT condition, and if the partial derivative is 0, solving:

the first derivative, f (b), is taken of the Lagrangian multiplier of equation (15)_ij) In the domain range, λ is a monotone decreasing function about independent variable, and the optimal λ value is solved by a binary search iteration method, wherein the maximum value and the minimum value of λ are respectively λ_maxAnd λ_minIt is shown that,satisfies the following conditions:

taking the independent variable satisfying the expressions (16) and (17) at the same timeSubstituting the formula (15) to obtain a series of values of lambda, namely:

then obtain

The independent variable obtained by the above formula (19) is taken as a valueSubstituting into the formula (15), and obtaining a series of lambda values in the same way, then:

wherein λ is_i ^minAnd taking a value of the minimum lambda calculated by the ith user corresponding to the ith user in the jth base station. The interval divided by the maximum value and the minimum value of the lambda obtained by the calculation can be used as the initial calculation range of the lambda in the binary search iteration method;

the process of solving the optimal lambda value by adopting a binary search iteration method is as follows: and verifying all users accessing the jth base station according to the calculated bandwidth distribution proportion as follows:

if b of all users accessing the jth base station_ijThe sum of the addition is less than 1, which indicates that in the iteration process, the value of lambda is too large, and the subsequent iteration needs to make lambda be larger_max＝λ⁽ⁿ⁾；

If b of all users accessing the jth base station_ijThe sum of the addition is more than 1, which indicates that the value of lambda is too small in the iteration process, and the subsequent iteration needs to make lambda smaller_min＝λ⁽ⁿ⁾；

Repeating the above iterative process continuously, i.e. making lambda⁽ⁿ⁺¹⁾＝(λ_max+λ_min) And/2, continuously calculating the bandwidth distribution proportion corresponding to each user until the sum of the final addition is just 1, stopping the iterative process by the algorithm, and calculating the bandwidth distribution proportion b at the moment_ijI.e. the optimal solution meeting the minimum rate requirement of each user.