KR102037785B1 - Electronic apparatus and control method thereof - Google Patents

Abstract

An electronic device for performing load balancing between a plurality of layers in a wireless access network is disclosed. The electronic device may store a plurality of layers of a plurality of layers of a time interval after a current time point based on a storage unit in which a total load value of each time interval before a current time point of a plurality of layers is stored, and the total load value of at least one time interval stored in the storage unit. And a processor configured to calculate an overall load value, calculate a load value of each of the plurality of layers based on the calculated total load value, and perform load balancing based on the calculated load values of each of the plurality of layers.

Description

ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF

The present invention relates to an electronic device and a control method thereof, and more particularly, to an electronic device and a control method for performing load balancing (balancing).

The explosive growth in mobile traffic is driving demand for wireless network capacity. Due to the diversification of mobile services, the requirements for transmission speed, coverage, and moving speed of terminals vary widely, and are evolving from a single wireless network to a heterogeneous network applying various communication technologies to satisfy various mobile services.

1 is a diagram illustrating a heterogeneous network according to the prior art.

Heterogeneous networks typically consist of multi-layer cells with varying coverage, capacity, and requirements for moving speed. Thus, in a multi-layer heterogeneous network, one user can use one of several candidate networks. At this time, the algorithm for allocating a user to the network has a great effect on the performance of the entire heterogeneous network system. In a conventional wireless network, it was common practice to assign a user to a network having a larger RSRP (Reference Signal Received Power). However, in a multi-layered heterogeneous network, the use of such a method increases the likelihood that a user will be allocated only to a cell having a higher transmission power, resulting in a load inequality problem in which some cells are loaded and some cells are loaded in a small amount.

In order to solve this problem, recently, the size of the transmission output of each cell of a heterogeneous network is determined, and a handover candidate set that occurs when each cell is changed to the determined transmission size is determined to improve the gain of the transmission output. After the calculation, a method of maintaining load balancing between cells by determining whether to change the transmission output based on the calculated gain value has been proposed.

However, such a load balancing algorithm, firstly, by handing off the user for load balancing can generate excessive handoff signaling load in the system and increase the probability of handoff failure. In other words, the user assignment is incorrect in the multi-layered heterogeneous network, which causes frequent reassignment of users. Second, handoffs can occur more frequently if the Offered Load changes continuously over time and location.

Despite the change in arrival load as described above, a method of maintaining load balancing while minimizing handoff needs to be developed.

SUMMARY OF THE INVENTION The present invention is in accordance with the above-described needs, and an object of the present invention is to provide an electronic device and a method of controlling the same, which actively handle load changes and perform load balancing.

According to an embodiment of the present disclosure, an electronic device performing load balancing between a plurality of layers in a wireless access system may include a total load for each time period before a current time point of the plurality of layers. A total load value of the plurality of hierarchies of the time intervals after the current time point is calculated based on the storage unit in which values are stored and the total load values of at least one time interval stored in the storage unit, And a processor configured to calculate a load value of each of the plurality of layers, and perform the load balancing based on the calculated load values of each of the plurality of layers.

In addition, the processor may calculate the total load value of the plurality of layers in the time interval after the current time point by weighting the total load values of each of the plurality of consecutive time intervals stored in the storage.

The processor may calculate an overall load value of the plurality of layers in a time interval after the current time point through the following equation.

Where ρ is the total load value of the plurality of hierarchies in the time interval after the current time point, T is the number of a plurality of consecutive time intervals, ρ _i is the total load value of the i th time interval, and ω _i is the ρ _i Indicates the weight applied to.

The processor may determine the value of the weight based on a difference between the current time point and the time interval stored in the storage unit.

The processor may calculate a load value of each of the plurality of layers from the calculated total load value by using a genetic algorithm.

The processor selects an initial load allocation ratio of each of the plurality of layers, calculates a call blocking probability for the plurality of layers based on the selected load allocation ratio, and calculates the calculated call based on the genetic algorithm. The initial load allocation ratio may be updated so that the blocking probability is equal to or less than a preset value, and the load value of each of the plurality of layers may be calculated based on the updated load allocation ratio.

The processor may calculate call blocking probabilities for the plurality of layers through the following equation.

Here, P denotes a call blocking probability for the plurality of layers, K denotes the number of the plurality of layers, and P _j denotes a call blocking probability of the j th layer.

In addition, the processor may calculate a call blocking probability of the j th layer through the following equation.

Here, P _j is the call blocking probability of the j th layer, ρ _j is the load value of the j th layer, and C _j is the capacity of the j th layer.

The processor may apply the genetic algorithm based on at least one of a blocking rate of a new call, a cutting rate of a handoff call, a processing rate of each of a plurality of layers, a handoff order, and a power usage of a terminal.

The processor may allocate a new user and a handoff user to one of the plurality of layers based on the calculated load values of the plurality of layers in the time interval after the current time point.

Meanwhile, according to an embodiment of the present disclosure, a control method of an electronic device that performs load balancing between a plurality of layers in a wireless access system may store an entire load value for each time period before a current time point of the plurality of layers. Calculating a total load value of the plurality of hierarchies in the time interval after the current time point based on the stored total load values of the at least one time interval, and based on the calculated total load values. Calculating a load value of each layer and performing the load balancing based on the calculated load values of each of the plurality of layers.

The calculating of the total load values of the plurality of hierarchies may include calculating the total load values of the plurality of hierarchies of the time intervals after the current time point by weighting the total load values of each of the stored plurality of consecutive time intervals. Can be.

In the calculating of the total load values of the plurality of layers, the total load values of the plurality of layers of the time interval after the current time point may be calculated through the following equation.

Where ρ is the total load value of the plurality of hierarchies in the time interval after the current time point, T is the number of a plurality of consecutive time intervals, ρ _i is the total load value of the i th time interval, and ω _i is the ρ _i Indicates the weight applied to.

The calculating of the total load values of the plurality of layers may determine the value of the weight based on the difference between the current time point and the time interval.

In the calculating of the load value of each of the plurality of layers, the load value of each of the plurality of layers may be calculated from the calculated total load value using a genetic algorithm.

The calculating of a load value of each of the plurality of layers may include selecting an initial load allocation ratio of each of the plurality of layers, and calculating call blocking probabilities for the plurality of layers based on the selected load allocation ratio. Updating the initial load allocation ratio such that the calculated call blocking probability is equal to or less than a predetermined value based on the genetic algorithm; and calculating load values of each of the plurality of layers based on the updated load allocation ratio. It may include the step.

The calculating of a load value of each of the plurality of layers may calculate call blocking probabilities for the plurality of layers through the following equation.

Here, P denotes a call blocking probability for the plurality of layers, K denotes the number of the plurality of layers, and P _j denotes a call blocking probability of the j th layer.

In addition, calculating the load value of each of the plurality of layers may calculate the call blocking probability of the j th layer through the following equation.

Here, P _j is the call blocking probability of the j th layer, ρ _j is the load value of the j th layer, and C _j is the capacity of the j th layer.

The calculating of the load value of each of the plurality of layers may include calculating the genetic algorithm based on at least one of a blocking rate of a new call, a truncation rate of a handoff call, a processing rate of each of the plurality of layers, a handoff order, and power consumption of the terminal. Applicable

In the performing of the load balancing, a new user and a handoff user may be assigned to one of the plurality of layers based on the calculated load values of the plurality of layers in the time interval after the current time point.

According to various embodiments of the present disclosure, the electronic device may actively cope with load changes and minimize handoff by performing load balancing, and may perform optimized management of a wireless access system.

1 is a diagram illustrating a heterogeneous network according to the prior art.
2 is a block diagram illustrating an electronic device according to an embodiment of the present disclosure.
3 is a diagram for describing a module stored in a storage unit according to an exemplary embodiment.
4 is a diagram for describing a method of predicting a load value after a current time point according to an exemplary embodiment.
5A and 5B are diagrams for describing a method of calculating a load ratio according to an embodiment of the present invention.
6 is a flowchart illustrating a control method of an electronic device according to an embodiment of the present disclosure.

Hereinafter, various embodiments of the present invention will be described in detail with reference to the accompanying drawings.

2 is a block diagram illustrating an electronic device 100 according to an embodiment of the present disclosure. As shown in FIG. 2, the electronic device 100 includes a storage 110 and a processor 120.

The electronic device 100 is a device capable of performing load balancing between a plurality of tiers in a wireless access system. For example, the electronic device 100 may be implemented as a device such as a server. However, the present invention is not limited thereto, and the electronic device 100 may be any device capable of performing load balancing between a plurality of layers in the wireless access system.

The storage unit 110 may store an entire load value for each time period before the current time point of the plurality of layers. For example, if the current time is 9:00 am, the storage unit 110 may store the total load value from 7:00 am to 8:00 am and the total load value from 8:00 am to 8:00 am.

However, the present invention is not limited thereto, and the storage 110 may store the total load value by varying the size of the time interval, the number of time intervals, and the like.

The storage unit 110 may store various modules for performing load balancing. Detailed description thereof will be described later.

The processor 120 controls the overall operation of the electronic device 100.

According to an exemplary embodiment, the processor 120 may be implemented as a digital signal processor (DSP), a microprocessor, or a time controller (TCON), but is not limited thereto. central processing unit (CPU), microcontroller unit (MCU), micro processing unit (MPU), controller, application processor (AP), or communication processor (CP), ARM processor The processor 120 may be implemented by a System on Chip (SoC), a large scale integration (LSI), or an FPGA (Integrated Processing Algorithm). Field Programmable Gate Array) may be implemented.

The processor 120 calculates an overall load value of a plurality of layers of a time interval after the current time point based on the total load value of at least one time interval stored in the storage 110, and based on the calculated total load value. The load value of each of the plurality of layers may be calculated, and load balancing may be performed based on the calculated load values of each of the plurality of layers.

The processor 120 may weight the total load values of each of the plurality of consecutive time intervals stored in the storage 110 to calculate the total load values of the plurality of layers of the time interval after the current time point.

In detail, the processor 120 may calculate total load values of a plurality of layers of a time interval after the current time point through Equation 1 below.

[Equation 1]

Where ρ is the total load value of the plural layers of the time interval after the current time point, T is the number of plural consecutive time intervals, ρ _i is the total load value of the i th time interval, and ω _i is applied to ρ _i . Indicates the weight.

The processor 120 may determine a weight value based on a difference between a current time point and a time interval stored in the storage 110. For example, the processor 120 may increase the value of the weight in a time section closer to the current time.

Meanwhile, the processor 120 may calculate load values of each of the plurality of layers from the total load values calculated using the genetic algorithm.

The processor 120 may select an initial load allocation ratio of each of the plurality of layers, and calculate call blocking probabilities for the plurality of layers based on the selected load allocation ratio. Here, the call blocking probabilities may indicate that when a plurality of users each request traffic, there may be no response to the request of some users due to a failure or the like.

The processor 120 updates the initial load allocation ratio such that the call blocking probability calculated based on the genetic algorithm is equal to or less than a preset value, and calculates load values of each of the plurality of layers based on the updated load allocation ratio. Can be.

Here, the processor 120 may calculate call blocking probabilities for a plurality of layers through Equation 2 below.

[Equation 2]

Here, P is a call blocking probability for a plurality of layers, K is a number of a plurality of layers, P _j is a call blocking probability of the j-th layer.

The processor 120 may calculate a call blocking probability of the j th layer through Equation 3 below.

[Equation 3]

Here, P _j is the call blocking probability of the j th layer, ρ _j is the load value of the j th layer, and C _j is the capacity of the j th layer.

Meanwhile, the processor 120 may apply a genetic algorithm based on at least one of a blocking rate of a new call, a cutting rate of a handoff call, a processing rate of each of a plurality of layers, a handoff order, and power consumption of the terminal. Application method is the same as the method using the above-described call blocking probability, it is omitted because it is a duplicate description.

The processor 120 may assign a new user and a handoff user to one of the plurality of layers based on the load value of each of the plurality of layers calculated in a time interval after the current time point.

Hereinafter, the operation of the processor 120 will be described more clearly through the drawings.

3 is a view for explaining a module stored in the storage unit 110 according to an embodiment of the present invention.

As illustrated in FIG. 3, the storage 110 may store a load balancing module, and the processor 120 may perform load balancing based on the load balancing module stored in the storage 110. Also, the right side of FIG. 3 shows a network model, and the network model may include K layers having different service coverages. A new user or a handoff user who arrives to receive a service may be assigned to one cell of a plurality of layers based on a result value determined by the load balancing module.

Here, the cell may mean a geographic area where the base station can provide wireless access to the terminals. That is, the same layer provides a service according to the same communication standard, and a plurality of cells may be included in the same layer according to the difference in the geographical area.

The load balancing module may include a load prediction module 310 and a load optimization allocation module 320. The load prediction module 310 is a module for predicting a load value after the current time point based on a load value before the current time point, and the load optimization allocation module 320 is configured to generate a plurality of loads based on the estimated load value after the current time point. It may be a module for allocating load to each layer.

The load prediction module 310 may include a load measurement unit 311, a measurement information storage unit 312, and a load prediction unit 313.

The load measuring unit 311 is a module for measuring load, and may measure load values of each of a plurality of layers for each time zone. The electronic device 100 may further include a communication unit and may receive a load value from each of the plurality of layers. That is, a cell located in each of the plurality of layers may provide the load measurement unit 311 with various parameters such as the current number of users and the total number of channels required by the load balancing module.

The measurement information storage unit 312 may store load values for each of the plurality of layers measured by the load measurement unit 311 according to time zones.

The load predictor 313 is a module that predicts load information at a next time point from load information of a predetermined time interval in the past by using a weighted average method, and may be represented by Equation 1 below. Here, the sum of the weights for each of the plurality of layers may be 1.

As shown in FIG. 4, since the load value generally changes linearly, the predicted load value may be most affected by the recent load value. Accordingly, the largest weight may be applied to the load value of the time interval closest to the current time point.

However, the present invention is not limited thereto, and weights may be applied in other ways. For example, the processor 120 performs prediction on a daily basis, and may set the weight of the same time interval the day before to the highest value.

In general, the load in wired and wireless networks varies greatly depending on the time zone, but it is characterized by an increase or decrease over time rather than a sudden change over time. The load situation can be predicted.

The load optimization allocation module 320 may include a genotype expression unit 321, a fitness calculation unit 322, an optimal sample generation unit 323, and an allocation unit 324. First, when the predicted load value of the entire plurality of layers is ρ, ρ can be expressed as Equation 4 below.

[Equation 4]

Here, K is the number of the plurality of layers, ρ _j is the load of the j-th layer.

Then, the load ratio of each of the plurality of layers to the predicted load value of the entire plurality of layers may be expressed as a _i = ρ _j / ρ. Here, i is an index for indicating each of the plurality of layers.

The genotype expression unit 321 is a module for displaying the load ratio of each of the plurality of layers in the previous genotype.

For example, as shown in FIG. 5A, a _i may be represented by N binary numbers, and in a multi-layer heterogeneous network composed of all K layers, an optimal allocation ratio may be represented by all K · N binary numbers. have.

The magnitude of each ai may be expressed as in Equation 5 below.

[Equation 5]

이며, b_k는 이진수로 표현된 부하값에서 k번째 자리수의 값, x_i는 부하값을 10진수로 나타낸 값을 의미한다.here,

Where b _k is the value of the kth digit of the load value expressed in binary and x _i is the value in decimal representation of the load value.

Here, the processor 120 may randomly set initial load values of each of the plurality of layers. Thereafter, the processor 120 may calculate an optimal load value of each of the plurality of layers based on the genetic algorithm.

The goodness-of-fit calculation unit 322 may calculate the goodness of fit based on at least one of the blocking rate of the new call, the cutting rate of the handoff call, the processing rate of each of the plurality of layers, the handoff order, and the power consumption of the terminal.

If the multi-objective function composed of the plurality of parameters described above is used, the processor 120 may evaluate the fitness through Monte Carlo simulations using the load ratio as an input value. Alternatively, the goodness of fit may be calculated using the following two parameters.

For example, the capacity of the j-th layer C _j is not the vertical hand-off between the layers when the load ρ _j are allocated, the call blocking probability can be expressed as Equation 3, the P _j are described above. The call blocking probabilities P for the plurality of layers may be expressed by Equation 2 described above.

The optimal sample generation unit 323 may calculate an optimal allocation ratio for each of the plurality of hierarchies based on the goodness of fit calculated by the goodness-of-fit calculation unit 322. The optimal sample generation unit 323 may calculate an optimal load allocation ratio by generating a new population of selection, crossing, and variation processes from a population of solutions represented by binary numbers and repeating the above processes.

FIG. 5B is a diagram comparing the performance of the algorithm proposed by the present invention when statically assigning 1: 1 or 2: 1 to each layer when layer K = 2. The proposed algorithm shows the smallest call blocking probability for each load value.

The allocator 324 is a module for allocating a new user and a handoff user to one of a plurality of layers based on an optimal load allocation ratio.

6 is a flowchart illustrating a control method of an electronic device according to an embodiment of the present disclosure.

A control method of an electronic device that performs load balancing between a plurality of layers in a wireless access system, first stores an entire load value for each time interval before a current time point of a plurality of layers (S610). In operation S620, the total load values of the plurality of layers of the time interval after the current time point are calculated based on the stored total load values of the at least one time interval. Then, the load value of each of the plurality of layers is calculated based on the calculated total load value (S630). Then, load balancing is performed based on the calculated load values of each of the plurality of layers (S640).

In operation S620, the total load values of the plurality of hierarchies may be weighted to calculate the total load values of the plurality of hierarchies of the time intervals after the current time point by weighting the total load values of each of the plurality of consecutive time intervals. have.

Here, in the calculating of the total load values of the plurality of layers (S620), the total load values of the plurality of layers of the time interval after the current time point may be calculated through the following equation.

Where ρ is the total load value of the plural layers of the time interval after the current time point, T is the number of plural consecutive time intervals, ρ _i is the total load value of the i th time interval, and ω _i is applied to ρ _i . Indicates the weight.

In operation S620 of calculating the total load values of the plurality of layers, the weight value may be determined based on the difference between the current time point and the time interval.

On the other hand, calculating the load value of each of the plurality of layers (S630) may calculate the load value of each of the plurality of layers from the total load value calculated using a genetic algorithm.

Here, calculating the load values of each of the plurality of layers may include selecting an initial load allocation ratio of each of the plurality of layers, and calculating call blocking probabilities for the plurality of layers based on the selected load allocation ratio. And updating the initial load allocation ratio such that the call blocking probability calculated based on the genetic algorithm is equal to or less than a predetermined value, and calculating load values of each of the plurality of layers based on the updated load allocation ratio. have.

In operation S630, the load value of each of the plurality of layers may be calculated by using the following equation.

Here, P is a call blocking probability for a plurality of layers, K is a number of a plurality of layers, P _j is a call blocking probability of the j-th layer.

In operation S630, the load value of each of the plurality of layers may be calculated by using the following equation.

Here, P _j is the call blocking probability of the j th layer, ρ _j is the load value of the j th layer, and C _j is the capacity of the j th layer.

On the other hand, calculating the load value of each of the plurality of layers (S630) is a genetic algorithm based on at least one of the blocking rate of the new call, the disconnection rate of the handoff call, the throughput of each of the plurality of layers, the handoff order and the power consumption of the terminal Can be applied.

In step S640 of performing load balancing, a new user and a handoff user may be assigned to one of the plurality of layers based on load values of each of the plurality of layers calculated in a time interval after the current time point.

According to various embodiments of the present disclosure, the electronic device may actively cope with load changes and minimize handoff by performing load balancing, and may perform optimized management of a wireless access system.

On the other hand, it has been described above using only a genetic algorithm, but is not limited thereto. For example, an algorithm such as a scale free network algorithm may be used, and any optimization algorithm may be used.

Meanwhile, the processor may change at least one of a time interval and a weight before the current time point based on the predicted accuracy of the overall load value. For example, if the estimated total load value differs from the actual load value by more than a predetermined value, the processor may reduce the number of time intervals or increase the weight of the time interval close to the current time point.

Meanwhile, according to an exemplary embodiment of the present disclosure, various embodiments described above may be implemented by software including instructions stored in a machine-readable storage media. Can be. The device may be a device capable of calling a stored command from a storage medium and operating in accordance with the called command, and may include an electronic device (for example, the electronic device A) according to the disclosed embodiments. When an instruction is executed by a processor, the processor may perform a function corresponding to the instruction by using other components directly or under the control of the processor. The instructions can include code generated or executed by a compiler or interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' means that the storage medium does not include a signal and is tangible, but does not distinguish that the data is stored semi-permanently or temporarily on the storage medium.

In addition, according to an embodiment of the present disclosure, the method according to the various embodiments described above may be provided in a computer program product. The computer program product may be traded between the seller and the buyer as a product. The computer program product may be distributed online in the form of a device-readable storage medium (eg compact disc read only memory (CD-ROM)) or through an application store (eg Play StoreTM). In the case of an online distribution, at least a portion of the computer program product may be stored at least temporarily on a storage medium such as a server of a manufacturer, a server of an application store, or a relay server, or may be temporarily created.

In addition, according to an embodiment of the present invention, the various embodiments described above may be stored in a recording medium readable by a computer or similar device using software, hardware, or a combination thereof. It can be implemented in In some cases, the embodiments described herein may be implemented by the processor itself. According to the software implementation, embodiments such as the procedures and functions described herein may be implemented as separate software modules. Each of the software modules may perform one or more functions and operations described herein.

Meanwhile, computer instructions for performing a processing operation of the device according to the various embodiments of the present disclosure may be stored in a non-transitory computer-readable medium. The computer instructions stored in the non-transitory computer readable medium allow the specific device to perform processing operations in the device according to the above-described various embodiments when executed by the processor of the specific device. A non-transitory computer readable medium refers to a medium that stores data semi-permanently and is readable by a device, not a medium storing data for a short time such as a register, a cache, a memory, and the like. Specific examples of non-transitory computer readable media may include CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, and the like.

In addition, each component (for example, a module or a program) according to the above-described various embodiments may be composed of a singular or plural number of objects, and some of the above-described subcomponents may be omitted or other subcomponents may be omitted. Components may be further included in various embodiments. Alternatively or additionally, some components (eg, modules or programs) may be integrated into one entity to perform the same or similar functions performed by each corresponding component prior to integration. According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, repeatedly, or heuristically, or at least some of the operations may be executed in a different order, omitted, or another operation may be added. Can be.

While the above has been illustrated and described with respect to preferred embodiments of the present disclosure, the present disclosure is not limited to the above-described specific embodiments, and is normally made in the art without departing from the gist of the present disclosure as claimed in the claims. Various modifications may be made by those skilled in the art, and these modifications should not be individually understood from the technical spirit or the prospect of the present disclosure.

100: electronic device 110: storage
120: processor

Claims (20)

An electronic device performing load balancing between a plurality of layers in a wireless access system,
A storage unit in which a total load value of each time interval before the current time points of the plurality of layers is stored; And
Compute the total load value of the plurality of layers of the time interval after the current time point based on the total load value of at least one time interval stored in the storage unit, and based on the calculated total load value A processor configured to calculate respective load values, and perform the load balancing based on the calculated load values of each of the plurality of layers.
The processor,
Select an initial load allocation ratio of each of the plurality of layers, calculate a call blocking probability for the plurality of layers based on the selected load allocation ratio, and set the initial call allocation probability to be equal to or less than a preset value. And update a load allocation ratio and calculate a load value of each of the plurality of layers based on the updated load allocation ratio. The method of claim 1,
The processor,
And total weight value of each of a plurality of consecutive time intervals stored in the storage unit to calculate a total load value of the plurality of layers of the time interval after the current time point. The method of claim 2,
The processor,
An electronic device for calculating an overall load value of the plurality of layers in a time interval after the current time point through the following equation;

Where ρ is the total load value of the plurality of hierarchies in the time interval after the current time point, T is the number of a plurality of consecutive time intervals, ρ _i is the total load value of the i th time interval, and ω _i is the ρ _i Indicates the weight applied to. The method of claim 3,
The processor,
And determine the value of the weight based on a difference between the current time point and the time interval stored in the storage unit. delete delete The method of claim 1,
The processor,
An electronic device for calculating call blocking probabilities for the plurality of layers through the following equation;

Here, P denotes a call blocking probability for the plurality of layers, K denotes the number of the plurality of layers, and P _j denotes a call blocking probability of the j th layer. The method of claim 7, wherein
The processor,
An electronic device for calculating a call blocking probability of the j th layer through the following equation.

Here, P _j is the call blocking probability of the j th layer, ρ _j is the load value of the j th layer, and C _j is the capacity of the j th layer. delete The method of claim 1,
The processor,
And assigning a new user and a handoff user to one of the plurality of layers based on the calculated load values of the plurality of layers in a time interval after the current time point. A control method of an electronic device for performing load balancing between a plurality of layers in a wireless access system,
Storing a total load value for each time period before a current time point of the plurality of layers;
Calculating an overall load value of the plurality of layers in the time interval after the current time point based on the stored total load value of the at least one time interval;
Calculating load values of each of the plurality of layers based on the calculated total load values; And
Performing the load balancing based on the calculated load values of each of the plurality of layers;
Calculating the load value of each of the plurality of layers,
Selecting an initial load allocation ratio of each of the plurality of layers;
Calculating call blocking probabilities for the plurality of layers based on the selected load allocation ratio;
Updating the initial load allocation ratio such that the calculated call blocking probability is equal to or less than a preset value; And
Calculating a load value of each of the plurality of layers based on the updated load allocation ratio. The method of claim 11,
Computing the total load value of the plurality of layers,
And weighting the total load values of each of the stored plurality of consecutive time intervals to calculate the total load values of the plurality of layers in the time interval after the current time point. The method of claim 12,
Computing the total load value of the plurality of layers,
A control method of calculating total load values of the plurality of layers in the time interval after the current time point through the following equation;

Where ρ is the total load value of the plurality of hierarchies in the time interval after the current time point, T is the number of a plurality of consecutive time intervals, ρ _i is the total load value of the i th time interval, and ω _i is the ρ _i Indicates the weight applied to. The method of claim 13,
Computing the total load value of the plurality of layers,
And determining the value of the weight based on the difference between the current time point and the time interval. delete delete The method of claim 11,
Calculating the load value of each of the plurality of layers,
A control method of calculating call blocking probabilities for the plurality of layers through the following equation;

Here, P denotes a call blocking probability for the plurality of layers, K denotes the number of the plurality of layers, and P _j denotes a call blocking probability of the j th layer. The method of claim 17,
Calculating the load value of each of the plurality of layers,
A control method for calculating the call blocking probability of the j-th layer through the following equation.

Here, P _j is the call blocking probability of the j th layer, ρ _j is the load value of the j th layer, and C _j is the capacity of the j th layer. delete The method of claim 11,
Performing the load balancing,
And assigning a new user and a handoff user to one of the plurality of layers based on the calculated load values of the plurality of layers in the time interval after the current time point.

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