KR102151314B1 - Optimization method and system of random content caching in heterogeneous small cell networks - Google Patents

Abstract

A method and system for optimizing random content storage in a heterogeneous small cell network are presented. The random content storage optimization method according to an embodiment is to set initial values of Lagrangian multipliers for all layers in a heterogeneous small cell network having different characteristics and capable of storing contents. step; Calculating an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier; Updating the value of the Lagrange multiplier and then updating the storage probability of all contents in the layer; Checking whether the sum of the storage probabilities at all layers satisfies the memory content constraint; And finally determining the storage probability when the sum of the storage probabilities satisfies a memory content constraint condition.

Description

Random content storage optimization method and system in heterogeneous small cell networks {OPTIMIZATION METHOD AND SYSTEM OF RANDOM CONTENT CACHING IN HETEROGENEOUS SMALL CELL NETWORKS}

The following embodiments relate to a method and system for optimizing random content storage in heterogeneous small cell networks, and more specifically, storing random content in heterogeneous small cell networks in which heterogeneous base stations having different characteristics are mixed. It relates to a method and system.

In recent years, with the rapid increase of wireless mobile devices such as smart phones and tablets, current communication networks are facing an unprecedented increase in wireless mobile traffic. Global data traffic demand is expected to increase, and as a way to cope with this traffic increase, wireless edge caching technology that stores content in advance at the edge of the network such as mobile devices and small cells. It is attracting attention.

Research on local caching in various networks, such as in small cell and D2D (Device to Device) networks, is being actively conducted. Various caching methods such as a crystalline caching method, a random caching method, and an encoding caching method for obtaining various wireless network gains such as delay gain, cooperative gain, multicasting gain, and channel selection diversity gain are being studied.

In particular, it is known that different channel selection diversity can be provided according to how and what content communication nodes store in a random network. Specifically, if nodes store the same content redundantly, it can provide improved channel quality for specific content, whereas, if each node stores different content, it can provide a variety of content services. It becomes difficult to provide an improved channel gain for that. The channel selection diversity gain has been studied in a homogeneous small cell network, but it is limited in direct application to a heterogeneous small cell network having different characteristics.

K. Shanmugam, N. Golrezaei, A. G. Dimakis, and A. F. Molisch, and G. Caire, “Femtocaching: Wireless content delivery through distributed caching helpers,” IEEE Trans. Inform. Theory, vol. 59, no. 12, pp. 8402-8413, Dec. 2013.

The embodiments describe a method and system for optimizing random content storage in a heterogeneous small cell network, and more specifically, a heterogeneous small cell network in which heterogeneous base stations having different characteristics (transmission power, base station density, storage memory size, etc.) are mixed ( heterogeneous small cell networks) to provide random content storage technology.

In addition, the embodiments provide an iterative algorithm that finds the optimal storage probability of each content for maximizing the cache hit probability, so that it can handle rapidly increasing wireless mobile traffic and greatly enhance wireless communication functions. It is to provide a method and system for optimizing random content storage in a heterogeneous small cell network that can be improved.

The random content storage optimization method according to an embodiment is to set initial values of Lagrangian multipliers for all layers in a heterogeneous small cell network having different characteristics and capable of storing contents. step; Calculating an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier; Updating the value of the Lagrange multiplier and then updating the storage probability of all contents in the layer; Checking whether the sum of the storage probabilities at all layers satisfies the memory content constraint; And finally determining the storage probability when the sum of the storage probabilities satisfies a memory content constraint condition.

After updating the value of the Lagrange multiplier, the step of updating the storage probability of all the contents of the layer is, by updating the value of the Lagrange multiplier, the storage probability according to all the contents is updated. Since it is a probability function of the storage probability, it may change, and may affect the value of the Lagrange multiplier.

The step of checking whether the sum of the storage probabilities in all the layers satisfies the memory content constraint may include updating the value of the Lagrange multiplier until the sum of the storage probabilities in all layers satisfies the memory content constraint. After that, it is possible to update the storage probability of all the contents of the layer.

The final determination of the storage probability includes the optimal storage probability for maximizing an average cache hit probability indicating success or failure of content transmission between limited memories of the small cell cache base stations of each layer. Can be found.

In the heterogeneous small cell network, all heterogeneous small cell base stations may have a single antenna, and small cell base stations of each layer may be randomly located in a 2D plane in a Poisson point process.

An arbitrary receiver requests content, and a small cell base station having the largest channel power gain among heterogeneous small cell base stations of all layers having the content can transmit the content to the arbitrary receiver.

In a random content storage optimization system according to another embodiment, in a heterogeneous small cell network having different characteristics and capable of storing contents, an initial value of Lagrangian multipliers is set for all layers. An initial value setting unit; A storage probability initial value calculation unit that calculates an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier; An update unit for updating the storage probability of all contents in the layer after updating the value of the Lagrange multiplier; A constraint condition determination unit for checking whether the sum of the storage probabilities in all layers satisfies the memory content constraint; And a storage probability determining unit that finally determines the storage probability when the sum of the storage probabilities satisfies a memory content constraint condition.

The update unit updates the storage probability according to all contents as the value of the Lagrange multiplier is updated, and the probability in the other layer is changed because it is a probability function of the storage probability, so that the value of the Lagrange multiplier may be affected. have.

The constraint condition determination unit updates the value of the Lagrange multiplier through the update unit until the sum of the storage probabilities in all layers satisfies the memory content constraint condition, and then updates the storage probabilities of all contents in the layer. I can.

The storage probability determiner may find the optimal storage probability for maximizing an average cache hit probability indicating whether a content transmission succeeds or not between limited memories of the small cell cache base station of each layer.

In the heterogeneous small cell network, all heterogeneous small cell base stations may have a single antenna, and small cell base stations of each layer may be randomly located in a 2D plane in a Poisson point process.

An arbitrary receiver requests content, and a small cell base station having the largest channel power gain among heterogeneous small cell base stations of all layers having the content can transmit the content to the arbitrary receiver.

According to embodiments, it is possible to provide a method and system for optimizing random content storage in a heterogeneous small cell network in which heterogeneous base stations having different characteristics (transmission power, base station density, storage memory size, etc.) are mixed.

In addition, according to embodiments, by providing an iterative algorithm that finds the optimal storage probability of each content for maximizing the cache hit probability, a heterogeneous small cell capable of handling rapidly increasing wireless mobile traffic and greatly improving wireless communication functions. A method and system for optimizing storage of random contents in a network can be provided.

1 is a diagram illustrating a random content storage optimization system in a heterogeneous small cell network according to an embodiment.
2 is a block diagram illustrating a random content storage optimization system in a heterogeneous small cell network according to an embodiment.
3 is a flowchart illustrating a method of optimizing storage of random contents in a heterogeneous small cell network according to an embodiment.

Hereinafter, embodiments will be described with reference to the accompanying drawings. However, the described embodiments may be modified in various forms, and the scope of the present invention is not limited by the embodiments described below. In addition, various embodiments are provided to more completely explain the present invention to those of ordinary skill in the art. In the drawings, the shapes and sizes of elements may be exaggerated for clearer explanation.

The following embodiments relate to a method and system for optimizing random content storage in heterogeneous small cell networks. The embodiments propose a random content storage technique in a heterogeneous small cell network in which heterogeneous base stations having different characteristics are mixed, and iteratively finds the optimal caching probability of each content to maximize the cache hit probability. We propose an iterative algorithm.

In the embodiments, when an arbitrary user terminal (receiver) requests an arbitrary file, when a file requested by a transmitting node having the largest received signal among transmitting nodes having the file is serviced, each transmitting node has a limited cache memory. It relates to a probabilistic storage technique for how to optimally store files in the. In particular, the embodiments are transmitting nodes belonging to at least one cluster formed in a cell, and distributedly store a plurality of files (that is, files that can be provided to a user terminal) stored in a cellular base station. When the sending nodes provide the requested file to the user terminal upon request, the probability of successfully transmitting the file requested by the user terminal from the sending node, that is, the optimal file storage (caching) probability that maximizes the file transfer success probability It is about the technology to do.

For example, among two or more transmitting nodes storing a file requested by the user terminal, the requested file is transmitted from the transmitting node with the closest distance to the user terminal, the best reception strength, or the largest SNR. In order to provide as, it relates to a technique for determining with what probability each of the plurality of files should be stored in a transmitting node. Here, the file transmission success probability represents the average probability that video streaming can be successfully received without a playback delay when a user terminal requests a file, and the file storage probability is each corresponding file to maximize the file transmission success probability. Can represent the storage probability of.

In the present embodiments, if the file transmission success probability is transmitted to each transmission node, files may be stored randomly according to the file transmission success probability for each transmission node. In addition, each transmitting node may store different types of file sets even if they receive the same file transmission success probability.

In the present embodiments, the transmitting node is a relay device such as a gateway other than a cellular base station corresponding to a cell, and is used to perform an auxiliary function to relieve the burden of the cellular base station providing data to the user terminal. Can be.

In addition, the transmitting node has a cache memory, and a file frequently requested by a user equipment (UE) from a cellular base station corresponding to the cluster to which the transmitting node belongs, and allocated from the cellular base station. At least one file may be pre-distributed and stored in its own cache.

1 is a diagram illustrating a random content storage optimization system in a heterogeneous small cell network according to an embodiment.

의 푸아송 포인트 프로세스를 기반으로 클러스터 내에 랜덤하게 위치됨을 가정할 수 있다.Referring to FIG. 1, in a K-tier heterogeneous cache-enabled small cell network, a receiver 110 and a plurality of transmitting nodes 120, 121, 122, 123, 124, 125, 126) can be located. Here, the receiver 110 may be a user terminal, and the plurality of transmitting nodes 120, 121, 122, 123, 124, 125, 126 may be small cell base stations. In FIG. 1, a case where there are 7 transmission nodes and 1 receiver is illustrated as an example, but this corresponds to an embodiment, and there may be less than 7 transmission nodes, 7 or more, and two or more receivers may also exist. And, in FIG. 1, the density per unit area of the transmitting nodes on a 2D (Dimension) plane

Based on the Poisson point process of, it can be assumed that they are randomly located within the cluster.

Referring to FIG. 1, each of the transmitting nodes 120, 121, 122, 123, 124, 125, 126 has a cache memory, and the cache memory contains at least one file allocated from a cellular base station (not shown). You may be saving it. For example, sending node 1 (120) stores file 1 and file 4 in the cache, sending node 2 (121) stores file 1, file 3, file 4 and file 6 in the cache, and the sending node 3(122) may be storing file 2, file 3, and file 4 in a cache, and sending node 4(123) may be storing file 2, file 5, and file 6 in a cache, and sending node 5(124) Is storing files 2 and 5 in the cache, sending node 6 125 may be storing files 1 and 3 in the cache, and sending node 7 126 is storing files 1, 2, 3 and You may be storing file 5 in the cache. Since the cache of the transmitting nodes has a limited memory size, that is, has a limited cache memory, some files of the cellular base station may be stored instead of all files. That is, some of the files F1 to F6 may be redundantly stored in each cache, or completely different files may be stored.

In FIG. 1, each of the transmitting nodes may have a single antenna, and a cache memory size corresponding to each of the transmitting nodes may be assumed to be M (that is, there are M caches). In addition, it may be assumed that different M files are stored in a cache corresponding to each of the M transmission nodes.

It will be described in more detail below.

A K-tier heterogeneous cache-enabled small cell network capable of storing contents and having different characteristics may be considered. Here, the different characteristics may be characteristics such as transmission power, base station density, and storage memory size.

The general system environment is as follows.

이며, 채널 모델은 Nakagami-m 페이딩이고 채널 분포는 다음 식과 같이 나타낼 수 있다. The total number of contents is N, and the library of each content index is

And the channel model is Nakagami-m fading, and the channel distribution can be expressed as follows.

[Equation 1]

이고, 낮은 파일 인덱스일수록 더 높은 인기도를 가질 수 있다(

). 패스로스(Pathloss) 모델은

이고, 여기서

은 거리이며,

는 지수 감쇄 승이다. 그리고 잡음(noise) 파워는

이다. Also, the popularity of each file is

, And the lower the file index, the higher the popularity can be (

). The Pathloss model is

Is, where

Is the street,

Is the exponential decay win. And the noise power is

to be.

Each layer has different characteristics, and characteristics of the k-th layer cache small cell base station are as follows.

(서로 다른

개의 컨텐츠를 저장)이고, 전송 파워는

이며, 스몰셀 기지국 단위 면적당 밀도는

이고, 컨텐츠 i의 저장 확률은

이다. 또한, k 번째 계층에서 컨텐츠 i를 가진 캐시 스몰셀 기지국 위치 집합은

이고, 이 때 컨텐츠 i를 가진 모든 계층의 스몰셀 기지국들의 위치 집합은

이다.Cache memory size is

(Different

2 contents), and the transmission power is

And the density per unit area of the small cell base station is

And the storage probability of content i is

to be. Also, the location set of the cache small cell base station with content i in the k-th layer is

In this case, the location set of small cell base stations of all layers with content i is

to be.

의 푸아송 포인트 프로세스로 랜덤하게 위치할 수 있다. All cache small cell base stations have a single antenna, and base stations of each layer have a density per unit area in the 2D plane.

Poisson's point process can be located randomly.

In addition, an arbitrary receiver may request a content from a base station having the largest channel power gain among small cell base stations of all layers having content i. Then, the received signal power can be expressed as the following equation.

[Equation 2]

는 위치 x에 해당하는 스몰셀 기지국의 전송 파워이고,

는 해당 기지국의 계층을 의미하며,

는 임의 수신기에서 위치 x의 스몰셀 기지국까지 거리를 나타낸다.here,

Is the transmission power of the small cell base station corresponding to the location x,

Means the layer of the corresponding base station,

Denotes the distance from the random receiver to the small cell base station at location x.

를 가지는 모든 이종 스몰셀 기지국들을, 채널 파워의 역수가 증가하는 순서로 위치 집합

의 인덱스(index)를 다시 재구성하고, 정렬된 채널 파워 역수의 집합은 다음 식과 같이 나타낼 수 있다.contents

All heterogeneous small cell base stations having a are set in the order of increasing the reciprocal of the channel power

The index of is reconstructed again, and the set of aligned channel power reciprocals can be expressed as follows.

[Equation 3]

, where

, where

은 임의의 수신기와 채널 파워 역수가 n 번째 작은 스몰셀 기지국 사이의 채널 파워 역수이다.here,

Is a channel power reciprocal between an arbitrary receiver and a small cell base station having an n-th small channel power reciprocal.

를 요구할 때, 임의의 수신기와 서비스하는 스몰셀 기지국 사이의 신호대잡음비(Signal to Noise Ratio, SNR)은 다음 식과 같이 나타낼 수 있다.Any receiver (user)

When requesting, a signal-to-noise ratio (SNR) between an arbitrary receiver and a serving small cell base station can be expressed as follows.

[Equation 4]

In addition, an average cache hit probability may be defined as the following equation in order to consider whether content transmission is successful as a performance index.

[Equation 5]

,

,

는 컨텐츠

를 디코딩하기 위한 최소 타겟 신호대잡음비(SNR)이다.here,

The content

Is the minimum target signal-to-noise ratio (SNR) for decoding.

2 is a block diagram illustrating a random content storage optimization system in a heterogeneous small cell network according to an embodiment.

Referring to FIG. 2, in a random content storage optimization system 100 in a heterogeneous small cell network according to an embodiment, an initial value setting unit 110, an initial storage probability calculation unit 120, an update unit 130, and a constraint It may include a condition determination unit 140 and a storage probability determination unit 150.

In a heterogeneous small cell network capable of storing content and having different characteristics, the initial value setting unit 110 may set initial values of Lagrangian multipliers for all layers of the OSI 7 layer of the heterogeneous small cell base station. .

The storage probability initial value calculation unit 120 may calculate an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier.

After updating the value of the Lagrange multiplier, the updater 130 may update the storage probability of all contents in the layer. The update unit 130 updates the storage probability according to all contents as the value of the Lagrange multiplier is updated, and the probability in the other layer is changed because it is a probability function of the storage probability, which will affect the value of the Lagrange multiplier. I can.

The constraint condition determination unit 140 may check whether the sum of the storage probabilities in all layers satisfies the memory content constraint condition.

Here, the constraint determination unit 140 updates the value of the Lagrange multiplier through the update unit 130 until the sum of the storage probabilities in all layers satisfies the memory content constraint, and then stores all the contents of the layer. You can have the probability update.

The storage probability determiner 150 may finally determine the storage probability when the sum of the storage probabilities satisfies the memory content constraint condition. The storage probability determiner 150 can find an optimal storage probability that maximizes an average cache hit probability indicating whether the content is successfully transmitted, between the limited memories of the small cell cache base station of each layer. have.

In a heterogeneous small cell network, all heterogeneous small cell base stations have a single antenna, and the small cell base stations of each layer may be randomly located in a 2D plane by a Poisson point process.

In addition, an arbitrary receiver requests content, and a small cell base station having the largest channel power gain among heterogeneous small cell base stations of all layers having the content can transmit the content to an arbitrary receiver.

3 is a flowchart illustrating a method of optimizing storage of random contents in a heterogeneous small cell network according to an embodiment.

3, in the random content storage optimization method according to an embodiment, in a heterogeneous small cell network capable of storing contents and having different characteristics, a Lagrange multiplier for all layers of the OSI 7 layer of the heterogeneous small cell base station Setting an initial value (S110), calculating an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier (S120), updating the value of the Lagrange multiplier (S130), Updating the storage probability (S140), checking whether the sum of the storage probabilities at all layers satisfies the memory content constraint (S150), and when the sum of the storage probabilities satisfies the memory content constraint, It may be performed including the step of finally determining the storage probability (S160).

According to embodiments, it is possible to provide a method for storing random contents in a heterogeneous small cell network in which heterogeneous base stations having different characteristics are mixed, and to find an optimal storage probability of each content to maximize a cache hit probability.

Hereinafter, a method of optimizing storage of random content in a heterogeneous small cell network according to an embodiment will be described as an example. A method of optimizing random content storage in a heterogeneous small cell network according to an embodiment may be described in more detail through a random content storage optimization system in a heterogeneous small cell network according to the embodiment described with reference to FIG. 2. In a heterogeneous small cell network according to an embodiment, the random content storage optimization system may include an initial value setting unit, a storage probability initial value calculation unit, an update unit, a constraint condition determination unit, and a storage probability determination unit.

Optimization problem to find the optimal storage probability

의 CDF(Cumulative　Distribution　Function)는 다음 식과 같이 나타낼 수 있다. Channel power reciprocal

The CDF (Cumulative Distribution Function) of can be expressed as the following equation.

[Equation 6]

이다.here,

to be.

를 요구하고, 컨텐츠

를 가진 이종 스몰셀 기지국들 중 순간적인 채널 파워 이득(instantaneous channel power gain)이 가장 큰 기지국으로부터 컨텐츠

를 서비스 받을 때, nakagami-m 페이딩 채널 하에서, 평균 캐시 히트 확률은 다음 식과 같이 나타낼 수 있다.Random user content

And content

Contents from the base station with the largest instantaneous channel power gain among heterogeneous small cell base stations with

When receiving the service, under the nakagami-m fading channel, the average cache hit probability can be expressed as follows.

[Equation 7]

이다.here,

to be.

,

에 대해서, 평균 캐시 히트 확률을 최대화하기 위한 최적의 저장 확률은 다음 식의 최적화 문제로부터 찾을 수 있다.Therefore, the limited memory size of the small cell cache base station of each layer

,

For, the optimal storage probability for maximizing the average cache hit probability can be found from the optimization problem of the following equation.

[Equation 8]

,

,

Optimal storage probability algorithm

에 대해서, 함수

는 저장 확률

에 대해

이므로, convex 함수이다.Each content

Regarding, the function

Is the storage probability

About

So, it is a convex function.

Since the weighted sum of the convex function is a convex function, the above optimization problem is a constrained convex optimization problem, and has only one optimal solution.

The Lagrangian function for the optimization problem can be expressed as the following equation.

[Equation 9]

와

는 음수가 아닌 라그랑주 승수(Lagrangian multipliers)이다. here,

Wow

Are non-negative Lagrangian multipliers.

The Karush-Kuhn-Tucker (KKT) condition as follows for the optimal storage probability can be obtained, and can be expressed by conditional expressions 1 to 4.

[Equation 10]

(조건식 1)

(Conditional Expression 1)

[Equation 11]

(조건식 2)

(Conditional Expression 2)

[Equation 12]

(조건식 3)

(Conditional Expression 3)

[Equation 13]

(조건식 4)

(Conditional Expression 4)

From [Equation 11], which is Conditional Equation 2, optimal storage probabilities can be given by the following equation.

[Equation 14]

,

,

이고,

이다. here,

ego,

to be.

를 저장할 확률

도 저장 확률

의 변수이다. 따라서 계층 k에서 결정된 저장 확률

들은 다른 계층의 저장 확률에 영향을 미치고, 다른 계층의 저장 확률을 정할 때 반영될 수 있다. 이에 따라 반복적인(iterative) 알고리즘이 필요하다. One thing to note is the contents of small cell base stations of different layers.

Probability to save

The probability of saving the road

Is a variable of. Hence the storage probability determined at layer k

They affect the storage probability of another layer and can be reflected when determining the storage probability of another layer. Accordingly, an iterative algorithm is required.

에 대한 라그랑주 승수(Lagrangian multipliers)

와

의 범위는 다음 식과 같이 나타낼 수 있다.From conditional equations 1 to 4 (ie, [Equation 10] to [Equation 13]), the storage probability

Lagrangian multipliers for

Wow

The range of can be expressed as the following equation.

[Equation 15]

,For

,

[Equation 16]

For

<

<

[Equation 17]

For

는

의 함수로 표현되므로, 저장 확률

는 라그랑주 승수(Lagrangian multiplier)는

에 의해서만 결정될 수 있다.

Is

Since it is expressed as a function of

Is the Lagrangian multiplier

Can only be determined by

,

이라 가정한다. 주어진

,

에 대해서, 다음이 성립한다.

,

Assume this. given

,

Regarding, the following holds.

이면,

,

이므로

이 된다. - if

If,

,

Because of

Becomes.

이면,

,

이므로

이 된다.- if

If,

,

Because of

Becomes.

를 만족시키는

는

의 범위에 존재한다.

Satisfying

Is

Exists in the range of.

범위에서

만족시키는

를 찾으면 된다. therefore,

In range

Satisfying

You can find it.

를 업데이트 함에 따라 모든 컨텐츠

에 따라 저장 확률

를 업데이트하게 되며, 다른 계층에서의 확률

,

는 저장 확률

의 확률의 함수이므로 역시 변하게 되고, 이에 따라

가 변화하게 되며, 이는 다시

의 값에 영향을 미치게 된다. But,

All content as you update

Storage probability according to

Will be updated, and the probability at different layers

,

Is the storage probability

Since it is a function of the probability of

Will change, which again

Will affect the value of

Based on these facts, an iterative bisection algorithm for finding an optimal storage probability for maximizing the average cache hit probability can be expressed as follows.

[Iterative Algorithm]

In step S110, the initial value setting unit may set the initial value of the Lagrange multiplier for all layers in a heterogeneous small cell network capable of storing contents and having different characteristics. This can be expressed as follows.

에 대해

초기값 설정]Step 1 [

About

Initial value setting]

,

,

와

계층에 대한

범위의 양 끝점)(

Wow

For tier

Both ends of the range)

(

: 초기값)

(

: Initial value)

In step S120, the storage probability initial value calculation unit may calculate an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier.

(

) 초기값에 따른 모든 컨텐츠 저장 확률 초기값 계산]Step 2 [Step 1

(

) Calculate the initial value of the probability of storing all contents according to the initial value]

for

for

계산

Calculation

end

end

In steps S130 and S140, the update unit may update the value of the Lagrange multiplier and then update the storage probability of all contents in the layer. The update unit updates the storage probability according to all contents as the value of the Lagrange multiplier is updated, and since the probability in another layer is a probability function of the storage probability, the update unit may change the value of the Lagrange multiplier. This can be expressed as follows.

에 대해,

값 업데이트(update) 후

계층의 모든 컨텐츠 N의 저장 확률 업데이트(update)를 반복] Step 3 [

About,

After value update

Repeatedly updating the storage probability of all contents N of the layer]

for

, then

if

, then

, then

else if

, then

end if

(모든 파일 N에 대해서, k 계층(tier)의 저장 확률 업데이트(update))for

(For all files N, update the storage probability of the k tier)

업데이트

update

end

end

In step S150, the constraint determination unit may check whether the sum of the storage probabilities in all layers satisfies the memory content constraint. More specifically, the constraint determination unit may update the value of the Lagrange multiplier through the update unit until the sum of the storage probabilities in all layers satisfies the memory content constraint, and then update the storage probability of all contents in the layer. have. This can be expressed as follows.

Step 4. [Repeat Step 3 until the sum of probabilities in all layers satisfies the memory content constraint]

do (

: error tolerance level)while

do (

: error tolerance level)

Repeat: Step 3

end while

In step S160, when the sum of the storage probabilities satisfies the memory content constraint condition, the storage probability determiner may finally determine the storage probability. The storage probability determiner may find an optimal storage probability for maximizing an average cache hit probability indicating whether or not the content is successfully transmitted, between limited memories of the small cell cache base station of each layer. This can be expressed as follows.

Step 5. [Final decision on the storage probability value found in Step 4]

for

for

As described above, in a heterogeneous small cell network, all heterogeneous small cell base stations have a single antenna, and small cell base stations of each layer may be randomly located in a 2D plane by a Poisson point process.

In addition, an arbitrary receiver requests content, and a small cell base station having the largest channel power gain among heterogeneous small cell base stations of all layers having the content can transmit the content to an arbitrary receiver.

As described above, according to the embodiments, it is possible to provide a system model that is closer to reality and can be applied than a caching (storing) model in an existing small cell network. It is possible to provide an algorithm that is more realistic and applicable to various situations in the 　wireless edge caching 　 technology that can handle the wireless mobile traffic that will increase rapidly in recent years and in the future, and the wireless communication function can be greatly improved.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose computers or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. Can be embodyed in The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like.

As described above, although the embodiments have been described by the limited embodiments and drawings, various modifications and variations are possible from the above description by those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the claims to be described later.

Claims (12)

In a heterogeneous small cell network capable of storing contents and having different characteristics, setting initial values of Lagrangian multipliers for all layers of the OSI 7 layer of the heterogeneous small cell base station;
Calculating an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier;
Updating the value of the Lagrange multiplier and then updating the storage probability of all contents in the layer;
Checking whether the sum of the storage probabilities at all layers satisfies the memory content constraint; And
When the sum of the storage probabilities satisfies the memory content constraint, finally determining the storage probabilities
Containing, random content storage optimization method. The method of claim 1,
After updating the value of the Lagrange multiplier, updating the storage probability of all contents in the layer,
As the value of the Lagrangian multiplier is updated, the storage probability according to all contents is updated, and the probability in another layer is changed because it is a probability function of the storage probability, thus affecting the value of the Lagrange multiplier.
Characterized in, random content storage optimization method. The method of claim 1,
The step of checking whether the sum of the storage probabilities in all the layers satisfies the memory content constraint,
Updating the value of the Lagrange multiplier until the sum of the storage probabilities in all the layers satisfies the memory content constraint, and then updating the storage probabilities of all the contents of the layer.
Characterized in, random content storage optimization method. The method of claim 1,
The step of finally determining the storage probability,
Finding the optimal storage probability that maximizes the average cache hit probability indicating whether the content is successfully transmitted or not between the limited memory of the small cell cache base station of each layer
Characterized in, random content storage optimization method. The method of claim 1,
In the heterogeneous small cell network, all heterogeneous small cell base stations have a single antenna, and the small cell base stations of each layer are randomly located in a 2D plane by a Poisson point process.
Characterized in, random content storage optimization method. The method of claim 1,
An arbitrary receiver requests content, and a small cell base station having the largest channel power gain among heterogeneous small cell base stations of all layers having the content transmits the content to the arbitrary receiver.
Characterized in, random content storage optimization method. In a heterogeneous small cell network that can store contents and has different characteristics, set the initial value to set the initial value of Lagrangian multipliers for all layers of the OSI 7 layer of the heterogeneous small cell base station part;
A storage probability initial value calculation unit that calculates an initial value of the storage probability of all contents according to the initial value of the Lagrange multiplier;
An update unit for updating the storage probability of all contents in the layer after updating the value of the Lagrange multiplier;
A constraint condition determination unit for checking whether the sum of the storage probabilities in all layers satisfies the memory content constraint; And
When the sum of the storage probabilities satisfies a memory content constraint condition, a storage probability determination unit that finally determines the storage probability
Containing, random content storage optimization system. The method of claim 7,
The update unit,
As the value of the Lagrangian multiplier is updated, the storage probability according to all contents is updated, and the probability in another layer is changed because it is a probability function of the storage probability, thus affecting the value of the Lagrange multiplier.
Characterized in, random content storage optimization system. The method of claim 7,
The constraint condition determination unit,
Updating the value of the Lagrange multiplier through the update unit until the sum of the storage probabilities in all the layers satisfies the memory content constraint, and then updating the storage probabilities of all contents in the layer
Characterized in, random content storage optimization system. The method of claim 7,
The storage probability determination unit,
Finding the optimal storage probability that maximizes the average cache hit probability indicating whether the content is successfully transmitted or not between the limited memory of the small cell cache base station of each layer
Characterized in, random content storage optimization system. The method of claim 7,
In the heterogeneous small cell network, all heterogeneous small cell base stations have a single antenna, and the small cell base stations of each layer are randomly located in a 2D plane by a Poisson point process.
Characterized in, random content storage optimization system. The method of claim 7,
An arbitrary receiver requests content, and a small cell base station having the largest channel power gain among heterogeneous small cell base stations of all layers having the content transmits the content to the arbitrary receiver.
Characterized in, random content storage optimization system.

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