Multi-service edge caching method based on Stackelberg game

Abstract

The invention discloses a multi-service edge caching method based on a Stackelberg game. The multi-service edge caching method comprises the following steps that 1, a base station collects application demand information and channel gain information of users in a network; 2, the base station enables the users requiring the same application service to form a NOMA cluster according to different application services required by the users; step 3, the base station adopts a Stackelberg game mechanism to make a decision for the optimal pricing of a plurality of NOMA clusters requiring different application services; and step 4, the base station selects the application services required by the lowest K followers in the optimal pricing # imgabs0 # given in the step 3 to perform edge caching according to the quantity K of applications, which can be cached by hardware of the base station, of the edge cache. According to the method, the situation that multiple users need the same application service is considered at the same time, so that the cache selection problem that the multiple application services can only cache part of the application services due to edge cache hardware is solved, and the method physically conforms to the scene of practical application and can be effectively applied to engineering practice.

Description

Technical Field

[0001] The present invention relates to the technical field of edge caching in wireless communications, and in particular to a multi-service edge caching method based on Stackelberg game. Background Art

[0002] In wireless networks, edge cache (EC) technology shortens the distance between files and users by caching popular files at the edge of the network, effectively reducing the delay when users obtain files, and greatly alleviating the backhaul capacity problem. Therefore, at the edge of the network, that is, the base station, an edge cache is set up to cache the application service data required by users. Edge cache uses a variety of cache strategies to determine which content should be cached and where to cache. Common strategies include content-based caching (such as making cache decisions based on the popularity and importance of the content) and user behavior-based caching (such as making cache decisions based on the user's access pattern and needs). However, due to the limitation of cache hardware, only a limited number of application service data in the network can be cached. Therefore, when the types of application services required by users are more than the upper limit of the number of application services that the cache hardware can carry, the base station will not be able to accept the overflow of application service types. Therefore, the reasonable allocation of edge caches for multiple application requirements of multiple users is a key issue that needs to be solved urgently. Summary of the invention

[0003] Purpose of the invention: The present invention provides a multi-service edge caching method based on Stackelberg game to solve the cache selection problem of multiple application services because the edge cache hardware can only cache part of the application services.

[0004] Technical solution: The multi-service edge caching method based on Stackelberg game described in the present invention comprises the following steps:

[0005] Step 1: The base station collects application demand information and channel gain information of users in the network;

[0006] Step 2: The base station groups users who need the same application service into a NOMA cluster according to the different application services required by the users.

[0007] Step 3: The base station uses the Stackelberg game mechanism to make decisions on the optimal pricing of multiple NOMA clusters with different application services;

[0008] Step 4: The base station selects the optimal pricing given in step 3 based on the number of applications K that can be cached by the edge cache due to its own hardware. The application services required by the lowest K followers are cached at the edge.

[0009] Furthermore, in step 2, users who require the same application service n form the same NOMA cluster. The purpose of each NOMA cluster follower purchasing the limited cache resources of the edge cache in the game mechanism is to improve the total transmission rate of its data service. The NOMA clusters composed of users are

[0010]

[0011] Where: p i is the power value allocated by the base station to user i, σ 2 is the noise at the user receiver, θ n is the set of users who require the nth application.

[0012] Furthermore, in step 3, the base station, as the leader of the two parties in the game, sells limited resources, while the NOMA clusters with different application services, as the buyers in the game, obtain benefits by purchasing limited resources.

[0013] Furthermore, the seller utility function in the game model is defined as the total cost paid by all NOMA clusters to purchase resources.

[0014] Furthermore, the base station as a seller in the game model assigns the same resource unit price to multiple users who require the same application service, and assigns different resource unit prices to users who require different application services.

[0015] Furthermore, the utility function of the buyer in the game model is defined as the difference between the total transmission rate of the NOMA cluster and the price paid for purchasing base station resources, that is, Where: p i is the power value allocated by the base station to user i, σ 2 is the noise at the user receiver, β n It represents the unit resource price of the base station sold to the NOMA cluster composed of users who need the same application service n. It is the price paid by a NOMA cluster consisting of users who require the same application service n to purchase resources.

[0016] Further, in step 4, pricing The lower the value, the more willing followers are to buy resources to gain benefits, and the better the price. Arrange in ascending order. The closer the follower is, the more the NOMA cluster composed of users who need the application service wants to get the cache of the application service they need. If the base station knows that the number of applications that the edge cache can cache based on its own hardware is K, then select The application services required by the lowest K followers are cached at the edge.

[0017] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the present invention takes into account the needs of multiple users for the same application service at the same time, so as to solve the cache selection problem of multiple application services because the edge cache hardware can only cache part of the application services. It physically conforms to the scenarios of real applications and can be effectively applied to actual engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0019] like Figure 1 As shown, a multi-service edge caching method based on Stackelberg game includes the following steps:

[0020] Step 1: The base station collects application demand information and channel gain information of users in the network;

[0021] Step 2: The base station groups users who need the same application service into a NOMA cluster according to the different application services required by the users.

[0022] Step 3: The base station uses the Stackelberg game mechanism to make decisions on the optimal pricing of multiple NOMA clusters with different application services;

[0023] Step 4: The base station selects the optimal pricing given in step 3 based on the number of applications K that can be cached by the edge cache due to its own hardware. The application services required by the lowest K followers are cached at the edge;

[0024] The Stackelberg game mechanism described in step 3 is to model the edge caching problem of multi-user requirements and multi-services as an economic game model for solution.

[0025] In the game, the base station, as the leader, sells limited resources, while the NOMA clusters with different application needs, as the buyers, obtain benefits by purchasing limited resources.

[0026] The buyer in the game is a single NOMA cluster consisting of all users who require the same application service.

[0027] In the game model, the seller utility function is defined as the total cost paid by all NOMA clusters to purchase resources.

[0028] In the game model, the base station as the seller assigns the same resource unit price to multiple users who require the same application service, and assigns different resource unit prices to users who require different application services.

[0029] The utility function of the buyer in the game model is defined as the difference between the total transmission rate of the NOMA cluster and the price paid for purchasing base station resources, that is,

[0030] In step 3, the seller can obtain the optimal unit resource pricing Then, in step 4, select The application services required by the lowest K followers are cached at the edge.

[0031] Combination Figure 1 As shown, the design scheme of the present invention is further specifically analyzed and described.

[0032] In the base station described in the scheme of the present invention, it is assumed that there are M user terminals defined as The channel gain from the base station to these users is expressed as Without loss of generality The users to the base station are arranged in descending order of channel gain, i.e. h1>…>h m >…>h M In a wireless network, assume that there are N sets of application service requirements. At the same time, the user set that requires the nth application is defined as θ n Since each user only needs one application business data at a time, In the present invention, users who require the same application service use non-orthogonal multiple access technology to perform downlink data transmission on the same subcarrier, while users who require different application services use orthogonal multiple access technology to effectively suppress co-channel interference and prevent the SIC decoding level from being too high.

[0033] In the present invention, considering that the backhaul link of the base station is limited, the base station cannot exchange data with the core network in real time. Edge caching technology shortens the distance between files and users by caching popular files at the edge of the network, effectively reducing the delay when users obtain files, and the backhaul capacity problem is also greatly alleviated. Therefore, at the edge of the network, that is, the base station, an edge cache is provided for caching application service data required by users. However, due to the limitation of the cache hardware, only data of a limited number of application services in the network can be cached. Therefore, when the types of application services required by users exceed the upper limit of the number of application services that the cache hardware can carry, the base station will not be able to accept the requirements of the overflowing application service types. Therefore, the reasonable allocation of edge caches for multiple application requirements of multiple users is a key issue that needs to be solved urgently.

[0034] Considering that users all want to purchase the cache resources of the base station edge cache to cache the application services they need, the base station can send data to them through the downlink, thereby meeting their business needs. Therefore, in a multi-service edge cache solution based on Stackelberg game provided by the present invention, users with the same application business needs are in a cooperative relationship in the issue of purchasing the limited cache resources of the base station edge cache, and their purpose is to obtain the edge cache to cache their required applications; while users with different application business needs are in a competitive relationship in the issue of purchasing the limited cache resources of the base station edge cache. The Stackelberg game is a pure strategy non-cooperative sequential game model in economics. According to the priority of the action and the completeness of the information mastered, the participants of the game can be divided into leaders and followers. Followers only have partial information and take the lead; while the leader takes action later because he has all the information of the followers. Among them, the leader needs to consider the optimal response of the followers when setting the game strategy, and the followers give their own optimal resource purchase size according to the optimal decision of the leader. In the present invention, since the base station has global information and limited cache resources of the edge cache, the base station is used as the leader of the game model, and the non-orthogonal multiple access cluster composed of users with the same application service who only have partial information acts as followers.

[0035] In the present invention, users who require the same application service n form the same NOMA cluster. The purpose of each NOMA cluster (follower) purchasing the limited cache resources of the edge cache in the game mechanism is to improve the total transmission rate of its data service. The NOMA clusters composed of users are

[0036]

[0037] Where: p i is the power value allocated by the base station to user i, σ 2 is the noise at the user receiver.

[0038] The base station allocates power to NOMA cluster users for downlink data transmission. Therefore, the utility function of the NOMA cluster composed of each user who requires the same application service n as a game follower consists of two parts, namely the total rate and the cost paid for purchasing resources, which is

[0039]

[0040] Where: β n represents the unit resource price of the base station sold to the NOMA cluster consisting of users who need the same application service n, so It is the price paid by a NOMA cluster consisting of users who require the same application service n to purchase resources.

[0041] As followers purchase more resources, their benefits will also increase, but their cost function will also increase. For followers in the game model, their goal is to obtain the highest benefits at the lowest cost in the game behavior, that is, to maximize their own utility function,

[0042]

[0043] The seller leader in the game relationship, i.e., the base station, sells the limited cache resources of the edge cache to multiple competing NOMA clusters for their data transmission. Therefore, its objective function is defined as the total cost paid by all NOMA clusters to purchase resources, which is

[0044]

[0045] Since the power of the base station is limited, the leader in the Stackelberg game relationship has the constraint:

[0046]

[0047] Among them, p max The maximum downlink power of the base station.

[0048] In the game, the base station as the leader aims to gain profits by selling limited resources. Therefore, the seller's goal in selling is to maximize its own utility function while considering constraints. Therefore, for the leader,

[0049]

[0050]

[0051] The optimization problems at followers and leaders together constitute the Stackelberg game. The final Stackelberg equilibrium can be obtained by the two parties of the game, the leader (base station) and followers (NOMA cluster composed of users who need the same application service) playing games according to certain rules. That is, the optimal unit resource pricing is obtained by maximizing the utility function representing the interests of the base station and the users with a competitive relationship.

[0052] From economics, we know that pricing The lower the value, the stronger the willingness of follower n to buy resources to obtain benefits. Therefore, the optimal pricing obtained in the present invention is Arrange in ascending order. The closer the followers are, the more likely the NOMA cluster consisting of users who need the application service wants to get the cache of the application service they need. Furthermore, if the base station knows that the number of applications that the edge cache can cache based on its own hardware is K, then select The application services required by the lowest K followers are cached at the edge.

Claims

1. A multi-service edge caching method based on Stackelberg game, characterized in that: The steps include: Step 1: The base station collects application demand information and channel gain information of users in the network; Step 2: The base station groups users who need the same application service into a NOMA cluster according to the different application services required by the users. Step 3: The base station uses the Stackelberg game mechanism to make decisions on the optimal pricing of multiple NOMA clusters with different application services; Step 4: The base station selects the optimal pricing given in step 3 based on the number of applications K that can be cached by the edge cache due to its own hardware. The application services required by the lowest K followers are cached at the edge.

2. The multi-service edge caching method based on Stackelberg game as claimed in claim 1, characterized in that: In step 2, users who need the same application service n form the same NOMA cluster. The purpose of each NOMA cluster follower purchasing the limited cache resources of the edge cache in the game mechanism is to improve the total transmission rate of its data service. The NOMA clusters composed of users are Where: p i is the power value allocated by the base station to user i, σ 2 is the noise at the user receiver, θ n is the set of users who need the nth application, h i is the channel gain.

3. The multi-service edge caching method based on Stackelberg game as claimed in claim 1, characterized in that: In step 3, the base station, as the leader of the two parties in the game, sells limited resources, while the NOMA clusters with different application services, as the buyers in the game, obtain benefits by purchasing limited resources.

4. The multi-service edge caching method based on Stackelberg game as claimed in claim 1, characterized in that: The seller utility function in the game model is defined as the total cost paid by all NOMA clusters to purchase resources 5. The multi-service edge caching method based on Stackelberg game as claimed in claim 1, characterized in that: In the game model, the base station as the seller assigns the same resource unit price to multiple users who require the same application service, and assigns different resource unit prices to users who require different application services.

6. The multi-service edge caching method based on Stackelberg game as claimed in claim 1, characterized in that: The utility function of the buyer in the game model is defined as the difference between the total transmission rate of the NOMA cluster and the price paid for purchasing base station resources, which is Where: p i is the power value allocated by the base station to user i, σ 2 is the noise at the user receiver, β n It represents the unit resource price of the base station sold to the NOMA cluster composed of users who need the same application service n. It is the price paid by a NOMA cluster consisting of users who require the same application service n to purchase resources.

7. The multi-service edge caching method based on Stackelberg game as claimed in claim 1, characterized in that: Pricing The lower the value, the more willing followers are to buy resources to gain benefits, and the better the price. Arrange in ascending order. The closer the follower is, the more the NOMA cluster composed of users who need the application service wants to get the cache of the application service they need. If the base station knows that the number of applications that the edge cache can cache based on its own hardware is K, then select The application services required by the lowest K followers are cached at the edge.

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