Resource auction method for multi-base-station and multi-slice scene of Internet of Vehicles

Abstract

The invention relates to a resource auction method for a multi-base-station and multi-slice scene of the Internet of Vehicles, and belongs to the field of resource management of the Internet of Vehicles. According to the multi-base-station multi-slice downlink transmission method, the network slice technology is applied, the influence of service requirements, time delays and vehicle mobility of different vehicles on network utility is fully considered, the purpose of maximizing system utility is achieved, and a multi-base-station multi-slice downlink transmission scene is constructed. The base station submits an auction request to the infrastructure provider as a function of the vehicle service demand, and the infrastructure provider determines a winner base station to maximize the overall utility of the system. Meanwhile, a resource allocation method based on auction is provided, resource allocation can be dynamically optimized in a multi-base-station and multi-slice environment, and it is ensured that auction has individual rationality and fairness. According to the method, the system spectrum efficiency and the slice service satisfaction rate are combined, the vehicle service quality is guaranteed, and the resource utilization efficiency is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of Internet of Vehicles resource management and relates to a resource auction method for a multi-base station and multi-slice scenario in an Internet of Vehicles. Background Art

[0002] The rapid development of intelligent transportation systems and vehicle networking technologies has made intelligent connected vehicles a research hotspot. Intelligent connected vehicles need to adopt super fifth-generation mobile communication technology or sixth-generation mobile communication technology. Intelligent connected vehicles can exchange information with surrounding vehicles and infrastructure to achieve a more comfortable environment and more accurate traffic information. Intelligent connected vehicles have different requirements for different services, including transmission rate, reliability and low latency. Existing 5.9GHz frequency band vehicle networking communication technologies, such as dedicated short-range communications and cellular vehicle networking, cannot fully meet the service needs of intelligent connected vehicles. Therefore, intelligent connected vehicles have put forward unprecedented requirements for the differentiated and customized service capabilities of the underlying network.

[0003] Unlike traditional networks, network slicing, as a logical network, has the characteristics of customizability, scalability and programmability. This flexibility provides operators with the possibility of improving resource efficiency, but it also brings great challenges to the resource management and orchestration of slices. When managing slice resources, it is necessary to consider the changes in business requirements over time. In order to cope with the fluctuations in service requirements, it is necessary to dynamically adjust the slice resource management strategy through an effective mechanism to ensure that the network slicing requirements of the wireless access network are met while the available radio resources are effectively utilized. However, in order to keep the slices running properly, it is necessary to ensure sufficient resource supply. Otherwise, the virtual network will not be able to guarantee the negotiated performance, resulting in the operator facing possible financial penalties for breach of contract. At the same time, the user's service quality may be significantly reduced or even completely lost. When managing slice resources, it is necessary to consider the changes in business requirements over time. In order to cope with the fluctuations in service requirements, it is necessary to dynamically adjust the slice resource management strategy through an effective mechanism to ensure that the network slicing requirements are met while the available radio resources are effectively utilized. In the vehicle mobility scenario with multiple base stations and multiple slices, the fluctuation of resource requirements may increase and make resource management between slices more challenging. Summary of the invention

[0004] In view of this, the purpose of the present invention is to: in a multi-base station and multi-slice Internet of Vehicles environment, how to achieve efficient allocation and management of network resources while meeting differentiated service requirements to improve the overall utility of the system. With the increase in service requirements of intelligent connected vehicles for communication rates, low latency, etc., the existing 5G / 6G network resource management methods are difficult to effectively support different vehicle service requirements in dynamic scenarios with multiple base stations and multiple slices. Network slice management and resource allocation in the Internet of Vehicles face complex dynamics, especially in terms of spectrum efficiency and service satisfaction rate. A new allocation mechanism is needed to ensure the rational use of resources and maintain service level agreements between slices. The current network slice resource allocation method mainly focuses on single base station scenarios, ignoring the importance of resource collaboration in multi-base station environments, and lacks a resource auction mechanism that can ensure fairness and rationality of participants.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A resource management method for a multi-base station and multi-slice scenario in an Internet of Vehicles, the method comprising the following steps:

[0007] S1: Obtain parameters such as the minimum rate and maximum delay of different vehicle services;

[0008] S2: The weighted sum of the spectrum resource utilization efficiency of the current base station and the satisfaction rate of the service level agreements of different slices is used as the base station utility function;

[0009] S3: Based on the base station utility function, a resource allocation model for multi-base station and multi-slice scenarios in the Internet of Vehicles is established with the goal of maximizing the system utility function;

[0010] S4: The base station determines whether the vehicle service requirements can be met;

[0011] S5: Use the auction mechanism to solve the established objective function and output the optimal resource allocation strategy.

[0012] Furthermore, in S1, the minimum rate and maximum delay of different vehicle services include: vehicle downlink data rate, the minimum transmission rate that the vehicle service slice can provide, and the maximum delay of the vehicle service slice.

[0013] Further, in S2, the weighted sum of the spectrum resource utilization efficiency of the current base station and the satisfaction rate of the service level agreements of different slices is used as the base station utility function:

[0014] The spectrum resource utilization efficiency of the current base station is defined as the ratio of the slice service vehicle downlink transmission rate to the slice bandwidth.

[0015] The service level agreement satisfaction rate of different slices is defined as the ratio of the number of successfully transmitted data packets of the slice to the total number of data packets sent by the slice.

[0016] The utility function of the base station is defined as the weighted sum of the spectrum resource utilization of the base station and the satisfaction rate of the service level agreements of different slices.

[0017] Furthermore, in S3, according to the base station utility function, a resource allocation model for multi-base station and multi-slice scenarios of Internet of Vehicles is established with the goal of maximizing the system utility function:

[0018] The system utility maximization function is defined as: maximizing the sum of all base station utility functions;

[0019] Furthermore, in S4, the base station determines whether the vehicle service business demand can be met:

[0020] The base station dynamically adjusts the allocation strategy based on the resource requirements of the vehicle business, which can be divided into two situations: (1) the base station resources can meet the needs of the slice; (2) the base station resources cannot meet the needs of the slice. The infrastructure provider updates the remaining system resources based on the resource allocation strategy of the base station. The infrastructure provider obtains profits by auctioning the remaining system resources. The base station with insufficient resources provides services to the vehicle by purchasing the remaining system resources to meet the slice requirements.

[0021] Furthermore, in S5, the established objective function is solved using an auction mechanism to output the optimal resource allocation strategy:

[0022] Define the auction mechanism as a tuple (X,Y), where X = {x1,...,x m} is the set of binary decision functions of all base stations, x m =1 means BSm wins this round of auction and can obtain the required resources, x m =0 means that base station m fails in this round of auction. m} is a set of payment rules for the rewards that base stations need to pay to obtain spectrum resources. represents the auction request of base station m, where is the amount of resources requested by base station m, is the bid of base station m. However, base stations are always selfish and hope to maximize their own utility. For example, base station m can increase the probability of winning by making a higher false bid. The false bid of the base station may reduce the efficiency of resource allocation and even reduce the utility of the system. m =(w m ,σ m ) represents the real request of BSm.

[0023] In each auction round, Indicates that the auctioneer collects requests from all participants. Represents all auction requests except base station m in this round of auction. It represents the remuneration that base station m needs to pay in this round of auction according to the payment rules.

[0024] In order to determine the winner from the participants, a unit price is introduced for each BS m, which is expressed as the ratio of the bid to the requested resources.

[0025] The base station must truthfully present its real request so that the base station can obtain greater utility in the real auction mechanism without considering the requests of other base stations. Individual rationality and authenticity ensure that the base stations are willing to declare their real needs. In order to achieve these two functions, the present invention designs a monotonic selection strategy based on the critical value theory to determine the winner of the auction mechanism. At the same time, the payment fee of each winner is determined by the payment rule. The payment rule is defined as follows:

[0026] In the auction mechanism, the auction request set for the base station A preference relation is introduced, defined as >. or Can get This means that if the bid of base station m is higher or the required amount of resources is less, the auction request Auction Request More advantageous. When base station m claims the auction request The fee to be selected as the winner is If base station m is the loser, the fee it needs to pay is 0. The critical value is the minimum bid of base station m when obtaining resources. When the auction is in progress, the infrastructure provider will collect the auction request set of the current auction participation mechanism. like If not empty, the auction request collection Find the unit price u m Largest auction request This determines the winner of the current round of auctions and generates corresponding allocation decisions based on the payment rules. After obtaining the preliminary winner decision, the infrastructure provider continues to update the payment set Y and the decision vector set X according to the unit price of the base station to finally determine the payment amount.

[0027] The beneficial effects of the present invention are:

[0028] First, the present invention fully considers the business requirements, latency and vehicle mobility of different vehicle services, and can meet the differentiated requirements of different vehicle services.

[0029] Second, the present invention combines the system spectrum efficiency with the slice service satisfaction rate, which not only ensures the service quality of the vehicle, but also effectively improves the resource utilization.

[0030] Third, the auction mechanism proposed by the present invention can ensure the authenticity and individual rationality of auction participants.

[0031] Fourth, through the optimization of the auction mechanism, the present invention can select the best resource allocation plan for each type of slice service, achieve resource conservation and maximize system benefits while meeting the latency and reliability requirements of each slice, ensure the differentiated requirements of service quality in the Internet of Vehicles system and make full use of network resources.

[0032] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 This is the auction flow chart of the present invention. DETAILED DESCRIPTION

[0035] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0036] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0037] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0038] like Figure 1 As shown, the resource management method for multi-base station multi-slice scenarios of the Internet of Vehicles of the present invention includes the following steps:

[0039] S1. Obtain vehicle business parameters

[0040] In the present invention, the base station counts vehicle service requirements in real time, such as vehicle downlink data rate and maximum delay of vehicle service slices, and updates the slice resource allocation strategy in real time according to the vehicle's requirements.

[0041] S2. Constructing base station utility function

[0042] In vehicle wireless networks, since spectrum resources are scarce, spectrum efficiency is used to measure the spectrum utilization of base stations. Spectral efficiency is defined as the ratio of vehicle downlink transmission rate to spectrum resources obtained by vehicles. The slice service level agreement is used to reflect the service quality perceived by vehicles. The slice service level agreement is defined as the percentage of successfully sent packets in the total sent packets of the slice. Therefore, the base station utility function consists of the weighted sum of spectrum efficiency and slice service level agreement.

[0043] S3. Constructing system utility function

[0044] In practice, the wireless access network in the 5G mobile system is considered to be a dense cellular network due to the use of higher frequency bands and smaller coverage. The goal of the present invention is to maximize the system utility in the resource allocation process, so after determining the utility function of a single base station, the optimal goal is to maximize the sum of the utility functions of all base stations.

[0045] S4. Whether it can meet the vehicle service needs

[0046] The present invention mainly focuses on resource allocation in multi-base station multi-slice scenarios. The infrastructure provider updates the remaining system resources according to the resource allocation strategy of the base station. The infrastructure provider obtains profits by auctioning the remaining system resources. The base stations with insufficient resources provide services to vehicles by purchasing the remaining system resources to meet the slicing requirements.

[0047] S5. Auction Solution

[0048] The infrastructure provider acts as the auctioneer and the base station acts as the bidder. In each round of auction, the infrastructure provider announces the resources for this round of auction. The base station submits an auction request to the infrastructure provider based on the service needs of the vehicle, which includes the required resources and bid. During the auction, the infrastructure provider collects the auction requests from the base station. The auction will continue within the tolerance time of the base station until no other base station submits an auction request. The infrastructure provider will then sort the auction requests according to the unit price and select the base station with the highest unit price as the winner. For the winner, the auctioneer will allocate resources to it according to the auction request and charge it according to the payment rules.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A resource auction method for multi-base station and multi-slice scenarios in an Internet of Vehicles, characterized by: The method comprises the following steps: S1: Obtain parameters such as the minimum rate and maximum delay of different vehicle services; S2: The weighted sum of the spectrum resource utilization efficiency of the current base station and the satisfaction rate of the service level agreements of different slices is used as the base station utility function; S3: Based on the base station utility function, a resource allocation model for multi-base station and multi-slice scenarios in the Internet of Vehicles is established with the goal of maximizing the system utility function; S4: The base station determines whether the vehicle service requirements can be met; S5: Use the auction mechanism to solve the established objective function and output the optimal resource allocation strategy.

2. According to claim 1, a resource auction method for multi-base station and multi-slice scenarios in Internet of Vehicles is characterized by: In S1, the minimum rate and maximum delay parameters of different vehicle services include: vehicle downlink data rate, the minimum transmission rate that the vehicle service slice can provide, and the maximum delay of the vehicle service slice.

3. According to claim 2, a resource auction method for multi-base station and multi-slice scenarios in Internet of Vehicles is characterized by: In S2, the weighted sum of the spectrum resource utilization efficiency of the current base station and the satisfaction rate of the service level agreements of different slices is used as the base station utility function: The spectrum resource utilization efficiency of the current base station is defined as: the ratio of the downlink transmission rate of the slice service vehicle to the slice bandwidth; The service level agreement satisfaction rate of different slices is defined as: the ratio of the data packets successfully transmitted by the slice to the total data packets sent by the slice; The utility function of the base station is defined as the weighted sum of the spectrum resource utilization of the base station and the satisfaction rate of the service level agreements of different slices.

4. The resource management method for multi-base station and multi-slice scenarios of Internet of Vehicles according to claim 3 is characterized in that: According to the base station utility function, a resource allocation model for multi-base station and multi-slice scenarios in the Internet of Vehicles is established with the goal of maximizing the system utility function: The system utility maximization function is defined as: maximizing the sum of all base station utility functions.

5. According to claim 4, a resource auction method for multi-base station and multi-slice scenarios in Internet of Vehicles is characterized by: In S4, the base station determines whether the vehicle service business demand can be met: The base station dynamically adjusts the allocation strategy based on the resource requirements of the vehicle business, which can be divided into two situations: (1) The base station resources can meet the needs of slicing; (2) The base station resources cannot meet the slicing requirements; The infrastructure provider updates the remaining system resources according to the resource allocation strategy of the base station. The infrastructure provider obtains profits by auctioning the remaining system resources. The base stations with insufficient resources provide services to vehicles by purchasing the remaining system resources to meet the slicing requirements.

6. The resource management method for multi-base station and multi-slice scenarios of Internet of Vehicles according to claim 5 is characterized in that: The S5 is specifically: Define the auction mechanism as a tuple (X,Y), where X = {x1,...,x m } is the set of binary decision functions of all base stations, x m =1 means that base station m wins this round of auction and can obtain the required resources, x m =0 means that base station m fails in this round of auction; Y = {Y1,...,Y m } is the set of payment rules that the base station needs to pay for the reward to obtain spectrum resources; represents the auction request of base station m, where is the amount of resources requested by base station m, is the bid of base station m; The base station is selfish and hopes to maximize its own utility. Base station m increases the probability of winning by making a higher false bid. The false bid of the base station leads to a decrease in the efficiency of resource allocation. m =(w m ,σ m ) represents the real request of BSm; In each auction round, It means that the auctioneer collects all the participants' requests; represents all auction requests except base station m in this round of auction; It represents the remuneration that base station m needs to pay in this round of auction according to the payment rules; To determine the winner from the participants, a unit price is introduced for each BSm, which is expressed as the ratio of the bid price to the requested resource amount; A monotonic selection strategy is designed based on the critical value theory to determine the winner of the auction mechanism. At the same time, the payment fee of each winner is determined by the payment rule; the payment rule is defined as follows: In the auction mechanism, the auction request set for the base station A preference relation is introduced, defined as >; if or get If the bid of base station m is higher or the required resources are less, the auction request Auction Request More advantageous; when base station m claims the auction request The fee to be selected as the winner is If base station m is the loser, the fee it needs to pay is 0; the critical value is the lowest bid of base station m when obtaining resources; When an auction is in progress, the infrastructure provider will collect the auction request set of the current auction participation mechanism. like If not empty, the auction request collection Find the unit price u m Largest auction request This determines the winner of the current round of auctions and generates corresponding allocation decisions based on the payment rules. After obtaining the preliminary winner decision, the infrastructure provider continues to update the payment set Y and the decision vector set X according to the unit price of the base station to finally determine the payment amount.

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