A resource auction method for multi-base station and multi-slice scenarios in vehicle-to-everything (V2X) networks

Abstract

This invention relates to a resource auction method for multi-base station, multi-slice scenarios in vehicle-to-everything (V2X) networks, belonging to the field of V2X resource management. This invention utilizes network slicing technology, fully considering the service needs, latency, and mobility of different vehicles on network utility to maximize system utility, and constructs a downlink transmission scenario with multiple base stations and multiple slices. Base stations submit auction requests to the infrastructure provider based on vehicle service needs, and the infrastructure provider determines the winning base station to maximize the overall system utility. Simultaneously, an auction-based resource allocation method is proposed, which can dynamically optimize resource allocation in a multi-base station, multi-slice environment, while ensuring individual rationality and fairness in the auction. This method combines system spectral efficiency with slice service satisfaction rate, guaranteeing vehicle service quality while effectively improving resource utilization efficiency.

Description

Technical Field

[0001] This invention belongs to the field of vehicle network resource management and relates to a resource auction method for vehicle network multi-base station multi-slice scenarios. Background Technology

[0002] The rapid development of intelligent transportation systems and vehicle-to-everything (V2X) technologies has made intelligent connected vehicles a research hotspot. Intelligent connected vehicles require the use of 5G+ or 6G mobile communication technologies. They can interact with surrounding vehicles and infrastructure to achieve a more comfortable environment and more accurate traffic information. Intelligent connected vehicles have different requirements for various services, including transmission speed, reliability, and low latency. Existing 5.9GHz band V2X communication technologies, such as dedicated short-range communication and cellular V2X, cannot fully meet the service needs of intelligent connected vehicles. Therefore, intelligent connected vehicles place unprecedented demands on the differentiated and customized service capabilities of the underlying network.

[0003] Unlike traditional networks, network slicing, as a logical network, possesses characteristics such as customizability, scalability, and programmability. This flexibility provides operators with the possibility of improving resource efficiency, but it also brings significant challenges to slice resource management and orchestration. When managing slice resources, it is necessary to consider the changing service demands over time. To cope with fluctuations in service demand, effective mechanisms are needed to dynamically adjust slice resource management strategies to ensure that the requirements of the radio access network slice are met while effectively utilizing available radio resources. However, to maintain the normal operation of the slice, sufficient resource supply must be ensured. Otherwise, the virtual network will be unable to guarantee negotiation performance, potentially leading to economic penalties for operator default. Simultaneously, the quality of service for users may be significantly reduced or even completely lost. In multi-base station, multi-slice vehicular mobility scenarios, fluctuations in resource demand may increase, making inter-slice resource management even more challenging. Summary of the Invention

[0004] In view of this, the purpose of this invention is to improve the overall system utility by efficiently allocating and managing network resources in a multi-base station, multi-slice vehicle-to-everything (V2X) environment while meeting differentiated service needs. With the increasing demands of intelligent connected vehicles for communication speeds and low latency, existing 5G / 6G network resource management methods are insufficient to effectively support the diverse service needs of different vehicles in dynamic scenarios with multiple base stations and slices. Network slice management and resource allocation in V2X face complex dynamics, particularly in terms of spectrum efficiency and service satisfaction. New allocation mechanisms are needed to ensure the rational utilization of resources and maintain service level agreements (SLAs) between slices. Current network slice resource allocation methods mainly focus on single-base station scenarios, neglecting the importance of resource collaboration in multi-base station environments, and lacking resource auction mechanisms that ensure fairness and rational participation.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A resource management method for multi-base station, multi-slice scenarios in vehicle-to-everything (V2X) networks, comprising the following steps:

[0007] S1: Obtain parameters such as minimum speed and maximum latency for different vehicle services;

[0008] S2: The base station utility function is a weighted sum of the current base station's spectrum resource utilization efficiency and the satisfaction rate of different slice service level protocols.

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

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

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

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

[0013] Furthermore, in S2, the base station utility function is a weighted sum of the current base station's spectrum resource utilization efficiency and the satisfaction rates of different slice service level protocols:

[0014] 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.

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

[0016] The utility function of a base station is defined as the weighted sum of the base station's spectrum resource utilization and the satisfaction rate of different slice service level protocols.

[0017] Furthermore, in step S3, a resource allocation model for a multi-base station, multi-slice scenario in the vehicle-to-everything (V2X) network is established based on the base station utility function, with the goal of maximizing the system utility function.

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

[0019] Furthermore, in step S4, the base station determines whether it can meet the vehicle service requirements:

[0020] The base station dynamically adjusts its resource allocation strategy based on the resource requirements of vehicle services, falling into two categories: (1) the base station's resources can meet the slicing requirements; (2) the base station's resources cannot meet the slicing requirements. The infrastructure provider updates the remaining system resources according to the base station's resource allocation strategy. The infrastructure provider profits by auctioning off the remaining system resources, and base stations with insufficient resources purchase the remaining system resources to provide services to vehicles, thus meeting the slicing requirements.

[0021] Furthermore, in step S5, an auction mechanism is used to solve the established objective function and output the optimal resource allocation strategy.

[0022] The auction mechanism is defined as a tuple (X,Y), where X={x1,...,x...} m} is the set of binary decision functions for all base stations, x m =1 indicates that BSM wins this round of auction and can obtain the required resources, x m =0 indicates that base station m failed in this round of auction. Y = {Y1,...,Y} m} is the set of payment rules that a base station must pay as a reward to obtain spectrum resources. This represents the auction request for base station m, where Let m be the amount of resources requested by base station m. Let π be the bid of base station m. However, base stations are always selfish, aiming to maximize their own utility. For example, base station m can increase its probability of winning by submitting a higher, spurious bid. Spurious bids by base stations can lead to reduced efficiency in resource allocation, or even decrease the overall system utility. m =(w m ,σ m This indicates the actual request from BSM.

[0023] In each round of auction, This indicates that the auctioneer has collected requests from all participants. This indicates all auction requests in this round of auctions except for base station m. This represents the remuneration that base station m needs to pay in this round of auction according to the payment rules.

[0024] To determine the winner among the participants, a unit price is introduced for each BS m, which is represented as the ratio of the bid to the requested resource.

[0025] Base stations must truthfully state their genuine requests so that they can obtain greater utility in a genuine auction mechanism, without considering the requests of other base stations. Individual rationality and authenticity ensure that base stations are willing to declare their true needs. To achieve these two functions, this invention designs a monotonic selection strategy based on critical value theory to determine the winner of the auction mechanism. Simultaneously, the payment fee for each winner is determined by the payment rules. The payment rules are defined as follows:

[0026] In the auction mechanism, this refers to the set of auction requests for base stations. A preference relation, defined as >, is introduced. If... or It can be obtained This means that if base station m bids higher or requires less resources, the auction request will proceed. Auction Request It has a greater advantage. When base station m claims an auction request The fee that needs to be paid when selected as the winner is If base station m is the loser, the cost is 0. The threshold is the minimum bid that base station m must make to acquire resources. During the auction, the infrastructure provider collects the set of auction requests from the current auction participation mechanism. like If not empty, then in the auction request set Find the unit price u m Largest auction request This determines the winner of the current round of the auction and generates a corresponding allocation decision based on the payment rules. After obtaining the initial winner decision, the infrastructure provider continues to update the payment set Y and the decision vector set X based on the unit price of the base station in order to finally determine the payment amount.

[0027] The beneficial effects of this invention are as follows:

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

[0029] Secondly, this invention combines system spectral efficiency with slice service satisfaction rate, which not only ensures the service quality of vehicles, but also effectively improves resource utilization.

[0030] Third, the auction mechanism proposed in this invention can guarantee the authenticity and individual rationality of auction participants.

[0031] Fourth, by optimizing the auction mechanism, this invention can select the best resource allocation scheme for various slice services, achieving resource conservation and maximizing system benefits while meeting the latency and reliability requirements of each slice, ensuring the differentiated service quality needs in the vehicle network system and making full use of network resources.

[0032] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0034] Figure 1 This is a flowchart of the auction process for this invention. Detailed Implementation

[0035] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed 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 are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0036] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0037] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0038] like Figure 1 As shown, the resource management method for multi-base station and multi-slice scenarios in vehicle-to-everything (V2X) networks of the present invention includes the following steps:

[0039] S1. Obtain vehicle service parameters

[0040] In this invention, the base station statistically analyzes vehicle service demands in real time, such as vehicle downlink data rate and maximum latency of vehicle service slices. The base station updates the slice resource allocation strategy in real time based on vehicle demands.

[0041] S2. Constructing the base station utility function

[0042] In vehicular wireless networks, spectrum resources are scarce. Spectrum efficiency is used to measure the spectrum utilization of base stations, defined as the ratio of the vehicle's downlink transmission rate to the spectrum resources available to the vehicle. The slice service level protocol reflects the vehicle's perceived quality of service, defined as the percentage of successfully transmitted packets out of the total packets transmitted in a slice. Therefore, the base station utility function consists of a weighted sum of spectrum efficiency and slice service level protocol.

[0043] S3. Constructing the system utility function

[0044] In practice, due to the use of higher frequency bands and smaller coverage areas, the radio access network in 5G mobile systems is considered a dense cellular network. The objective of this invention is to maximize system utility during resource allocation; therefore, after determining the utility function of a single base station, the optimal objective is to maximize the sum of the utility functions of all base stations.

[0045] S4. Can it meet vehicle service needs?

[0046] This invention primarily focuses on resource allocation in multi-base station, multi-slice scenarios. The infrastructure provider updates the remaining system resources according to the base station's resource allocation strategy. The infrastructure provider profits by auctioning off the remaining system resources, and base stations with insufficient resources purchase these resources to provide services to vehicles, thus fulfilling slicing requirements.

[0047] S5, Auction Solution

[0048] The infrastructure provider acts as the auctioneer, and the base stations act as bidders. In each round of the auction, the infrastructure provider announces the resources available for that round. Base stations submit auction requests to the infrastructure provider based on the service needs of the vehicles, including the required resources and their bids. During the auction, the infrastructure provider collects the auction requests from the base stations. The auction continues within the base station's tolerance period until no other base station submits an auction request. The infrastructure provider then sorts the auction requests according to their unit price and selects the base station with the highest unit price as the winner. For the winner, the auctioneer allocates resources to them according to their auction requests and charges them according to the payment rules.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A resource auction method for multi-base station, multi-slice scenarios in vehicle-to-everything (V2X) networks, characterized in that: The method includes the following steps: S1: Obtain the minimum rate and maximum latency parameters for different vehicle services; S2: The base station utility function is a weighted sum of the current base station's spectrum resource utilization efficiency and the satisfaction rate of different slice service level protocols. S3: Based on the base station utility function, establish a resource allocation model for multi-base station and multi-slice scenarios in the Internet of Vehicles with the goal of maximizing the system utility function; S4: The base station determines whether it can meet the vehicle service requirements; S5: Solve the established objective function using an auction mechanism, and output the optimal resource allocation strategy, specifically: The auction mechanism is defined as a tuple (X,Y), where X={x1,...,x...} m } is the set of binary decision functions for all base stations, x m =1 indicates that base station m wins this round of auction and can obtain the required resources, x m =0 indicates that base station m failed in this round of auction; Y = {Y1,...,Y} m } is the set of payment rules that a base station must pay as a reward to obtain spectrum resources; This represents the auction request for base station m, where Let m be the amount of resources requested by base station m. Let π be the bid of base station m; base stations are selfish, aiming to maximize their own utility; base station m increases its probability of winning by making higher spurious bids; spurious bids by base stations lead to reduced efficiency in resource allocation; π m =(w m ,σ m This indicates the actual request from BSM; In each round of auction, This indicates that the auctioneer has collected requests from all participants; This indicates all auction requests in this round of auctions except for base station m; This 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 among the participants, for each BS m Introduce a unit price, which is expressed as the ratio of the bid to the requested resource quantity; Based on critical value theory, a monotonic selection strategy is designed to determine the winner of the auction mechanism; simultaneously, the payment fee for each winner is determined by the payment rules, which are defined as follows: In the auction mechanism, this refers to the set of auction requests for base stations. A preference relation is introduced, defined as follows: like or get If base station m bids higher or requires less resources, then the auction request... Auction Request More advantageous; when base station m claims an auction request The fee that needs to be paid when selected as the winner is If base station m is the loser, the cost is 0; the threshold is the minimum bid that base station m must make to acquire resources. During the auction, the infrastructure provider will collect the set of auction requests from the current auction participation mechanism. like If not empty, then in the auction request set Find the unit price u m Largest auction request This determines the winner of the current round of the auction and generates a corresponding allocation decision based on the payment rules. After obtaining the initial winner decision, the infrastructure provider continues to update the payment set Y and the decision vector set X based on the unit price of the base station in order to finally determine the payment amount.

2. The resource auction method for multi-base station, multi-slice scenarios in vehicle-to-everything (V2X) networks according to claim 1, characterized in that: In S1, the minimum rate and maximum latency parameters for different vehicle services include: vehicle downlink data rate, minimum transmission rate that the vehicle service slice can provide, and maximum latency of the vehicle service slice.

3. The resource auction method for multi-base station, multi-slice scenarios in vehicle-to-everything (V2X) networks according to claim 2, characterized in that: In step S2, the base station utility function is a weighted sum of the current base station's spectrum resource utilization efficiency and the satisfaction rates of different slice service level protocols. 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 for different slices is defined as the ratio of successfully transmitted data packets to the total number of data packets sent by the slice. The utility function of a base station is defined as the weighted sum of the base station's spectrum resource utilization and the satisfaction rate of different slice service level protocols.

4. The resource management method for multi-base station, multi-slice scenarios in vehicle-to-everything (V2X) networks according to claim 3, characterized in that: Based on the base station utility function, a resource allocation model for a multi-base station, multi-slice scenario in vehicle-to-everything (V2X) network is established with the goal of maximizing the system utility function. The system utility maximization function is defined as: maximizing the sum of the utility functions of all base stations.

5. A resource auction method for a multi-base station, multi-slice scenario in vehicle-to-everything (V2X) networking according to claim 4, characterized in that: In step S4, the base station determines whether it can meet the vehicle service requirements: The base station dynamically adjusts its allocation strategy based on the resource requirements of vehicle services, which can be divided into two cases: (1) The base station's resources can meet the requirements of slicing; (2) The base station's resources cannot meet the requirements of slicing; Infrastructure providers update the remaining system resources according to the base station resource allocation strategy; infrastructure providers obtain profits by auctioning the remaining system resources, and base stations with insufficient resources provide services to vehicles by purchasing the remaining system resources, thus meeting the slicing requirements.

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