CN114554450B

CN114554450B - Unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching

Info

Publication number: CN114554450B
Application number: CN202210159383.1A
Authority: CN
Inventors: 秦鹏; 王淼; 和昊婷
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2022-02-21
Filing date: 2022-02-21
Publication date: 2024-04-02
Anticipated expiration: 2042-02-21
Also published as: CN114554450A

Abstract

The invention discloses an unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching. The scheme comprises the following steps: first, elements involved in the unmanned aerial vehicle assisted 6G network are abstracted into three types of entities, HAPs, UAVs and end users, wherein users need to connect to HAPs through UAVs. Then, we form a loop of the preference list of three types of entities in the unmanned aerial vehicle auxiliary 6G network resource, each agent builds its own preference list by ordering the preferences of the other agent, and constructs it into a trilateral matching problem with size and loop preference. Finally, we convert the three-edge matching problem into a limited three-edge matching problem with size and circulation preference by designing some reasonable limits, and solve the problem by using a three-edge matching method to obtain a stable three-edge matching result. The system improves the income of HAPs and solves the problem of resource allocation in high-efficiency transmission of mass data.

Description

Unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching

Technical Field

The invention relates to the field of future communication networks and Internet of things, in particular to an unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching.

Background

With the development of new applications such as internet of things, cloud computing and big data, research on sixth generation (6G) mobile communication technology is increasingly attracting attention from industry and academia. The air-ground integrated 6G network has the obvious advantages of large coverage, high throughput and strong recovery capability, and has become an important development field of wide area communication guarantee and information application for realizing remote area communication and information service which are difficult to cover by offshore, air and ground network systems. Currently, the number of users of ground terminals is continuously growing at a high speed, and thus how to effectively utilize limited resources to meet the increasing demands of users has become an increasing concern.

By overcoming the limitations of traditional resource allocation schemes and gambling theory, matched gambling has become a potential scheme in network resource allocation. The main advantage of matching is that it provides a distributed solution and considers the priority of each relevant entity. Classical matching schemes exist between two entities, however, the bilateral matching method cannot effectively solve the problem of resource allocation including three types of entities. In an air-ground integrated 6G network, three types of entities, HAPs, UAVs and end users, need to be considered simultaneously to realize efficient allocation of resources. The bilateral matching algorithm is often used for decoupling three types of entities, which causes the loss of preference information of various types of entities, thereby causing the loss of system efficiency.

There is a three-party matching scheme between three entities, and the relationship problem between the three entities (users, HAPs and UAVs) can be re-expressed as a three-party matching game with size and circular preference list (TMSC) between the three entities. The goal of TMSC is to find a stable match with the largest cardinality among the user, HAPs and UAVs. Since the process of determining whether a stable match exists in the TMSC model itself is NP-complete, it can be translated into a limited trilateral match problem (R-TMSC) with size and cycling preference by adding some reasonable constraints. According to the invention, through a trilateral matching scheme, three entities of HAPs, UAVs and end users are considered at the same time, network resources are distributed, qoS is ensured, and meanwhile, the income of the HAPs is effectively improved, and the overall performance of the system is improved.

Disclosure of Invention

In order to solve the problems, the invention discloses an unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching. The scheme comprises the following steps: first, elements involved in the unmanned aerial vehicle assisted 6G network are abstracted into three types of entities, HAPs, UAVs and end users, wherein users need to connect to HAPs through UAVs. Then, we form a loop from the preference list of three types of entities in the space-earth integrated 6G network resource, and each agent builds its own preference list by ordering the preference of another agent, and constructs it into a trilateral matching problem with size and loop preference. Finally, we have found a stable trilateral match by designing some reasonable limits, converting it into a limited trilateral match problem with size and cyclic preference. The system improves the income of HAPs and solves the problem of resource allocation in high-efficiency transmission of mass data.

The unmanned aerial vehicle auxiliary 6G network model is composed of an air-based network and a ground terminal user, wherein the air-based communication network comprises a low-altitude communication platform and a high-altitude communication platform. We abstract the different roles involved in the above network into three types of entities, HAPs, UAVs and end users, where users connect to HAPs through UAVs, considering the cooperation of HAPs and UAVs to enable large-scale access and data backhaul to users in remote areas. The high altitude section comprisesHAPs, cryptophan (L.) Diels>,/>. The low altitude section comprises->UAVs (personal UAVs)>,/>. The drone serves the area of interest in accordance with a given flight path. The ground terminal user includes->Personal (S)>,/>. Wherein the user connects to the HAPs through UAVs. UAVs fly along a given path, possibly disengaging or establishing a connection with the user.

By binary variablesRepresenting user +.>Whether or not to be in charge of UAV>Connection, binary variable->Representation of UAV->Whether or not to->The connection is specifically defined as follows:

and (3) with

The most important factor we use to measure system performance is the revenue that HAPs earn from users. Thus, the problem is expressed as follows:

P0:

C1：

C2:

C3：

C4：

C5：

C6：

c1 is UAVThe maximum number of users matching UAVs cannot exceed its capacity limit, i.e. +.>C2 represents +.>The number of the matched maximum HAPs cannot exceed 1, and C3 is HAP +.>The maximum UAVs number matching the HAPs cannot exceed its capacity limit, i.e.>C4 represents that the data rate between the user and the UAV is subject to the channel capacity between the user and the UAV>C5, C6 represent the binary variable +.>And->Only 0 or 1 can be taken.

From TMSC to R-TMSC, two limitations are added (1) the preference list of HAPs is derived from a master preference list. This master list is a strict sequential set of all users (e.g. according to the price offered), from which a preference list of all HAPs is derived, including all or part, (2) HAPs are independent of UAVs, i.e. for each UAVs the HAPs in its preference list form a tie. We call this model satisfying both (1) and (2) the R-TMSC model.

In view of the above limitations, an R-TMSC model was constructed for our scenario. First, we build a preference list for each HAP, user, and UAV. As previously mentioned, in the loop preference problem, the preference list of each type of entity contains only one type of other entity. So HAPs rank only users, users rank only UAVs, UAVs rank only HAPs.

The preference list of each HAPs for the user is derived from a master list, which is based on the user's offersThe users are arranged in descending order. Users requiring higher data rates will offer higher prices and are favored by HAPs. The preference lists for all content sources come from the master list, in our example, all HAPs create the same preference list (we assume that all UAVs can accept all users)

On the other hand, the user is based on the quality of service measured by the data transmission rateRanking acceptable UAVs (when building the preference list we assume interference +.>=0, because the matching operation of other users is not known to any user in advance). Thus, the user selects UAVs indirectly based on the desired data rate. We represent the user's preference list as

HAPs are independent of UAVs according to the R-TMSC model. In other words, the preference list of any UAVs contains a constraint, and the ranking of all HAPs is the same and can be expressed as

The basic idea of problem solving is to search for the "best" triplet. Starting with one empty set, the triplet is added to the matching set each time. Each "best" triplet (in orderIn the form of (c) is generated by first selecting HAPs that meet a particular requirement, then selecting the best user that meets its requirement, and finally selecting the most eligible UAVs.

The technical scheme of the invention has the following advantages:

the invention discloses an unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching. The scheme comprises the following steps: first, elements involved in the unmanned aerial vehicle assisted 6G network are abstracted into three types of entities, HAPs, UAVs and end users, wherein users need to connect to HAPs through UAVs. Then, we form a loop of the preference list of three types of entities in the unmanned aerial vehicle auxiliary 6G network resource, each agent builds its own preference list by ordering the preferences of the other agent, and constructs it into a trilateral matching problem with size and loop preference. Finally, we turn it into a limited trilateral matching problem with size and cycle preference by designing some reasonable limits, and finally find a stable trilateral matching result. The system improves the income of HAPs and solves the problem of resource allocation in high-efficiency transmission of mass data.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a graph of user quantity versus HAPs revenue.

Fig. 2a is a graph of the number of users versus the total throughput.

Fig. 2b is a graph of the number of users versus the average throughput.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.

The invention provides an unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching, and the embodiment is described in detail below with reference to the accompanying drawings.

The implementation mode of the invention is divided into two steps, wherein the first step is to build a system model, and the second step is to implement an algorithm.

FIG. 1 compares three-sided matching, two-sided matching, greedy, and random distribution methods from the perspective of the revenue of HAPs. The greater the number of users, the greater the HAPs revenue, the more the algorithms will tend to increase as the sum of prices offered by the HAPs for the matching users minus the sum of costs paid for the matching UAVs resources, as can be seen in fig. 1. Meanwhile, the benefits of our algorithm are better than the other three methods.

Fig. 2a and 2b compare the total and average throughput of a user with the user, respectively. We increase the number of users from 30 to 150, with a step size of 20. As shown in fig. 2a, under all four schemes, the network throughput increases as more users join the network. This is because the drones are reused between users sharing the same drone, which increases efficiency. On the other hand, fig. 2b shows that as more users match the available drones and HAPs, the average user throughput decreases. This is due to interference caused by users sharing the same resources. We can also observe from fig. 2a and 2b that our algorithm is superior to the other four allocation methods.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention referred to in this disclosure is not limited to the specific combination of features described above, but encompasses other embodiments in which any combination of features described above or their equivalents is contemplated without departing from the inventive concepts described. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Claims

1. The unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching is characterized by comprising the following steps of:

abstracting unmanned aerial vehicle auxiliary 6G network resource allocation into trilateral matching among HAPs, UAVs and users;

constructing a preference list for three types of entities in the unmanned aerial vehicle auxiliary 6G network resource; converting the three-edge matching problem into a limited three-edge matching problem with size and circulation preference, and solving the problem by using a three-edge matching method; the three types of entity construction preference lists comprise ranking users according to the user quotations, ranking UAVs according to the data transmission rates and ranking the HAPs by the UAVs;

the solving by using the three-edge matching method comprises the following steps: determining target HAPs meeting specific requirements, determining optimal users corresponding to the HAPs according to the target HAPs, and determining eligible UAVs according to the optimal users.

2. The method of claim 1, wherein abstracting the unmanned aerial vehicle assisted 6G network resource allocation to three-sided matching between HAPs, UAVs, and users comprises:

the elements related to the unmanned aerial vehicle auxiliary 6G network are abstracted into three types of entities of HAPs, UAVs and end users, wherein the users are connected to the HAPs through the UAVs to optimize the network.

3. The method of claim 2, wherein abstracting the unmanned aerial vehicle assisted 6G network resource allocation to three-sided matching between HAPs, UAVs, and users further comprises:

forming a cycle of preference lists of three types of entities in the unmanned aerial vehicle auxiliary 6G network resource, and establishing a self preference list by each agent through sequencing the preference of the other agent, so as to construct the three-edge matching problem with the size and the cycle preference;

through designing some reasonable limits, the three-edge matching method is converted into a limited three-edge matching problem with size and circulation preference, and finally a stable three-edge matching result is found; wherein the some reasonable limitations include: the preference list of the HAPs is derived from the master preference list corresponding to the strict sequence set of all users, and the ranking of all HAPs in the preference list corresponding to any one UAVs is the same.