Multi-user multi-unmanned aerial vehicle auxiliary communication method based on time division multiple access

Abstract

The invention provides a time division multiple access-based multi-user multi-unmanned aerial vehicle auxiliary communication method, which comprises the following steps that: for a scene comprising M users, K unmanned aerial vehicles and a base station, the users send data to be transmitted to the unmanned aerial vehicles, and the unmanned aerial vehicles forward the received data to the base station; by adopting a Stackelberg game mechanism and a time division multiple access technology, the unmanned aerial vehicle and the user determine an optimal unit resource pricing, and the user determines an optimal time allocation coefficient resource purchased by the user according to the optimal unit resource; the unmanned aerial vehicle and the base station decide an optimal unit resource pricing, and the unmanned aerial vehicle decides an optimal time allocation coefficient resource purchased by the unmanned aerial vehicle according to the optimal unit resource; the user transmits data to the unmanned aerial vehicle in the sub-cycle of the user according to the purchased optimal time allocation coefficient resource, and the unmanned aerial vehicle transmits the data to the base station in the sub-cycle of the unmanned aerial vehicle according to the purchased optimal time allocation coefficient resource.

Description

Technical Field

[0001] The present invention belongs to the technical field of drone-assisted communication in wireless communications, and specifically relates to a multi-user multi-drone-assisted communication method based on time division multiple access. Background Art

[0002] Drone-assisted communication is an innovative communication technology that uses drones as aerial platforms and carries communication equipment to achieve signal relay, enhancement and data transmission. This technology is particularly suitable for scenes with complex terrain, disaster sites or remote areas where traditional communication facilities are difficult to cover or have been damaged. Drones can be deployed quickly to establish temporary communication networks, providing timely and effective communication support for rescue, exploration, etc.

[0003] Drone-assisted communication mainly uses drones to carry communication equipment to establish temporary communication network nodes in the air, receive signals from the ground, amplify and process them, and then transmit them again, thereby extending the communication distance and improving the communication quality. In addition, drones can also be used as a data transmission platform to achieve high-speed data transmission and provide timely and effective data support for various fields.

[0004] However, in disaster scenarios, one of the commonly used methods is to hover multiple drones in the air to provide auxiliary communications for multiple users, and its complex resource management is a key issue that needs to be solved. Summary of the invention

[0005] Purpose of the invention: In order to solve the problem of resource allocation for assisting communication for multiple users by hovering multiple drones in the air when surrounding base stations are unable to work, the present invention proposes a multi-user multi-drone assisted communication method based on time division multiple access.

[0006] Technical solution: A multi-user multi-UAV assisted communication method based on time division multiple access, comprising the following steps: Step 1: Set the scenario as: M users, K drones and a base station. The user sends the data to be transmitted to the drone, and the drone forwards the received data to the base station; Obtaining coordinate information of each user in the scene, coordinate information of each drone, hovering height information of each drone, and coordinate information of the base station; determining a channel gain from the user to the drone according to a distance from each user to each drone, and determining a channel gain from the drone to the base station according to a distance from each drone to the base station; Step 2: Use time division multiple access technology to divide the transmission period T from each user to the drone into M sub-periods, assign a time allocation coefficient to each user, and only allow one user to send data to the drone in the same sub-period; The Stackelberg game mechanism is adopted, with K drones as the leaders of the two parties in the game, and M users with time allocation coefficients as the buyers of the two parties in the game. The leaders and buyers decide the optimal unit resource pricing, the leaders sell resources, and the users decide the optimal time allocation coefficient resources they purchase and the access binary indicator variables of the users accessing the drones according to the optimal unit resource pricing. Step 3: Determine the number of drones actually used based on the binary indicator variable of users accessing drones obtained in step 2; Step 4: Use time division multiple access technology to assign a time allocation coefficient to each drone actually used; The Stackelberg game mechanism is adopted, with the base station as the leader of the two parties in the game and the drone actually used as the buyer. The leader and the buyer decide the optimal unit resource pricing, and the drone determines the optimal time allocation coefficient resource it purchases based on the optimal unit resource pricing. Step 5: The user transmits data to the drone in its own sub-period according to the purchased optimal time allocation coefficient resources, and the drone transmits data to the base station in its own sub-period according to the purchased optimal time allocation coefficient resources.

[0007] Furthermore, the Stackelberg game mechanism is adopted, K drones are used as the leaders of the two parties in the game, and M users with time allocation coefficients are used as the buyers of the two parties in the game. The leaders and buyers decide on the optimal unit resource pricing. The specific operations include: The objective function of user m is expressed as: ; in, is the binary indicator variable for user m’s access to drone k. This indicates that user m is connected to drone k, otherwise it is not connected; is the transmission power value of user m; is the receiver noise of the UAV; Pricing the time allocation coefficient per unit resource for leaders; represents the time allocation coefficient resource purchased by user m; , ; The bandwidth for the communication between the user and the drone; represents the channel gain from user m to drone k; The constraints of the objective function of user m are expressed as: ; ; The objective function for K drones is expressed as: ; st ; By maximizing the objective function of user m and the objective function of K drones, the optimal unit resource pricing is obtained .

[0008] Furthermore, in step 3, the number of drones actually used is determined according to the binary indicator variable of the user accessing the drone obtained in step 2, including: The binary indicator variable for user access to drones is expressed as: ; like , indicating that the user does not access drone k; like , indicating that one or more users access drone k; This will determine the actual number of drones used.

[0009] Furthermore, in step 4, the time division multiple access technology is used to allocate a time allocation coefficient to each actually used drone, specifically including: The time period for each drone to reach the base station Divide sub-periods, where ; In the same sub-period, only one drone is allowed to send data to the base station; The time allocation coefficient of UAV k is expressed as .

[0010] Furthermore, in step 4, the Stackelberg game mechanism is adopted, with the base station as the leader of the two parties in the game, and the drone actually used as the buyer. The leader and the buyer decide on the optimal unit resource pricing. The specific operations include: The objective function of UAV k is expressed as: ; st ; ; In the formula, is the transmission power value of UAV k; is the receiver noise of the base station; Pricing the unit resources of base stations; is the bandwidth of the communication phase between the drone and the base station, The time allocation coefficient resource purchased for drone k; represents the channel gain from UAV k to the base station; The objective function of the base station is expressed as: ; st ; By maximizing the objective function of the base station and the objective function of each drone, the optimal unit resource pricing is obtained.

[0011] Beneficial effects: Compared with the prior art, the present invention has the following advantages: (1) The method of the present invention assists multiple users in communication by hovering multiple drones, which physically conforms to the actual application scenario and can be effectively applied to engineering practice; (2) The method of the present invention focuses on the network scenario of multiple drones, multiple users and a single base station, and rationally allocates resources for auxiliary communications of multiple users and multiple drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of a multi-user multi-UAV assisted communication method based on time division multiple access proposed by the present invention. DETAILED DESCRIPTION

[0013] The technical solution of the present invention is now further described in conjunction with the accompanying drawings and embodiments.

[0014] like Figure 1 As shown, this embodiment proposes a multi-user multi-UAV assisted communication method based on time division multiple access, and the specific operations include: Assume that there are M users in the network, defined as ,Due to factors such as natural disasters, the surrounding base stations cannot work, so multiple fixed-wing drones hovering in the air are deployed in the network, defined as , users use drones as data relays to transmit data to distant base stations, thereby ensuring communications for multiple users in disaster-stricken areas.

[0015] The horizontal coordinate of user m is defined as The horizontal coordinate of UAV k is defined as , the hovering height of UAV k is The horizontal coordinates of the base station are defined as .make represents the channel gain at a distance of 1 meter, so the channel gain from user m to drone k can be expressed as: ; The channel gain from drone k to the base station can be expressed as: ; Considering that in this embodiment, the user first sends data to the drone, and then the drone forwards the received data to the distant base station, the multi-user multi-drone assisted communication is naturally divided into two communication stages. Assume that the total system bandwidth is In order to reduce the mutual interference between the two communication stages, the frequency division multiple access method is adopted to allocate bandwidth for the two stages of communication. and .

[0016] (1) First, a resource management method for multi-user to multi-UAV data transmission is proposed, including: Since users need to send data to the drone, in order to avoid interference between users during data transmission, the more common time division multiple access (TDMA) technology is used in this embodiment, that is, each user-to-drone transmission period T is divided into M sub-periods, and the network only allows one user to send data to the drone in the same sub-period. Represents the time allocation coefficient of user m using TDMA.

[0017] Considering that multiple users are scattered in the disaster area, which is a common rescue network scenario, the traditional network architecture that uses one drone to send data cannot serve all users well, especially for users who are far away from the drone. Therefore, in this embodiment, multiple drones forward data for users in the disaster area, that is, users can choose the drone with the best performance to send data.

[0018] Considering that each user wants to send data to the drone for data forwarding, but due to the time allocation limitation of TDMA technology, the drone cannot receive data sent from users without limit. Therefore, in the resource management scheme of the multi-user to multi-drone data transmission stage, users with different TDMA time allocation coefficients compete with each other in the issue of purchasing data transmission resources for drones. Steinberg game is a pure strategy non-cooperative sequential game model in economics. According to the priority of action and the completeness of information, the participants of the game can be divided into leaders and followers. Followers only have partial information and take the lead; while leaders act later because they have all the information of followers. Among them, the leader needs to consider the optimal response of followers when setting the game strategy, and followers give their own optimal resource purchase size according to the optimal decision of the leader. In the stage of multi-user data transmission to multi-UAVs in this embodiment, due to the use of TDMA technology, only one user sends data to one of the multi-UAVs in the same sub-period. Therefore, a group of multi-UAVs is used as the leader of the game model, and users who only have partial information act as followers.

[0019] The objective function of user m consists of two parts, namely the difference between the rate benefit obtained and the price paid for purchasing the time allocation coefficient resources, expressed as: ; in, is a binary indicator variable for users accessing drones. This indicates that user m is connected to drone k, otherwise it is not connected; The transmission power value of user m is a known value; is the receiver noise of the UAV; The unit resource pricing of the TDMA time allocation coefficient for the leader; The data transmission rate that can be obtained by user m using TDMA technology; Purchase time allocation coefficient resources for user m And the price to pay.

[0020] Since a user can only select the drone with the best performance to forward data, the constraints are: .

[0021] As the 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. Therefore, for the resource game of multi-user to multi-UAV data transmission stage, the followers (user m) have: ; ; st ; The seller leader in the resource game relationship in the stage of transmitting data from multiple users to multiple drones, that is, the drone cluster, sells limited time allocation coefficient resources to multiple competing users for forwarding their data, so its objective function is defined as maximizing the total cost paid by all users to purchase time allocation coefficient resources. Therefore, the leader (drone cluster) of the resource game in the stage of transmitting data from multiple users to multiple drones is: ; st ; The optimization problems of users and drone clusters together constitute the Stackelberg game for the resources in the data transmission stage from multiple users to multiple drones. The final Stackelberg equilibrium can be obtained by the game leaders (drone clusters) and followers (users) playing games according to certain rules. That is, the optimal unit resource pricing is obtained by maximizing the utility function representing the interests of drones and users with a mutually competitive relationship. Then, followers decide the optimal purchase time allocation coefficient resource based on the leader’s optimal resource pricing. and the binary indicator variable of user access to drones .

[0022] (2) Then, the resource management method for the stage of multiple drones transmitting data to the base station is given: According to the binary indicator variable , the usage of the drone can be obtained.

[0023] like , indicating that no user sends data to drone k, and drone k has no data forwarded to the base station during the stage of multiple drones transmitting data to the base station. Therefore, drone k does not participate in the resource management method of the stage of multiple drones transmitting data to the base station, and is deleted from the drone cluster.

[0024] like , it means that one or more users send data to UAV k, and UAV k has data forwarded to the base station during the stage of multiple UAVs transmitting data to the base station. Therefore, UAV k participates in the resource management method during the stage of multiple UAVs transmitting data to the base station.

[0025] Considering that each drone wants to send the data sent by the user in the first stage to the base station in the second stage, in order to avoid interference between multiple drones during data transmission, the more common TDMA technology is still used, that is, the time period for each drone to reach the base station is set to Divide sub-periods, where ; In the same sub-period, only one drone is allowed to send data to the base station; It is represented as the time allocation coefficient when UAV k uses TDMA to send data to the base station.

[0026] Similarly, due to the time allocation limitations of TDMA technology, the base station cannot receive unlimited data transmission from users. Therefore, in the resource management solution for the multi-UAV data transmission stage given by the present invention, UAVs with different TDMA time allocation coefficients compete with each other in purchasing data transmission resources of the base station, with the base station as the leader of the game model, and each UAV that only has partial information acts as followers.

[0027] At the follower (drone k), the objective function of the optimization problem also consists of two parts, which are the difference between the rate benefit obtained and the cost of purchasing the time allocation coefficient resources, expressed as: ; in, is the transmission power value of UAV k, which is a known value; is the receiver noise of the base station; Pricing the unit resource of the TDMA time allocation coefficient for the base station; The data transmission rate achievable using TDMA technology for UAV k; Purchase time allocation coefficient resources for drone k And the price to pay.

[0028] Since UAV k needs to forward all the data of all users obtained in the stage of multi-user to multi-UAV data transmission during the stage of multi-UAV to base station data transmission, the constraint is: ; in, is the amount of data that UAV k can transmit during the multi-UAV to base station data transmission stage, is the amount of data of all users obtained by UAV k during the multi-user to multi-UAV data transmission stage.

[0029] As the number of resources purchased by followers increases, their benefits also increase, but their cost function also increases. 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. Therefore, for the resource game of multiple drones transmitting data to the base station, the followers (drone k) are: ; st ; ; In this game, the seller leader, i.e., the base station, sells limited time allocation coefficient resources to multiple competing drones for their data transmission, so its objective function is defined as maximizing the total cost paid by all participating drones to purchase time allocation coefficient resources. Therefore, the leader (base station) of the resource game for the stage of multiple drones transmitting data to the base station is: ; st ; The optimization problems of drones and base stations together constitute a Stackelberg game for the resources in the data transmission stage of multiple drones to base stations. The final Stackelberg equilibrium can be obtained by the game leaders (base stations) and followers (drones) 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 stations and the drones with a mutually competitive relationship. Then, followers decide the optimal purchase time allocation coefficient resource based on the leader’s optimal resource pricing. .

Claims

1. A multi-user multi-UAV assisted communication method based on time division multiple access, characterized in that: The following steps are involved: Step 1: Set the scenario as: M users, K drones and a base station. The user sends the data to be transmitted to the drone, and the drone forwards the received data to the base station; Acquire coordinate information of each user in the scene, coordinate information of each drone, hovering height information of each drone, and coordinate information of a base station; According to the distance from each user to each drone, the channel gain from the user to the drone is determined, and according to the distance from each drone to the base station, the channel gain from the drone to the base station is determined; Step 2: Use time division multiple access technology to divide the transmission period T from each user to the drone into M sub-periods, assign a time allocation coefficient to each user, and only allow one user to send data to the drone in the same sub-period; The Stackelberg game mechanism is adopted, with K drones as the leaders of the two parties in the game, and M users with time allocation coefficients as the buyers of the two parties in the game. The leaders and buyers decide the optimal unit resource pricing, the leaders sell resources, and the users decide the optimal time allocation coefficient resources they purchase and the access binary indicator variables of the users accessing the drones according to the optimal unit resource pricing. Step 3: Determine the number of drones actually used based on the binary indicator variable of users accessing drones obtained in step 2; Step 4: Use time division multiple access technology to assign a time allocation coefficient to each drone actually used; The Stackelberg game mechanism is adopted, with the base station as the leader of the two parties in the game and the drone actually used as the buyer. The leader and the buyer decide the optimal unit resource pricing, and the drone determines the optimal time allocation coefficient resource it purchases based on the optimal unit resource pricing. Step 5: The user transmits data to the drone in its own sub-period according to the purchased optimal time allocation coefficient resources, and the drone transmits data to the base station in its own sub-period according to the purchased optimal time allocation coefficient resources.

2. According to the time division multiple access based multi-user multi-UAV assisted communication method of claim 1, it is characterized by: The Stackelberg game mechanism is adopted, K drones are used as the leaders of the two parties in the game, and M users with time allocation coefficients are used as the buyers of the two parties in the game. The leaders and buyers decide on the optimal unit resource pricing. The specific operations include: The objective function of user m is expressed as: ； in, is the binary indicator variable for user m’s access to drone k. This indicates that user m is connected to drone k, otherwise it is not connected; is the transmission power value of user m; is the receiver noise of the UAV; Pricing the time allocation coefficient per unit resource for leaders; represents the time allocation coefficient resource purchased by user m; , ; The bandwidth for the communication between the user and the drone; represents the channel gain from user m to drone k; The constraints of the objective function of user m are expressed as: ； ； The objective function for K drones is expressed as: ； s.t. ; By maximizing the objective function of user m and the objective function of K drones, the optimal unit resource pricing is obtained .

3. The multi-user multi-UAV assisted communication method based on time division multiple access according to claim 2, characterized in that: In step 3, the method of determining the number of drones actually used based on the binary indicator variable of the user accessing the drone obtained in step 2 includes: The binary indicator variable for user access to drones is expressed as: ; like , indicating that the user does not access drone k; like , indicating that one or more users access drone k; This will determine the actual number of drones used.

4. The multi-user multi-UAV assisted communication method based on time division multiple access according to claim 3 is characterized in that: In step 4, the time division multiple access technology is used to allocate a time allocation coefficient to each actually used drone, specifically including: The time period for each drone to reach the base station Divide sub-periods, where ; In the same sub-period, only one drone is allowed to send data to the base station; The time allocation coefficient of UAV k is expressed as .

5. The multi-user multi-UAV assisted communication method based on time division multiple access according to claim 4, characterized in that: In step 4, the Stackelberg game mechanism is adopted, with the base station as the leader of the two parties in the game and the drone actually used as the buyer. The leader and the buyer decide on the optimal unit resource pricing. The specific operations include: The objective function of UAV k is expressed as: ； s.t. ; ； In the formula, is the transmission power value of UAV k; is the receiver noise of the base station; Pricing the unit resources of base stations; is the bandwidth of the communication phase between the drone and the base station, The time allocation coefficient resource purchased for drone k; represents the channel gain from UAV k to the base station; The objective function of the base station is expressed as: ； s.t. ; By maximizing the objective function of the base station and the objective function of each drone, the optimal unit resource pricing is obtained.

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