Unmanned aerial vehicle group task scheduling and channel allocation method based on potential energy game

Abstract

The invention discloses an unmanned aerial vehicle group task scheduling and channel allocation method based on a potential energy game, and the method comprises the following steps: (1), building a data transmission scene of an unmanned aerial vehicle group to ground users in an unmanned aerial vehicle group auxiliary ground communication network; (2) modeling an unmanned aerial vehicle group task scheduling and channel allocation problem into a flight path optimization and channel access game model based on a potential energy game, a game participant being a set of unmanned aerial vehicles; (3) solving the flight path optimization and channel access game model by using an optimal response-based user scheduling and channel joint selection method, so that the total utility of all ground users served by each unmanned aerial vehicle is maximized, and unmanned aerial vehicle group task scheduling and channel allocation are completed; the task execution effectiveness of the unmanned aerial vehicle is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method for unmanned aerial vehicle (UAV) swarm task scheduling and channel allocation based on potential energy game. Background Art

[0002] The rapid development of ground wireless communication networks can provide good communication services for ground users and meet their daily communication needs. However, with the explosive growth of ground wireless devices, the ground communication network is showing a trend of intensive development. At the same time, ground wireless communications are usually affected by buildings, which makes the multipath fading of communication links more serious, making it difficult to provide users with reliable communication services. Drones have been widely used in drone-assisted ground communication networks due to their high dynamics, dynamic configuration and deployment. Drones can be used as aerial base stations to increase the possibility of line-of-sight transmission with ground users, enabling them to obtain better transmission performance.

[0003] At present, in the research of air-ground fusion network, existing work mainly aims to improve the whole network rate by jointly optimizing the position deployment, channel access, power control, etc. of drone swarm. However, in most studies, drone task scheduling only considers user services at specific locations, that is, once the drone position is optimized, it remains fixed and only serves specific users. In addition, in the research considering drone trajectory optimization, most studies assume that there are sufficient channels in the network and there is no mutual interference between users. However, with the increase of ground users and the dynamic movement of drones, the dynamic interference of links between users becomes very serious. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a method for UAV swarm task scheduling and channel allocation based on potential energy game, so as to improve the UAV-to-ground communication transmission performance in the air-ground fusion network.

[0005] Technical solution: The method for scheduling and allocating channels of drone swarms based on potential game in the present invention comprises the following steps: (1) In the UAV swarm-assisted ground communication network, establish the data transmission scenarios of the UAV swarm to the ground users, including line-of-sight transmission and non-line-of-sight transmission and their corresponding probabilities; (2) The UAV swarm task scheduling and channel allocation problem is modeled as a trajectory optimization and channel access game model based on potential energy game, where the game participants are a collection of UAVs; (3) The trajectory optimization and channel access game model is solved using the optimal response-based user scheduling and channel joint selection method to maximize the total utility of all ground users served by each drone and complete the drone swarm task scheduling and channel allocation.

[0006] Furthermore, step (1) is specifically as follows: Assume that the drone mission execution scenario includes N drones, each drone serves K ground users in sequence, and there are M channels in the network; for drone i, it transmits data directly above the user it serves; the channel model between the drone and the user is divided into line-of-sight transmission and non-line-of-sight transmission.

[0007] Furthermore, in step (1), the specific calculation method of the channel gain of the probability channel model is as follows: For line-of-sight transmission, the probability The calculation method is as follows: ; Among them, a and b are parameters describing the environment, is the elevation angle of the drone, which is related to the position of the drone and the position of the ground user; the elevation angle between drone i and ground user n , the specific calculation method is as follows: ; Among them, H is the flight altitude of the UAV, is the distance between UAV i and ground user n; the coordinates of UAV i are defined as , the coordinates of ground user n are , the specific calculation method is as follows: ; For non-line-of-sight transmission, the probability The calculation method is as follows: ; The probabilistic channel model defined by for: ; in, is the path loss exponential factor, is the additional attenuation coefficient due to non-line-of-sight transmission.

[0008] Furthermore, in step (2), the model formula is as follows: ; in, represents the set of drones, N is the number of drones; is the action strategy set of UAV i; is the utility function of UAV i.

[0009] Furthermore, in step (2), the utility function of drone i is , which is defined as follows: For any drone i, define is the channel selection of UAV i in K stages, For the scheduling selection of service users for UAV i in K stages, The sequential selection of drone i to serve user k; based on this, the utility function of drone i is defined as follows: ; Where k represents the ground user number served by UAV i, represents the transmission power of drone i, represents the transmission power of UAV j, represents the channel gain between UAV j and the kth ground user served by UAV i, and They represent the channel selection of UAV i and UAV j respectively.

[0010] Furthermore, step (3) includes the following steps: (31) Initialization, the initial number of iterations is set to , each drone in the drone set randomly selects the channel and user service order, and defines the initial strategy as ,in, , represents the channel selection strategy vector of UAV i at iteration 0, , represents the user service order vector of drone i at iteration 0; (32) Fixed the channel selection for all current drones , the optimal response algorithm is used to optimize the service user scheduling selection of the drone group, and the optimization result is recorded as ; (33) Fixed the optimized service user scheduling selection for all current drones , the optimal response algorithm is used to optimize the channel selection of the drone group, and the optimization result is recorded as ; (34) Repeat steps (32) to (33) until the selection method converges; output the utility of each current UAV and the joint channel and user service scheduling selection results to complete the UAV group task scheduling and channel allocation.

[0011] Furthermore, in step (32), the optimal response algorithm is used to optimize the service user scheduling selection of the drone group, and the specific update criteria are as follows: Randomly select a drone i, and select the service user scheduling strategy corresponding to the maximum utility as the service user scheduling for the next iteration according to the following formula. The remaining drones repeat the previous service user scheduling: ; in, It is The service user scheduling selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

[0012] Furthermore, in step (33), the channel selection of the drone swarm is optimized using the optimal response algorithm, and the specific update criteria are as follows: Randomly select a drone i, and select the channel resource corresponding to the maximum utility as the channel selection for the next iteration according to the following formula. The remaining drones repeat the previous channel selection: ; in, It is The channel selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

[0013] An electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, any one of the methods for unmanned aerial vehicle swarm task scheduling and channel allocation based on potential energy game is implemented.

[0014] A storage medium described in the present invention stores a computer program, and when the computer program is executed by a processor, it implements any one of the methods for unmanned aerial vehicle swarm task scheduling and channel allocation based on potential energy game.

[0015] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: by using non-cooperative games, each drone has the ability to make independent decisions and make the most beneficial decisions for itself in a distributed manner, which effectively reduces the computational complexity of the network; at the same time, the proposed joint task scheduling and channel allocation framework can effectively avoid dynamic interference between drones and equipment, and effectively improve the mission execution efficiency of drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the data transmission scenario from a swarm of drones to ground users according to the present invention.

[0017] Figure 2 This is a flow chart of the user scheduling and channel joint selection method based on optimal response of the present invention.

[0018] Figure 3 This is a simulation diagram of the user scheduling and channel joint selection method based on optimal response of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0020] Example 1: Figure 1 As shown, an embodiment of the present invention provides a method for unmanned aerial vehicle group task scheduling and channel allocation based on potential energy game, comprising the following steps: Step 1: In the drone swarm assisted ground communication network, a scenario of data transmission from the drone swarm to the ground user is established. The probabilistic channel model is considered in the scenario, including line-of-sight transmission and non-line-of-sight transmission and their corresponding probabilities, as follows: Consider a drone mission execution scenario, including N drones, each drone serves K ground users in sequence, and there are M channels in the network;

[0021] For line-of-sight transmission, the probability The calculation method is as follows: ; Among them, a and b are parameters describing the environment, is the elevation angle of the drone, which is related to the position of the drone and the position of the ground user; the elevation angle between drone i and ground user n , the specific calculation method is as follows: ; Among them, H is the flight altitude of the UAV, is the distance between UAV i and ground user n; the coordinates of UAV i are defined as , the coordinates of ground user n are , the specific calculation method is as follows: ; For non-line-of-sight transmission, the probability The calculation method is as follows: ; The probabilistic channel model defined by for: ; in, is the path loss exponential factor, is the additional attenuation coefficient due to non-line-of-sight transmission.

[0022] Step 2: Model the UAV swarm task scheduling and channel allocation problem as a trajectory optimization and channel access game model based on potential game, where the game participants are the UAV collection, as follows: The cooperative offloading problem is modeled as a channel access game model based on task offloading. The game model is defined as: ; in, represents the set of drones, N is the number of drones; is the action strategy set of drone i is the utility function of drone i. For any drone i, define is the channel selection of UAV i in K stages, For the scheduling selection of service users for UAV i in K stages, The sequential selection of drone i to serve user k; based on this, the utility function of drone i is defined as follows: ; Where k represents the ground user number served by UAV i, represents the transmission power of UAV i, represents the transmission power of UAV j, represents the channel gain between UAV j and the kth ground user served by UAV i, and They represent the channel selection of UAV i and UAV j respectively.

[0023] Step 3: Propose a user scheduling and channel joint selection method based on optimal response to solve the game, so as to maximize the total utility of all ground users served by each drone. The user scheduling and channel joint selection method based on optimal response is as follows: (31) Initialization, the initial number of iterations is set to , each drone in the drone set randomly selects the channel and user service order, and defines the initial strategy as ,in, , represents the channel selection strategy vector of UAV i at iteration 0, , represents the user service order vector of drone i at iteration 0; (32) Fixed the channel selection for all current drones , the optimal response algorithm is used to optimize the service user scheduling selection of the drone group, and the optimization result is recorded as ; The specific update criteria are as follows: Randomly select a drone i, and select the service user scheduling strategy corresponding to the maximum utility as the service user scheduling for the next iteration according to the following formula. The remaining drones repeat the previous service user scheduling: ; in, It is The service user scheduling selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

[0024] (33) Fixed the optimized service user scheduling selection for all current drones , the optimal response algorithm is used to optimize the channel selection of the drone group, and the optimization result is recorded as ; The specific update criteria are as follows: Randomly select a drone i, and select the channel resource corresponding to the maximum utility as the channel selection for the next iteration according to the following formula. The remaining drones repeat the previous channel selection: ; in, It is The channel selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

[0025] (34) Repeat steps (32) to (33) until the selection method converges; output the utility of each current UAV and the joint channel and user service scheduling selection results to complete the UAV group task scheduling and channel allocation.

[0026] Embodiment 2: The present invention provides a method for scheduling and allocating channels of a drone swarm based on potential energy game, comprising the following steps: S1: Consider a scenario where a swarm of drones transmits data to ground users, such as Figure 1 As shown, there are N drones, each drone serves K ground users in sequence, and 2 channels. Each drone serves 3 ground users in a certain order.

[0027] For line-of-sight transmission, the probability The calculation method is as follows: ; Among them, a and b are parameters describing the environment. and . is the elevation angle of the drone, which is related to the position of the drone and the position of the ground user; the elevation angle between drone i and ground user n , the specific calculation method is as follows: ; Among them, H is the flight altitude of the UAV, , is the distance between UAV i and ground user n; the coordinates of UAV i are defined as , the coordinates of ground user n are , the specific calculation method is as follows: ; For non-line-of-sight transmission, the probability The calculation method is as follows: ; The probabilistic channel model defined by for: ; in, is the path loss exponential factor, , is the additional attenuation coefficient due to non-line-of-sight transmission, .

[0028] S2: The UAV swarm task scheduling and channel allocation problem is modeled as a trajectory optimization and channel access game model based on potential energy game, where the game participants are the UAV collection, as follows: The cooperative offloading problem is modeled as a channel access game model based on task offloading. The game model is defined as: ; in, represents the set of drones, N is the number of drones; is the action strategy set of drone i is the utility function of drone i. For any drone i, define is the channel selection of UAV i in K stages, For the scheduling selection of service users for UAV i in K stages, The sequential selection of drone i to serve user k; based on this, the utility function of drone i is defined as follows: ; Where k represents the ground user number served by UAV i, represents the transmission power of drone i, represents the transmission power of UAV j, and . represents the channel gain between UAV j and the kth ground user served by UAV i, and They represent the channel selection of UAV i and UAV j respectively.

[0029] S3: A user scheduling and channel joint selection method based on optimal response is proposed to solve the game, so that the total utility of all ground users served by each drone is maximized. Among them, the user scheduling and channel joint selection method based on optimal response is as follows: Figure 2 As shown, the details are as follows: (S31) Initialization, the initial number of iterations is set to , each drone in the drone set randomly selects the channel and user service order, and defines the initial strategy as ,in, , represents the channel selection strategy vector of UAV i at iteration 0, , represents the user service order vector of drone i at iteration 0; (S32) Fixed the channel selection of all current drones , the optimal response algorithm is used to optimize the service user scheduling selection of the drone group, and the optimization result is recorded as ; The specific update criteria are as follows: Randomly select a drone i, and select the service user scheduling strategy corresponding to the maximum utility as the service user scheduling for the next iteration according to the following formula. The remaining drones repeat the previous service user scheduling: ; in, It is The service user scheduling selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

[0030] (S33) Fix the optimized service user scheduling selection for all current drones , the optimal response algorithm is used to optimize the channel selection of the drone group, and the optimization result is recorded as ; The specific update criteria are as follows: Randomly select a drone i, and select the channel resource corresponding to the maximum utility as the channel selection for the next iteration according to the following formula. The remaining drones repeat the previous channel selection: ; in, It is The channel selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

[0031] (S34) Repeat steps (S32) to (S33) until the selection method converges; output the utility of each current drone and the joint channel and user service scheduling selection result to complete the drone group task scheduling and channel allocation.

[0032] Consider a drone mission execution scenario where the number of drones changes from 2 to 6, the number of ground users changes from 6 to 18, and the number of channels in the fixed network is 2. Figure 3 The results of the experiment run.

[0033] like Figure 3 As shown, this is an experimental simulation diagram of the relationship between the utility of the entire network task and the number of drones (number of users) under the application of the proposed user scheduling and channel joint selection method based on optimal response and the user scheduling optimization algorithm, channel selection optimization algorithm and random selection algorithm. It can be seen that the global utility of the method designed by the present invention is significantly higher than that of the other three methods, and as the number of drones (number of users) increases, the utility gradually decreases, and the performance gap with random selection becomes increasingly larger.

[0034] Embodiment 3: The embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, it implements any one of the methods for drone swarm task scheduling and channel allocation based on potential energy game.

[0035] Embodiment 4: The embodiment of the present invention further provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, it implements any one of the methods for drone swarm task scheduling and channel allocation based on potential energy game.

Claims

1. A method for task scheduling and channel allocation of drone swarms based on potential energy game, characterized in that: The following steps are involved: (1) In the UAV swarm-assisted ground communication network, establish the data transmission scenarios of the UAV swarm to the ground users, including line-of-sight transmission and non-line-of-sight transmission and their corresponding probabilities; (2) The UAV swarm task scheduling and channel allocation problem is modeled as a trajectory optimization and channel access game model based on potential energy game, where the game participants are a collection of UAVs; (3) The trajectory optimization and channel access game model is solved using the optimal response-based user scheduling and channel joint selection method to maximize the total utility of all ground users served by each drone and complete the drone swarm task scheduling and channel allocation.

2. The method for UAV swarm task scheduling and channel allocation based on potential game according to claim 1 is characterized in that: Step (1) is as follows: Assume that the UAV mission execution scenario includes N UAVs, each of which serves K ground users in sequence, and there are M channels in the network; for UAV i, it transmits data directly above the user it serves; the channel model between the UAV and the user is divided into line-of-sight transmission and non-line-of-sight transmission.

3. The method for UAV swarm task scheduling and channel allocation based on potential game according to claim 2 is characterized in that: In step (1), the specific calculation method of the channel gain of the probabilistic channel model is as follows: For line-of-sight transmission, the probability The calculation method is as follows: ； Among them, a and b are parameters describing the environment, is the elevation angle of the drone, which is related to the position of the drone and the position of the ground user; the elevation angle between drone i and ground user n , the specific calculation method is as follows: ； Among them, H is the flight altitude of the UAV, is the distance between UAV i and ground user n; the coordinates of UAV i are defined as , the coordinates of ground user n are , the specific calculation method is as follows: ； For non-line-of-sight transmission, the probability The calculation method is as follows: ； The probabilistic channel model defined by for: ； in, is the path loss exponential factor, is the additional attenuation coefficient due to non-line-of-sight transmission.

4. The method for UAV swarm task scheduling and channel allocation based on potential energy game according to claim 1 is characterized in that: In step (2), the model formula is as follows: ； in, represents the set of drones, N is the number of drones; is the action strategy set of drone i is the utility function of UAV i.

5. The method for UAV swarm task scheduling and channel allocation based on potential energy game according to claim 4 is characterized in that: In step (2), the utility function of drone i is , specifically defined as follows: For any drone i, define is the channel selection of UAV i in K stages, For the scheduling selection of service users for UAV i in K stages, The sequential selection of drone i to serve user k; based on this, the utility function of drone i is defined as follows: ； Where k represents the ground user number served by UAV i, represents the transmission power of drone i, represents the transmission power of UAV j, represents the channel gain between UAV j and the kth ground user served by UAV i, and They represent the channel selection of UAV i and UAV j respectively.

6. The method for UAV swarm task scheduling and channel allocation based on potential energy game according to claim 1 is characterized in that: Step (3) includes the following steps: (31) Initialization, the initial number of iterations is set to , each drone in the drone set randomly selects the channel and user service order, and defines the initial strategy as ,in, , represents the channel selection strategy vector of UAV i at iteration 0, , represents the user service order vector of drone i at iteration 0; (32) Fixed the channel selection for all current drones , the optimal response algorithm is used to optimize the service user scheduling selection of the drone group, and the optimization result is recorded as ; (33) Fixed the optimized service user scheduling selection for all current drones , the optimal response algorithm is used to optimize the channel selection of the drone group, and the optimization result is recorded as ; (34) Repeat steps (32) to (33) until the selection method converges; output the utility of each current UAV and the joint channel and user service scheduling selection results to complete the UAV group task scheduling and channel allocation.

7. The method for UAV swarm task scheduling and channel allocation based on potential game according to claim 6 is characterized in that: In step (32), the optimal response algorithm is used to optimize the service user scheduling selection of the drone group. The specific update criteria are as follows: Randomly select a drone i, and select the service user scheduling strategy corresponding to the maximum utility as the service user scheduling for the next iteration according to the following formula. The remaining drones repeat the previous service user scheduling: ； in, It is The service user scheduling selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

8. The method for UAV swarm task scheduling and channel allocation based on potential energy game according to claim 6 is characterized in that: In step (33), the optimal response algorithm is used to optimize the channel selection of the drone group. The specific update criteria are as follows: Randomly select a drone i, and select the channel resource corresponding to the maximum utility as the channel selection for the next iteration according to the following formula. The remaining drones repeat the previous channel selection: ； in, It is The channel selection of drone i in the iteration, is the utility function of drone i in the qth iteration.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into the processor, a method for unmanned aerial vehicle swarm task scheduling and channel allocation based on potential energy game according to any one of claims 1 to 8 is implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, a method for unmanned aerial vehicle swarm task scheduling and channel allocation based on potential energy game according to any one of claims 1 to 8 is implemented.

Images