A method for unmanned aerial vehicle self-organizing multi-hop network data transmission in disaster areas

Abstract

The application discloses a kind of unmanned aerial vehicle self-organizing multi-hop network data transmission methods for disaster area, comprising: step one, according to the number of disaster area determines the number of unmanned aerial vehicle array and the number of unmanned aerial vehicle in each array;Step two, determine the movement range of each unmanned aerial vehicle array group and the location of remote ground base station;Step three, establish objective function, through objective function and improved multi-objective grey wolf algorithm obtain the best position of each unmanned aerial vehicle array, ideal excitation current weight and best communication order;Step four, each unmanned aerial vehicle array group moves to the best position, and adjusts excitation current to the best weight, the unmanned aerial vehicle array carries out information transmission according to the best communication order obtained.The best position of each unmanned aerial vehicle array, ideal excitation current weight and best communication order are obtained through objective function and improved multi-objective grey wolf algorithm, improve the transmission efficiency of unmanned aerial vehicle array, meet the requirements of disaster area rescue.

Description

Technical Field

[0001] This invention relates to a method for data transmission in a self-organizing multi-hop network of unmanned aerial vehicles (UAVs) in disaster areas, belonging to the field of wireless communication. Background Technology

[0002] In the field of wireless communication, drones can be deployed as aerial base stations to enhance or replace terrestrial cellular networks, providing reliable wireless network communication services to ground users. Therefore, during disaster relief efforts, when ground network infrastructure in multiple disaster areas malfunctions, drones can act as aerial base stations to provide wireless network services to trapped individuals, collect ground information, and transmit that information to rescue personnel.

[0003] However, due to limitations in UAV transmission power and onboard energy, their transmission performance cannot meet the requirements of disaster areas. Therefore, this invention proposes a cooperative beamforming method based on a UAV virtual antenna array. Multiple UAVs are deployed in multiple disaster-stricken areas to form an array for collecting ground information. This array forms a unidirectional multi-hop wireless communication link, which promptly transmits information collected from all disaster-stricken areas to a remote base station for rescue personnel to reference. Summary of the Invention

[0004] This invention designs and develops a data transmission method for UAV self-organizing multi-hop networks in disaster areas. By using an objective function and an improved multi-objective gray wolf algorithm, the optimal position, ideal excitation current weight, and optimal communication sequence of each UAV array are obtained. The UAV array transmits information according to the obtained optimal communication sequence, which improves the transmission efficiency of the UAV array and reduces the motion energy consumption of the UAV, thus meeting the requirements of disaster relief.

[0005] The technical solution provided by this invention is as follows:

[0006] A method for data transmission in a self-organizing multi-hop network for unmanned aerial vehicles (UAVs) in disaster areas includes:

[0007] Step 1: Determine the number of drone arrays and the number of drones in each array based on the number of disaster areas;

[0008] Step 2: Determine the movement range of each drone array and the location of the remote ground base station;

[0009] Step 3: Establish the objective function, and use the objective function and the improved multi-objective gray wolf algorithm to obtain the optimal position, ideal excitation current weight, and optimal communication sequence for each UAV array;

[0010] Step 4: Each UAV array moves to the optimal position and adjusts the excitation current to the optimal weight. The UAV array transmits information according to the obtained optimal communication order. By executing a virtual antenna array, cooperative beamforming is used to send data to the next array in the communication order until the last UAV array sends all the collected information to the remote ground base station.

[0011] Preferably, in step one, multiple UAV arrays form a unidirectional multi-hop wireless communication link, and each UAV array that achieves cooperative beamforming is designated as a link node, further comprising:

[0012] Determine the set of link nodes based on the number of disaster areas: and the collection of drones in each node: ;

[0013] in, Represents the number of link nodes. This represents the number of drones in the node.

[0014] Preferably, step three includes:

[0015] Establish the objective function and define the multi-objective problem: ;

[0016] ;

[0017] ;

[0018] );

[0019] In the formula, For multi-objective problems, Let the first objective function be... The second objective function is... For the third objective function, Represents the position of the drone in three-dimensional space. The excitation current weight of the drone is represented. Represents the communication order between link nodes, with A type of communication method.

[0020] Preferably, step four includes:

[0021] Step 1: Based on the determined population size, initialize the total number of UAVs in all arrays in each individual, initialize the position and excitation current weight of all UAV elements, and initialize a set of inter-array communication sequences using the partial matching crossover method. Combine the position, excitation current weight, and communication sequence of all UAVs in each individual as candidate solutions to form a candidate solution set.

[0022] Step 2: Based on the established objective function, calculate the objective value corresponding to each individual in the candidate solution set, sort them according to the priority of the three objective functions, and select the non-dominated solution set Archive of the candidate solution set according to the Pareto optimality method, with a maximum limit of 30.

[0023] Step 3: Set the iteration count to 500 and perform iterations. Using the leader selection strategy of the traditional multi-objective gray wolf algorithm, select the top three optimal solutions from the Archive, and label them as follows: , and ;

[0024] use , and Location information of all UAVs in the candidate solutions The update is performed, and the excitation current weights of all UAVs in the candidate solutions are also updated. The communication order between all link nodes composed of UAVs in the candidate solution is updated by the crossover mutation operator. It then learns a reverse learning strategy to update the location information of all UAVs in the candidate solutions. ;

[0025] Step 4: After all individuals have been updated, select the non-dominated solution set from the updated candidate solutions and merge it with Archive to form a brand new non-dominated solution new_Archive. Use the same rules to select the non-dominated solution set from new_Archive, with a maximum size limit of 30, and mark it as Archive for the next iteration update of the population individuals.

[0026] Step 5: When the set number of iterations, 500, is reached, the iteration ends, and the optimal solution that satisfies the objective function is output.

[0027] If the number of iterations has not been reached, repeat steps 3 and 4.

[0028] The beneficial effects of this invention are as follows: This invention provides a cooperative beamforming method based on a UAV virtual antenna array. Multiple UAVs are deployed in multiple disaster-stricken areas to form a UAV array for collecting ground information. These UAV arrays constitute a unidirectional multi-hop wireless communication link, which promptly transmits information collected from all disaster-stricken areas to a remote base station for rescue personnel. To overcome the limitations of UAV power and onboard energy, a multi-objective problem of maximizing transmission rate and minimizing energy consumption is used to jointly optimize the sum of transmission rates of all links in the UAV network, the minimum transmission rate between arrays, and the motion energy consumption of the UAV arrays. An improved multi-objective gray wolf optimization algorithm is used to solve the designed multi-objective problem, deriving the optimal position of each UAV array, the ideal excitation current weight, and the optimal transmission order between arrays. This improves the transmission efficiency of the UAV array and reduces the motion energy consumption of the UAVs, thereby meeting the transmission needs of disaster relief. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the cooperative beamforming disaster relief data communication structure based on a UAV virtual antenna array as described in this invention.

[0030] Figure 2 This is a flowchart illustrating the collaborative beamforming disaster relief data communication based on UAV antenna arrays as described in this invention. Detailed Implementation

[0031] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0032] like Figure 1-2 As shown, this invention provides a data transmission method for UAV self-organizing multi-hop networks in disaster areas. By using an objective function and an improved multi-objective gray wolf algorithm, the optimal position, ideal excitation current weight, and optimal communication sequence for each UAV array are obtained. The UAV array transmits information according to the obtained optimal communication sequence, improving the transmission efficiency of the UAV array and reducing the kinetic energy consumption of the UAVs, thus meeting the requirements of disaster relief. The method includes:

[0033] Step 1: Determine the number of drone arrays and the number of drones in each array based on the number of disaster areas;

[0034] Step 2: Determine the movement range of each drone array and the location of the remote ground base station;

[0035] Step 3: Establish the objective function, and use the objective function and the improved multi-objective gray wolf algorithm to obtain the optimal position, ideal excitation current weight, and optimal communication sequence for each UAV array;

[0036] The improved multi-objective gray wolf algorithm includes:

[0037] (1) Improve the performance of the initial solution of the traditional multi-objective Grey Wolf algorithm by introducing a chaotic solution initialization operator (the standard form of the TentMap chaotic operator):

[0038] Standard TentMap format:

[0039] ;

[0040] In the formula, The index subscript representing the chaotic sequence, the initialization of the solution can be expressed as:

[0041] ;

[0042] In the formula, The solution is the first Each solution dimension and Representing the first The maximum and minimum values ​​of the boundary corresponding to the dimension.

[0043] (2) The convergence speed and search performance of the algorithm are balanced by optimizing the linear convergence factor of the original algorithm to be nonlinear:

[0044]

[0045] In the formula, and They represent convergence factors respectively. The maximum and minimum values ​​are generally 2 and 0; and This represents the current iteration number and the maximum iteration number.

[0046] (3) Formulate a hybrid solution update strategy to update the hybrid solution space in the algorithm, wherein an adaptive update strategy and a reverse learning method are used to update the position information in the continuous solution space, a sine and cosine optimization strategy is used to update the excitation current information in the continuous solution space, and a crossover mutation operator is used to update the communication order in the discrete solution space:

[0047] Adaptive strategy:

[0048] ;

[0049] In the formula, +1 and All represent the location information of the drone. ; , and Represent , and fitness value, This represents the average fitness value of all individuals in the population.

[0050] Sine and Cosine Optimization Strategies:

[0051] ;

[0052] in, , , , ;

[0053] Reverse learning:

[0054] ;

[0055] Step 4: Each UAV array moves to the optimal position and adjusts the excitation current to the optimal weight. The UAV array transmits information according to the obtained optimal communication order. By executing a virtual antenna array, cooperative beamforming is used to send data to the next array in the communication order until the last UAV array sends all the collected information to the remote ground base station.

[0056] A potential solution in the solution space of an optimization problem is called an individual, and a group of multiple individuals is called a population. Multiple drones are deployed to each disaster area to form a drone array. These drone arrays constitute a unidirectional, multi-hop wireless communication link. By implementing a virtual antenna array and using cooperative beamforming, data is transmitted to the next array in the communication sequence. Finally, the last drone array transmits all data to the remote base station. Each drone array implementing cooperative beamforming is called a link node, specifically including:

[0057] Step 1: Determine the set of link nodes based on the number of disaster areas. and the collection of drones in each node ,in Represents the number of link nodes. This represents the number of drones in the node.

[0058] Step 2: Determine the location of the remote base station receiving the information;

[0059] Step 3: Design the objective function and define the multi-objective problem:

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] In the formula, For multi-objective problems, Represents the position of the drone in three-dimensional space. The excitation current weight of the drone is represented. Represents the communication order between link nodes, with A type of communication method.

[0073] In the first objective function, This represents the rate at which data is transmitted between link nodes. This represents the rate at which the final link node transmits data to the remote base station. Indicates transmission bandwidth. This indicates the total transmission power of the drone array. represent Path loss coefficient in transmission mode This represents the distance from the origin of the link node to the drone used to receive data in the next link node; This is the path loss index. Indicates noise power. Indicates the antenna array gain, where, Represents the direction of the transmission target. This indicates the size of the far-field beam pattern of each UAV unit. It refers to antenna array efficiency; For array factor, For the number of drones, Indicates the first The first in the group of UAV antenna arrays The excitation current weight of the drone. Represents the phase constant. Indicates wavelength. and These represent the elevation angle and the azimuth angle, respectively. Indicates the first The first in the group of drone array The location of the drone; Represents channel gain. represent Path loss coefficient in transmission mode This represents the distance from the origin of the drone's virtual antenna array to the remote base station; This represents the probability of transmission using a line-of-sight channel model. Indicates the angle of elevation. , as well as It is a constant that changes due to the environment; similarly, This represents the probability of transmission using a non-line-of-sight channel model, and ; and These represent the attenuation factors under two different channel models.

[0074] In the second objective function, ,therefore This represents the minimum transmission rate of all drone arrays communicating using the A2A model. It is the minimization operator that calculates the minimum value of a vector;

[0075] In the third objective function, Indicates the first The energy consumed by the drones to move to the optimal position. Representing the The time it takes for a group of drones to move to the optimal location; Indicates the drone at a certain time speed, Indicates the end time of the flight. Indicates the weight of the drone. Represents gravitational acceleration; This indicates the propulsion energy consumption of a drone when flying in two-dimensional horizontal space. and These are two constants, representing the blade profile and induced power in the hovering state, respectively. Indicates the speed of the drone. This represents the tip velocity of the rotor blades. This represents the average rotor blade induced velocity during hovering. and These represent the fuselage drag ratio and rotor blade stiffness, respectively. and These represent air density and rotor disk area, respectively.

[0076] Step 4: Solve the defined multi-objective problem using the improved multi-objective gray wolf optimization algorithm to obtain the optimal position of the UAV, the optimal excitation current weight, and the optimal communication order between the link nodes in each link node;

[0077] Including: (1) According to the determined population size, initialize the total number of UAVs in all arrays in each individual, initialize the position and excitation current weight of all UAV elements, and use the partial matching crossover method to initialize a set of communication order between arrays, and take the position, excitation current weight and communication order of all UAVs in each individual as candidate solutions, and these individuals together form a set of candidate solutions;

[0078] (2) Evaluate the quality of solutions in the candidate solution set based on the objective function of the design;

[0079] Based on the established objective function, the objective value corresponding to each individual in the candidate solution set is calculated. Then, according to the rule of the priority of the three objectives from high to low, the non-dominated solution set (Archive) of the candidate solution set is selected according to the Pareto optimality rule. The maximum size of the Archive is limited to 30.

[0080] (3) The iteration begins, with the number of iterations set to 500. Based on the leader selection strategy of the traditional multi-objective gray wolf algorithm, the first three optimal solutions are selected from the Archive and marked as... , and Based on the proposed adaptive improvement strategy, using , and Update the location information of all UAVs in the candidate solutions in (1) The excitation current weights of all UAVs are updated using a sine and cosine optimization strategy. The communication order of all drones is updated using the crossover mutation operator. Finally, a reverse learning strategy is used to update the location information of all drones. Before the end of each iteration, the target value of the updated individual must be recalculated and updated.

[0081] (4) After all individuals have been updated, select the non-dominated solution set from the updated candidate solutions according to the rules in (2) and merge it with Archive to form a brand new non-dominated solution set (new_Archive). Finally, select the non-dominated solution set from new_Archive according to the same rules, with a maximum size limit of 30, and mark it as Archive for the next iteration update of the population individuals;

[0082] (5) Based on (4), update the position information of other solutions by obtaining the positions of the three optimal solutions, update the excitation current weights of all solutions by using the sine and cosine optimization strategy, and update the communication order of all solutions by using the crossover mutation operator;

[0083] (6) When the set number of iterations is 500, the iteration ends and the optimal solution that satisfies the objective function is output, namely the optimal position, optimal excitation current weight and optimal communication order of all UAV elements; otherwise, these solutions are returned as the current solution to step (3), and then (3) to (5) are repeated until the iteration termination condition is met.

[0084] Step 5: Move the drone to the optimal position and adjust the excitation current to the optimal weight;

[0085] Step 6: The link nodes transmit information according to the obtained optimal communication order, such as the communication sequence. Then the second link node first transmits the collected information to the first link node, and then the first link node transmits all the information collected from the disaster area on the ground and the previous link node to the third link node, and so on, until finally all the information of the disaster area is sent to the remote base station by the ninth link node.

[0086] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for data transmission in a self-organizing multi-hop network for unmanned aerial vehicles (UAVs) in disaster areas, characterized in that, include: Step 1: Determine the number of drone arrays and the number of drones in each array based on the number of disaster areas; Multiple drone arrays form a unidirectional, multi-hop wireless communication link. Each drone array that performs cooperative beamforming is designated as a link node. The link also includes: Determine the set of link nodes based on the number of disaster areas: and the collection of drones in each node: ; in, Represents the number of link nodes. This represents the number of drones in the node; Step 2: Determine the movement range of each drone array and the location of the remote ground base station; Step 3: Establish the objective function, and use the objective function and the improved multi-objective gray wolf algorithm to obtain the optimal position, ideal excitation current weight, and optimal communication sequence for each UAV array; Establish the objective function and define the multi-objective problem: ; ； ； ； In the formula, For multi-objective problems, Let the first objective function be... The second objective function is... For the third objective function, Represents the position of the drone in three-dimensional space. The excitation current weight of the drone is represented. Represents the communication order between link nodes, with Various communication methods Indicates the first The energy consumed by the drones to move to the optimal position. This represents the rate at which data is transmitted between link nodes. This represents the rate at which the final link node transmits data to the remote base station. Step 4: Each UAV array moves to the optimal position and adjusts the excitation current to the optimal weight. The UAV array transmits information according to the obtained optimal communication order. By executing a virtual antenna array, cooperative beamforming is used to send data to the next array in the communication order until the last UAV array sends all the collected information to the remote ground base station, including: Step 1: Based on the determined population size, initialize the total number of UAVs in all arrays in each individual, initialize the position and excitation current weight of all UAV elements, and initialize a set of inter-array communication sequences using the partial matching crossover method. Combine the position, excitation current weight, and communication sequence of all UAVs in each individual as candidate solutions to form a candidate solution set. Step 2: Based on the established objective function, calculate the objective value corresponding to each individual in the candidate solution set, sort them according to the priority of the three objective functions, and select the non-dominated solution set Archive of the candidate solution set according to the Pareto optimality method, with a maximum limit of 30. Step 3: Set the iteration count to 500 and perform iterations. Using the leader selection strategy of the traditional multi-objective gray wolf algorithm, select the top three optimal solutions from the Archive, and label them as follows: , and ; use , and Location information of all UAVs in the candidate solutions The update is performed, and the excitation current weights of all UAVs in the candidate solutions are also updated. The communication order between all link nodes composed of UAVs in the candidate solution is updated by the crossover mutation operator. It then learns a reverse learning strategy to update the location information of all UAVs in the candidate solutions. ; Step 4: After all individuals have been updated, select the non-dominated solution set from the updated candidate solutions and merge it with Archive to form a brand new non-dominated solution new_Archive. Use the same rules to select the non-dominated solution set from new_Archive, with a maximum size limit of 30, and mark it as Archive for the next iteration update of the population individuals. Step 5: When the set number of iterations, 500, is reached, the iteration ends, and the optimal solution that satisfies the objective function is output. If the number of iterations has not been reached, repeat steps 3 and 4.

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