Multi-unmanned aerial vehicle communication sub-channel allocation and trajectory joint optimization method and device

Abstract

The invention provides a multi-unmanned aerial vehicle communication sub-channel allocation and trajectory joint optimization method and device, and relates to the technical field of unmanned aerial vehicle communication. The method comprises the following steps: dividing a ground combat area into a plurality of combat areas, and associating each unmanned aerial vehicle with a plurality of ground combat nodes of one combat area through a user association method; constructing a maximum ground combat node reconnaissance data return total rate model; proposing a multi-unmanned aerial vehicle auxiliary communication sub-channel allocation method, and solving a maximum ground combat node reconnaissance data return total rate model to obtain a sub-channel allocation result; and providing a multi-unmanned aerial vehicle auxiliary communication trajectory optimization method, approximating the maximum ground combat node reconnaissance data return total rate model to a convex model and solving the model to obtain an optimal unmanned aerial vehicle flight trajectory, and realizing maximization of the ground combat node reconnaissance data return total rate. According to the invention, the cross-domain battlefield cooperative combat and situation awareness capabilities are effectively enhanced, and the development of the unmanned aerial vehicle cluster communication technology is promoted.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method and apparatus for joint optimization of multi-UAV communication sub-channel allocation and trajectory. Background Technology

[0002] In modern military communications, unmanned aerial vehicles (UAVs) have become a key element in enhancing battlefield situational awareness in cross-domain collaborative operations due to their significant advantages such as high mobility, flexible deployment, and ease of establishing line-of-sight links. UAV swarms can serve as aerial communication relay base stations, providing wide-area, reliable reconnaissance data backhaul services to ground combat nodes, which is crucial for ensuring the effectiveness of cross-domain collaborative operations and enhancing battlefield situational awareness. However, current UAV communication technologies suffer from a series of problems, such as unfair resource allocation and inefficient user association, which severely restrict their effectiveness in the battlefield environment.

[0003] First, existing subchannel allocation technologies generally prioritize maximizing the overall system rate. While this optimization strategy can improve the overall throughput of the combat network to some extent, it neglects the communication needs of ground combat nodes with poor channel conditions or those located at the service edge. This results in some nodes experiencing excessively low reconnaissance data transmission rates, failing to meet minimum mission requirements. In the rapidly changing battlefield environment, the loss of information from any single combat unit can have a catastrophic impact on the entire battle. Therefore, the limitations of existing subchannel allocation technologies in military applications are becoming increasingly apparent, urgently requiring an innovative solution that can improve the overall system rate while ensuring communication fairness among all nodes.

[0004] Secondly, traditional UAV user association methods fail to fully consider the inherent clustering characteristics of ground combat nodes deployed in a battlefield environment. In collaborative combat missions, ground nodes are often distributed in clusters in the form of small groups or detachments to execute specific tactical tasks. However, existing user association technologies mostly treat ground nodes as independent, homogeneous individuals, ignoring their collective characteristics, resulting in UAVs being unable to form an efficient cluster service mode when performing service association. This association method not only reduces the targeting and efficiency of UAV services but also makes it difficult to match the allocation of network resources with tactical intentions and the collaborative relationships between nodes, thus limiting further improvements in the overall rate of reconnaissance data transmission. In addition, flight trajectory planning in existing UAV-assisted communication schemes often relies on preset paths or requires a large amount of manual intervention, which limits the battlefield adaptability of UAV platforms. In a dynamic and complex battlefield environment, UAVs struggle to continuously provide optimal communication relay services to ground combat nodes, thus becoming a key bottleneck restricting the improvement of reconnaissance data transmission efficiency. Summary of the Invention

[0005] To address the technical problems of low efficiency and poor fairness in the transmission of reconnaissance data from ground combat nodes, unstable communication links between ground combat nodes, and limited information processing capabilities of combat equipment in existing technologies, this invention provides a method and apparatus for multi-UAV communication sub-channel allocation and trajectory joint optimization. The technical solution is as follows:

[0006] On the one hand, a method for joint optimization of multi-UAV communication sub-channel allocation and trajectory is provided. This method is implemented by a device for joint optimization of multi-UAV communication sub-channel allocation and trajectory, and includes:

[0007] S1. Initialize ground combat area data and data for multiple drones.

[0008] S2. Divide the ground combat area into multiple theaters, each of which includes multiple ground combat nodes. Use user association methods to associate each UAV with multiple ground combat nodes in a theater and determine the initial circular trajectory of each UAV.

[0009] S3. Construct a model that maximizes the total rate of reconnaissance data transmission back to ground combat nodes.

[0010] S4. A method for allocating sub-channels for multi-UAV assisted communication is proposed. Based on the initial circular trajectory and the method for allocating sub-channels for multi-UAV assisted communication, a model is solved to maximize the total rate of data transmission back to ground combat nodes, and the sub-channel allocation results are obtained.

[0011] S5. A multi-UAV assisted communication trajectory optimization method is proposed. Based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model for maximizing the total data transmission rate of ground combat nodes is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, thereby maximizing the total data transmission rate of ground combat nodes.

[0012] Optionally, S2 includes:

[0013] The k-means++ clustering algorithm is used to determine the optimal number of theater zones by comparing the average profile coefficients. The ground combat area is then divided into multiple theater zones. In each theater zone, multiple ground combat nodes are provided with uplink relay services for reconnaissance data by a UAV. The initial flight trajectory of each UAV is circular, with the center of the circle being the cluster center of the theater zone and the radius covering half of the ground combat nodes in the theater zone.

[0014] Optionally, S3 includes:

[0015] S31. Discretize the total flight cycle of each UAV into multiple sub-cycles. In any sub-cycle, assign a sub-channel to each ground combat node. Ground combat nodes in the same theater cannot use the same sub-channel, thus constructing a sub-channel allocation restriction.

[0016] S32. Limit the minimum reconnaissance data transmission rate for each ground combat node to meet the real-time transmission requirements of reconnaissance data for ground combat nodes in cross-domain integrated operations.

[0017] S33. The displacement distance of each UAV between any adjacent sub-cycles must meet the physical constraint of the UAV's maximum flight speed, thus constructing the UAV flight trajectory constraint.

[0018] S34. Based on the sub-channel allocation restrictions, the minimum reconnaissance data return rate restrictions for each ground combat node, and the UAV flight trajectory restrictions, the total reconnaissance data return rate of the ground combat node is obtained, as shown in the following formula (1):

[0019]

[0020] In the formula, r total Let A represent the total data transmission rate of ground combat nodes, Q represent the UAV trajectory matrix, i∈τ represent sub-periods, u∈U represent UAVs, g∈G represent ground combat nodes, and k∈κ represent the set of sub-channels. This indicates that during sub-cycle i, UAV u provides reconnaissance data uplink relay service to ground combat node g through sub-channel k. This indicates that during sub-cycle i, UAV u did not provide reconnaissance data uplink relay service to ground combat node g via sub-channel k, where B represents the sub-channel bandwidth. ε represents the signal transmit power of ground combat node g during sub-period i, and ε represents the channel gain at a distance of 1m. q represents the horizontal position of the drone u in sub-period i. g σ represents the horizontal position of the ground combat node g, H represents the flight altitude of the UAV, and σ represents the horizontal position of the ground combat node g. 2 Let g' represent the additive white Gaussian noise power, and g' represent the ground combat node that will cause electromagnetic interference to the ground combat node g. This represents the set of ground combat nodes that use sub-channel k to transmit reconnaissance data back during sub-period i. q represents the signal transmission power of the interfering node g′ during sub-period i. g′ This indicates the horizontal position of the interfering node g′.

[0021] S35. Based on the total data transmission rate of ground combat nodes, the model for maximizing the total data transmission rate of ground combat nodes is obtained, as shown in equation (2) below:

[0022]

[0023] Optionally, in S4, the model for maximizing the total data return rate of ground combat nodes' reconnaissance data is solved based on the initial circular trajectory and the multi-UAV assisted communication sub-channel allocation method, resulting in the sub-channel allocation results, including:

[0024] S41. Sort the channel gain values ​​between the ground combat nodes and the corresponding UAVs in the theater of operations in descending order to obtain the sorting results. Assign sub-channels to the bottom 10% of the ground combat nodes in the sorting results separately and do not participate in matching and switching.

[0025] S42. For the multiple sub-channels corresponding to the top 90% of the ground combat nodes in the sorting results, calculate the maximum number of ground combat nodes that can use each sub-channel simultaneously, and obtain the upper limit N of the number of nodes that can simultaneously use the same sub-channel for reconnaissance data transmission. max .

[0026] S43. Based on the upper limit N of the number of nodes simultaneously using the same sub-channel for reconnaissance data return. max The top 90% of ground combat nodes in the ranking results are divided into two categories of sub-channels. The number of sub-channels in each category is determined by solving the sub-channel classification equation. The first category of sub-channels is defined as the sub-channels that are N... max Multiple ground combat nodes can use it simultaneously; the second type of subchannel is used by N max -1 ground combat node can be used simultaneously.

[0027] S44. Determine whether the number of sub-channels 'a' that have been allocated is less than the total number of sub-channels 'K'. If it is less, proceed to step S45. If it is not less, end the sub-channel allocation and output the sub-channel allocation result.

[0028] S45. Select the ground combat node ranked first in the sorting results that is in an unassigned sub-channel state, and mark it as the primary allocation node g. main .

[0029] S46. Determine if the number of sub-channels 'a' that have been allocated is less than K-x+1; if it is less, select a sub-channel in another theater of operations that is compatible with the primary allocation node 'g'. main The corresponding UAV with the smallest channel gain value N max -1 node as matching node g match If it is not less than, then select a node g in other war zones that is the same as the primary allocation node g. main The corresponding UAV with the smallest channel gain value N max -2 nodes as matching nodes g matchWhere x represents the number of second-type sub-channels; other theaters refer to the ground combat areas excluding the primary allocation node g. main Other theaters outside the current theater.

[0030] S47. Determine the matching node g match Check if any ground combat nodes have been previously assigned sub-channels; if not, then assign the primary allocation node g. main Matching node g match If the nodes are matched into a group, an unused sub-channel is allocated to each ground combat node in the group, and the ground combat nodes in the group are marked as having an allocated sub-channel. Let a = a + 1, and proceed to step S44. If a match exists, calculate the matching node g. match With the primary allocation node g main Total reconnaissance data transmission rate r later And matching node g match The total data transmission rate r of the old group's reconnaissance data previous Determine r previous >r later Check if the condition is met; if so, update the matching node g. match For ground combat nodes that have already been assigned sub-channels, proceed to step S47; if not, then match node g. match Rematched with the primary allocation node g main A group is formed, and an unused sub-channel is allocated to each ground combat node in the group. The ground combat nodes in the group are then marked as having an allocated sub-channel status. The matching node g is then... match The remaining ground combat nodes in the old group are marked as unassigned sub-channels, and the process proceeds to step S44.

[0031] Optionally, the calculation in S42 can be performed by a maximum of several ground combat nodes simultaneously, as shown in equation (3) below:

[0032]

[0033] In the formula, N max G represents the maximum number of nodes that can simultaneously use the same sub-channel to transmit reconnaissance data back. N K represents the total number of ground combat nodes, and K represents the total number of sub-channels.

[0034] The sub-channel classification equation in S43 is shown in equation (4) below:

[0035] x(N max -1)+(K-0.1G N -x)N max =0.9G N (4)

[0036] In the formula, x represents the number of Nmax - The number of sub-channels used simultaneously by one ground combat node, K-0.1G N -x indicates that N is used. max The number of sub-channels used simultaneously by each ground combat node.

[0037] Optionally, in S5, based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model maximizing the total data transmission rate of ground combat nodes is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, including:

[0038] S51. Based on the sub-channel allocation results, the successive convex approximation method is used and slack variables are introduced to approximate the model that maximizes the total rate of reconnaissance data backhaul from ground combat nodes as a convex model, thus obtaining the approximate optimized model.

[0039] S52. Use the convex optimization toolbox to iteratively solve the approximate optimization model; wherein, the stopping condition of the iteration process is that the number of iterations reaches the maximum number of iterations, or the rate of change of the total rate of return of reconnaissance data from ground combat nodes between two consecutive iterations is less than a preset threshold.

[0040] Alternatively, the approximate optimized model is shown in equations (5)-(11) below:

[0041]

[0042]

[0043] In the formula, Q represents the UAV trajectory matrix, S represents the relaxation variable matrix, i∈τ represents the sub-period, u∈U represents the UAV, g∈G represents the ground combat node, and k∈κ represents the set of sub-channels. This indicates that during sub-cycle i, UAV u provides reconnaissance data uplink relay service to ground combat node g through sub-channel k. This indicates that during sub-cycle i, UAV u did not provide reconnaissance data uplink relay service to ground combat node g via sub-channel k. This represents the lower bound of the amount of reconnaissance data transmitted back by ground combat node g through subchannel k of UAV u during sub-period i, where B represents the subchannel bandwidth, and σ represents the data volume. 2 Let g' represent the additive white Gaussian noise power, and g' represent the ground combat node that will cause electromagnetic interference to the ground combat node g. This represents the set of ground combat nodes that use sub-channel k to transmit reconnaissance data back during sub-period i. ε represents the signal transmission power of the interfering node g′ during sub-period i, and ε represents the channel gain at a distance of 1m. R represents the slack variable corresponding to UAV u and interference node g′ in sub-period i. min This represents the minimum reconnaissance data transmission rate threshold for each ground combat node in any sub-cycle. This indicates the horizontal position of the drone u in the last sub-cycle I. T represents the horizontal position of the drone u in sub-period i. i v represents the duration of sub-period i. max This indicates the maximum instantaneous speed of the drone's flight. This represents the optimal horizontal position of UAV u in sub-period i, obtained in the t-th iteration during iterative optimization using the successive convex approximation method. g′ H represents the horizontal position of the interfering node g′, and H represents the flight altitude of the UAV. q represents the signal transmission power of ground combat node g during sub-period i. g This indicates the horizontal position of the ground combat node g.

[0044] On the other hand, a device for joint optimization of multi-UAV communication sub-channel allocation and trajectory is provided. This device is applied to the method for joint optimization of multi-UAV communication sub-channel allocation and trajectory. The device includes:

[0045] The initialization module is used to initialize ground combat area data and data from multiple drones.

[0046] The association module is used to divide the ground combat area into multiple theaters, each of which includes multiple ground combat nodes. Through user association methods, each UAV is associated with multiple ground combat nodes in a theater and the initial circular trajectory of each UAV is determined.

[0047] The module is used to build a model that maximizes the total rate of reconnaissance data transmission back to ground combat nodes.

[0048] The sub-channel allocation module is used to propose a sub-channel allocation method for multi-UAV assisted communication. Based on the initial circular trajectory and the multi-UAV assisted communication sub-channel allocation method, it solves the model that maximizes the total data transmission rate of ground combat nodes' reconnaissance data and obtains the sub-channel allocation results.

[0049] The flight trajectory optimization module is used to propose a multi-UAV assisted communication trajectory optimization method. Based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model for maximizing the total data transmission rate of ground combat nodes is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, thereby maximizing the total data transmission rate of ground combat nodes.

[0050] On the other hand, a device for joint optimization of multi-UAV communication sub-channel allocation and trajectory is provided. The device includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the methods described above for joint optimization of multi-UAV communication sub-channel allocation and trajectory is implemented.

[0051] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the above-described methods for multi-UAV communication sub-channel allocation and trajectory joint optimization.

[0052] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0053] This invention aims to propose a joint optimization method for multi-UAV assisted communication sub-channel allocation and flight trajectory to overcome the shortcomings of existing technologies. This method innovatively integrates core technologies such as user association, sub-channel allocation, and trajectory optimization. While significantly improving the overall data transmission rate of reconnaissance data from ground combat nodes, it also ensures fairness in data transmission, fully adapting to the cluster distribution characteristics of ground combat nodes. This provides an efficient and reliable solution for UAV applications in military communication scenarios, effectively enhancing battlefield situational awareness and promoting the development of UAV communication technology. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of a method for joint optimization of multi-UAV communication sub-channel allocation and trajectory provided by an embodiment of the present invention;

[0056] Figure 2 This is a flowchart of the method for joint optimization of multi-UAV assisted communication sub-channel allocation and flight trajectory in a cross-domain collaborative combat scenario provided by an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of a multi-UAV assisted communication network structure in a cross-domain collaborative combat scenario provided by an embodiment of the present invention;

[0058] Figure 4 This is a block diagram of a multi-UAV communication sub-channel allocation and trajectory joint optimization device provided in an embodiment of the present invention;

[0059] Figure 5 This is a schematic diagram of the structure of a multi-UAV communication sub-channel allocation and trajectory joint optimization device provided in an embodiment of the present invention. Detailed Implementation

[0060] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0061] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0062] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0063] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0064] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0065] This invention provides a method for joint optimization of multi-UAV communication sub-channel allocation and trajectory. This method can be implemented by a device for joint optimization of multi-UAV communication sub-channel allocation and trajectory, which can be a terminal or a server. Figure 1 , Figure 2 The flowchart shown is for a method of joint optimization of multi-UAV communication sub-channel allocation and trajectory. The processing flow of this method may include the following steps:

[0066] S1. Initialize ground combat area data and data for multiple drones.

[0067] Specifically, such as Figure 2 As shown, initialize the position q of the ground combat node g. g Signal transmission power Reconnaissance data return rate threshold r minInitialize the maximum flight speed v of the drone max Number of sub-channels |κ|, flight altitude H, maximum number of trajectory optimization iterations t max Set the sub-cycle duration T in relation to the convergence threshold η. i Subchannel bandwidth B, unity channel gain ε, additive white Gaussian noise power σ 2 .

[0068] S2. Divide the ground combat area into multiple theaters, each of which includes multiple ground combat nodes. Use user association methods to associate each UAV with multiple ground combat nodes in a theater and determine the initial circular trajectory of each UAV.

[0069] In one feasible implementation, the present invention proposes a multi-UAV assisted communication user association method, which matches each UAV with a ground combat node with cluster characteristics.

[0070] Specifically, the k-means++ clustering algorithm is used to divide the ground combat area into multiple theaters. In each theater, ground nodes are provided with uplink relay services for reconnaissance data by a UAV. The optimal number of theater divisions is determined by comparing the average contour coefficient.

[0071] All drones initially follow a circular trajectory, with the center being the cluster center of the war zone and the radius covering exactly half of the ground nodes in that war zone.

[0072] S3. Construct a model that maximizes the total rate of reconnaissance data transmission back to ground combat nodes.

[0073] Optionally, step S3 above may include the following steps S31-S35:

[0074] S31. Discretize the total flight cycle of each UAV into multiple sub-cycles. In any sub-cycle, assign a sub-channel to each ground combat node. Ground combat nodes in the same theater of operations cannot use the same sub-channel. The sub-channel allocation restriction is expressed as follows:

[0075]

[0076]

[0077] Where τ, U, G, and κ represent the sets of sub-periods, UAVs, ground combat nodes, and sub-channels, respectively, and i, u, g, and k represent the sub-period i, UAV u, ground combat node g, and sub-channel k, respectively. This indicates that during sub-cycle i, UAV u provides reconnaissance data uplink relay service to ground combat node g through sub-channel k. G' represents the set of ground nodes for which UAV u does not provide uplink relay service for reconnaissance data to ground combat node g through subchannel k during sub-period i.

[0078] S32. To meet the real-time transmission requirements of reconnaissance data from ground combat nodes in cross-domain integrated operations, it is necessary to limit the minimum reconnaissance data return rate for each ground node, namely:

[0079]

[0080] Where b represents the sub-channel bandwidth, ε represents the signal transmit power of ground combat node g during sub-period i, and ε represents the channel gain at a distance of 1m. q represents the horizontal position of the drone u in sub-period i. g σ represents the horizontal position of the ground combat node g, H represents the flight altitude of the UAV, and σ represents the horizontal position of the ground combat node g. 2 Let g' represent the additive white Gaussian noise power, and g' represent the ground combat node that will cause electromagnetic interference to the ground combat node g. This represents the set of ground combat nodes that use sub-channel k to transmit reconnaissance data back during sub-period i. q represents the signal transmission power of the interfering node g′ during sub-period i. g′ r represents the horizontal position of the interfering node g′. min This represents the minimum reconnaissance data return rate threshold for each ground combat node in any sub-cycle.

[0081] S33. To ensure the continuity of battlefield reconnaissance data uplink relay services provided by aerial UAVs, the UAV must return to its initial deployment position at the end of each total flight cycle. When optimizing the UAV's flight trajectory, the displacement distance of the UAV between any adjacent sub-cycles must meet the physical constraint of its maximum flight speed. The above UAV flight trajectory constraints can be expressed as:

[0082]

[0083] Among them, T i v represents the duration of sub-period i. max This represents the maximum instantaneous speed of the drone's flight, and I represents the last sub-cycle.

[0084] S34, The total data transmission rate of ground combat node reconnaissance data is:

[0085]

[0086] Where, r total The total rate of data transmission from ground combat nodes is represented by A, the sub-channel allocation matrix is ​​represented by Q, and the UAV trajectory matrix is ​​represented by Q.

[0087] S35, the optimization objective of the model that maximizes the total rate of reconnaissance data transmission from ground combat nodes is:

[0088]

[0089] S4. A method for allocating sub-channels for multi-UAV assisted communication is proposed. Based on the initial circular trajectory and the method for allocating sub-channels for multi-UAV assisted communication, a model is solved to maximize the total rate of reconnaissance data transmission back to ground combat nodes. The sub-channel allocation results are obtained, which can reduce interference between ground combat nodes and improve the total rate of reconnaissance data transmission back to ground combat nodes.

[0090] Optionally, the sub-channel allocation result obtained by solving the model that maximizes the total rate of reconnaissance data return from the ground combat node based on the initial circular trajectory and the multi-UAV assisted communication sub-channel allocation method in S4 may include the following steps S41-S47:

[0091] S41. Calculate the channel gain value between each ground combat node and each UAV based on the initial circular trajectory. Sort the channel gain values ​​between the ground combat node and the corresponding UAV in the theater in descending order. Assign a sub-channel to the ground nodes whose channel gain values ​​are in the bottom 10% of the ranking. These nodes use a sub-channel alone and do not participate in subsequent matching and exchange.

[0092] S42. Calculate the maximum number of ground combat nodes that can simultaneously use each of the remaining sub-channels. The calculation formula is as follows:

[0093]

[0094] In the formula, N max G represents the maximum number of nodes that can simultaneously use the same sub-channel to transmit reconnaissance data back. N K represents the total number of ground combat nodes, and K represents the total number of sub-channels.

[0095] S43. Divide the remaining sub-channels into two categories, one of which is N max Multiple ground combat nodes are used simultaneously, one type is N max - One ground combat node can be used simultaneously. The number of sub-channels in each category is determined by solving the following sub-channel classification equation:

[0096] x(N max -1)+(K-0.1G N -x)N max =0.9G N (11)

[0097] In the formula, x represents the number of N max - The number of sub-channels used simultaneously by one ground combat node, K-0.1GN -x indicates that N is used. max The number of sub-channels used simultaneously by each ground combat node.

[0098] S44. Determine whether the number of sub-channels 'a' that have been allocated is less than the total number of sub-channels 'K'. If it is less, proceed to step S45. If it is not less, end the sub-channel allocation and output the sub-channel allocation result.

[0099] S45. Select the ground combat node ranked first and in an unassigned sub-channel state from the sorting results of step S41, and mark it as the main allocation node g. main .

[0100] S46. Determine if the number of sub-channels 'a' that have been allocated is less than K-x+1; if it is less, select the primary allocation node 'g' located in other theaters and which is similar to the previously selected primary allocation node 'g'. main The corresponding UAV with the smallest gain N max -1 node as matching node g match Otherwise, select a primary allocation node located in another war zone that is similar to the previously selected primary allocation node g. main The corresponding UAV with the smallest gain N max -2 nodes as matching nodes g match .

[0101] S47. Determine the matching node g match If any combat nodes have already been assigned sub-channels, then the primary allocation node g will be assigned a sub-channel. main Matching node g match The nodes are matched into a group, and an unused sub-channel is allocated to each combat node in the group. All nodes are then marked as having an allocated sub-channel status, a = a + 1, and step S44 continues. If a matching node g exists, the matching node g is calculated. match With the primary allocation node g main Total reconnaissance data transmission rate r later And matching node g match The total data transmission rate r of the old group's reconnaissance data previous If r previous >r later Then update the matching node g. match If a combat node has already been assigned a sub-channel, proceed to step S47 again; otherwise, match node g. match Rematch with g main A group is formed, and each combat node in the group is assigned an unused sub-channel, and all of them are marked as having an assigned sub-channel status. match The remaining nodes in the old group are all marked as unassigned sub-channels, and step S44 is executed.

[0102] S5. A multi-UAV assisted communication trajectory optimization method is proposed. Based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model for maximizing the total data transmission rate of ground combat nodes is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, thereby maximizing the total data transmission rate of ground combat nodes.

[0103] Optionally, in S5, the model maximizing the total data transmission rate of ground combat nodes is approximated as a convex model based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method. Solving the approximated optimization model yields the optimal UAV flight trajectory, which may include the following steps S51-S52:

[0104] S51. Based on the sub-channel allocation results, using a successive convex approximation method and introducing relaxation variables, the model maximizing the total data return rate of ground combat nodes is approximated as a convex model. The approximated optimized model is expressed as:

[0105]

[0106] In the formula, S represents the slack variable matrix. This represents the lower bound of the amount of reconnaissance data transmitted back by ground combat node g through sub-channel k of UAV u during sub-period i. This represents the relaxation variables corresponding to UAV u and interference node g′ in sub-period i. For ease of representation, only one interference node is considered in equation (18), and the same applies to multiple interference nodes. This represents the optimal horizontal position of UAV u in sub-period i, obtained during the t-th iteration of the iterative optimization process using the successive convex approximation method.

[0107] S52. Use the convex optimization toolbox to iteratively solve the approximate optimization model.

[0108] In one feasible implementation, during the first iteration The value is the horizontal position of the UAV u in sub-period i within the initial circular trajectory, and in subsequent iterations... The values ​​are all the optimal horizontal positions of the UAV u in sub-period i obtained from the previous iteration.

[0109] Furthermore, to meet battlefield timeliness requirements, the iteration count limit must also be considered. This optimization method sets two iteration stopping conditions: the first condition is when the number of iterations reaches the maximum iteration count t. max Condition 2 is that the rate of change of the total rate of data transmission from ground combat nodes in the two iterations is less than the set threshold η. If either of the two conditions is met, the iteration stops. At this time, the result of the last iteration is the optimized UAV flight trajectory.

[0110] This invention is used in cross-domain collaborative combat scenarios to improve the overall reconnaissance data transmission rate of ground combat nodes while ensuring the fairness of the reconnaissance data transmission rate of each ground combat node.

[0111] This invention associates each UAV with a ground node in a theater of operations through a user association method. This method fully considers the cluster distribution characteristics of ground combat nodes, improving the targeting and efficiency of services. It establishes an optimization model to maximize the total rate of reconnaissance data transmission from ground combat nodes. A multi-UAV assisted communication sub-channel allocation method is proposed, solving the optimization model based on an initial circular trajectory. This reduces interference between ground nodes while ensuring fairness in reconnaissance data transmission among all nodes. A multi-UAV assisted communication trajectory optimization method is also proposed. Based on the sub-channel allocation results, the optimization model is approximated as a convex model and iteratively solved, significantly improving the total rate of reconnaissance data transmission from ground combat nodes and achieving autonomous trajectory planning. This invention effectively enhances cross-domain battlefield collaborative operations and situational awareness capabilities, promoting the development of UAV swarm communication technology.

[0112] This invention aims to propose a joint optimization method for multi-UAV assisted communication sub-channel allocation and flight trajectory to overcome the shortcomings of existing technologies. This method innovatively integrates core technologies such as user association, sub-channel allocation, and trajectory optimization. While significantly improving the overall data transmission rate of reconnaissance data from ground combat nodes, it also ensures fairness in data transmission, fully adapting to the cluster distribution characteristics of ground combat nodes. This provides an efficient and reliable solution for UAV applications in military communication scenarios, effectively enhancing battlefield situational awareness and promoting the development of UAV communication technology.

[0113] Figure 4 This is a block diagram illustrating a multi-UAV communication sub-channel allocation and trajectory joint optimization apparatus according to an exemplary embodiment. The apparatus is used in a multi-UAV communication sub-channel allocation and trajectory joint optimization method. (Refer to...) Figure 4 The device includes an initialization module 310, an association module 320, a construction module 330, a sub-channel allocation module 340, and a flight trajectory optimization module 350. Among them:

[0114] Initialization module 310 is used to initialize ground combat area data and data of multiple UAVs.

[0115] The association module 320 is used to divide the ground combat area into multiple war zones, each war zone including multiple ground combat nodes. It associates each UAV with multiple ground combat nodes in a war zone through a user association method and determines the initial circular trajectory of each UAV.

[0116] Module 330 is used to build a model that maximizes the total rate of reconnaissance data transmission back to ground combat nodes.

[0117] The sub-channel allocation module 340 is used to propose a sub-channel allocation method for multi-UAV assisted communication. Based on the initial circular trajectory and the multi-UAV assisted communication sub-channel allocation method, it solves the model that maximizes the total rate of reconnaissance data backhaul from ground combat nodes, and obtains the sub-channel allocation result.

[0118] The flight trajectory optimization module 350 is used to propose a multi-UAV assisted communication trajectory optimization method. Based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model for maximizing the total rate of reconnaissance data transmission back to the ground combat node is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, thereby maximizing the total rate of reconnaissance data transmission back to the ground combat node.

[0119] This invention aims to propose a joint optimization method for multi-UAV assisted communication sub-channel allocation and flight trajectory to overcome the shortcomings of existing technologies. This method innovatively integrates core technologies such as user association, sub-channel allocation, and trajectory optimization. While significantly improving the overall data transmission rate of reconnaissance data from ground combat nodes, it also ensures fairness in data transmission, fully adapting to the cluster distribution characteristics of ground combat nodes. This provides an efficient and reliable solution for UAV applications in military communication scenarios, effectively enhancing battlefield situational awareness and promoting the development of UAV communication technology.

[0120] Figure 5 This is a schematic diagram of the structure of a multi-UAV communication sub-channel allocation and trajectory joint optimization device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the multi-UAV communication sub-channel allocation and trajectory joint optimization device may include the above-mentioned Figure 4 The multi-UAV communication sub-channel allocation and trajectory joint optimization device shown is optionally included in the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410, which may include a first processor 2001.

[0121] Optionally, the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410 may also include a memory 2002 and a transceiver 2003.

[0122] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0123] The following is combined Figure 5 The components of the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410 are described in detail below:

[0124] The first processor 2001 is the control center of the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0125] Optionally, the first processor 2001 can execute various functions of the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0126] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 5 CPU0 and CPU1 are shown in the diagram.

[0127] In a specific implementation, as one example, the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410 may also include multiple processors, for example... Figure 5 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0128] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0129] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently, and may be connected via the interface circuit of the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410. Figure 5 (Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0130] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0131] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 5 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0132] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently, and it can be connected to the interface circuit of the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410. Figure 5 (Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0133] It should be noted that, Figure 5 The structure of the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0134] Furthermore, the technical effects of the multi-UAV communication sub-channel allocation and trajectory joint optimization device 410 can be referred to the technical effects of the multi-UAV communication sub-channel allocation and trajectory joint optimization method described in the above method embodiments, and will not be repeated here.

[0135] It should be understood that the first processor 2001 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0136] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0137] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0138] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0139] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0140] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0141] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0142] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0143] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0146] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for joint optimization of multi-UAV communication sub-channel allocation and trajectory, characterized in that, The method includes: S1. Initialize ground combat area data and data of multiple drones; S2. Divide the ground combat area into multiple war zones, each war zone includes multiple ground combat nodes. Use user association methods to associate each UAV with multiple ground combat nodes in a war zone and determine the initial circular trajectory of each UAV. S3. Construct a model that maximizes the total rate of reconnaissance data transmission back to ground combat nodes; S4. A method for allocating sub-channels for multi-UAV assisted communication is proposed. Based on the initial circular trajectory and the method for allocating sub-channels for multi-UAV assisted communication, the model for maximizing the total rate of reconnaissance data backhaul from ground combat nodes is solved to obtain the sub-channel allocation results. S5. A multi-UAV assisted communication trajectory optimization method is proposed. Based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model for maximizing the total rate of reconnaissance data backhaul from ground combat nodes is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, thereby maximizing the total rate of reconnaissance data backhaul from ground combat nodes.

2. The method for joint optimization of multi-UAV communication sub-channel allocation and trajectory according to claim 1, characterized in that, S2 includes: The k-means++ clustering algorithm is used to determine the optimal number of theater zones by comparing the average profile coefficients. The ground combat area is then divided into multiple theater zones. In each theater zone, multiple ground combat nodes are provided with uplink relay services for reconnaissance data by a UAV. The initial flight trajectory of each UAV is circular, with the center of the circle being the cluster center of the theater zone and the radius covering half of the ground combat nodes in the theater zone.

3. The method for joint optimization of multi-UAV communication sub-channel allocation and trajectory according to claim 1, characterized in that, The S3 includes: S31. Discretize the total flight cycle of each UAV into multiple sub-cycles. In any sub-cycle, allocate a sub-channel to each ground combat node. Ground combat nodes in the same theater cannot use the same sub-channel. Construct sub-channel allocation restrictions. S32. Limit the minimum reconnaissance data transmission rate for each ground combat node to meet the real-time transmission requirements of reconnaissance data for ground combat nodes in cross-domain integrated operations. S33. The displacement distance of each UAV between any adjacent sub-cycles must meet the physical constraint of the maximum flight speed of the UAV, thus constructing the flight trajectory constraint of the UAV; S34. Based on the sub-channel allocation restrictions, the minimum reconnaissance data return rate restrictions for each ground combat node, and the UAV flight trajectory restrictions, the total reconnaissance data return rate of the ground combat node is obtained, as shown in the following formula (1): In the formula, r total Let A represent the total data transmission rate of ground combat nodes, Q represent the UAV trajectory matrix, i∈τ represent sub-periods, u∈U represent UAVs, g∈G represent ground combat nodes, and k∈κ represent the set of sub-channels. This indicates that during sub-cycle i, UAV u provides reconnaissance data uplink relay service to ground combat node g through sub-channel k. This indicates that during sub-cycle i, UAV u did not provide reconnaissance data uplink relay service to ground combat node g via sub-channel k, where B represents the sub-channel bandwidth. ε represents the signal transmit power of ground combat node g during sub-period i, and ε represents the channel gain at a distance of 1m. q represents the horizontal position of the drone u in sub-period i. g σ represents the horizontal position of the ground combat node g, H represents the flight altitude of the UAV, and σ represents the horizontal position of the ground combat node g. 2 Let g' represent the additive white Gaussian noise power, and g' represent the ground combat node that will cause electromagnetic interference to the ground combat node g. This represents the set of ground combat nodes that use sub-channel k to transmit reconnaissance data back during sub-period i. q represents the signal transmission power of the interfering node g′ during sub-period i. g′ Indicates the horizontal position of the interfering node g′; S35. Based on the total data transmission rate of the ground combat node reconnaissance data, the model for maximizing the total data transmission rate of the ground combat node reconnaissance data is obtained, as shown in the following formula (2):

4. The method for joint optimization of multi-UAV communication sub-channel allocation and trajectory according to claim 1, characterized in that, The step S4 involves solving the model that maximizes the total rate of reconnaissance data return from ground combat nodes based on the initial circular trajectory and the multi-UAV assisted communication sub-channel allocation method, to obtain the sub-channel allocation results, including: S41. Sort the channel gain values ​​between the ground combat nodes and the corresponding UAVs in the theater of operations in descending order to obtain the sorting results. Assign sub-channels to the bottom 10% of the ground combat nodes in the sorting results separately and do not participate in matching and switching. S42. For the multiple sub-channels corresponding to the top 90% of the ground combat nodes in the sorting results, calculate the maximum number of ground combat nodes that can use each sub-channel simultaneously, and obtain the upper limit N of the number of nodes that can simultaneously use the same sub-channel for reconnaissance data transmission. max ; S43. Based on the upper limit N of the number of nodes simultaneously using the same sub-channel for reconnaissance data backhaul. max The top 90% of ground combat nodes in the ranking results are divided into two categories of sub-channels. The number of sub-channels in each category is determined by solving the sub-channel classification equation. The first category of sub-channels is defined as the sub-channels that are N... max Multiple ground combat nodes can use it simultaneously; the second type of subchannel is used by N max -1 ground combat node can be used simultaneously; S44. Determine whether the number of sub-channels 'a' that have been allocated is less than the total number of sub-channels 'K'. If it is less, proceed to step S45. If it is not less, end the sub-channel allocation and output the sub-channel allocation result. S45. Select the ground combat node ranked first in the sorting results that is in an unassigned sub-channel state, and mark it as the primary allocation node g. main ; S46. Determine if the number of sub-channels 'a' that have been allocated is less than K-x+1; if it is less, select a sub-channel in another theater of operations that is compatible with the primary allocation node 'g'. main The corresponding UAV with the smallest channel gain value N max -1 node as matching node g match If it is not less than, then select a node g in other war zones that is the same as the primary allocation node. main The corresponding UAV with the smallest channel gain value N max -2 nodes as matching nodes g match Where x represents the number of second-type sub-channels; the other theaters refer to the ground combat areas excluding the primary allocation node g. main Other theaters outside the current theater; S47. Determine the matching node g match Check if any ground combat nodes have been previously assigned sub-channels; if not, then assign the primary allocation node g. main Matching node g match If the nodes are matched into a group, an unused sub-channel is allocated to each ground combat node in the group, and the ground combat nodes in the group are marked as having an allocated sub-channel. Let a = a + 1, and proceed to step S44. If a match exists, calculate the matching node g. match With the primary allocation node g main Total reconnaissance data transmission rate r later And matching node g match The total data transmission rate r of the old group's reconnaissance data previous Determine r previous >r later Check if the condition is met; if so, update the matching node g. match For ground combat nodes that have already been assigned sub-channels, proceed to step S47; if not, then match node g. match Rematched with the primary allocation node g main A group is formed, and an unused sub-channel is allocated to each ground combat node in the group. The ground combat nodes in the group are then marked as having an allocated sub-channel status. The matching node g is then... match The remaining ground combat nodes in the old group are marked as unassigned sub-channels, and the process proceeds to step S44.

5. The method for joint optimization of multi-UAV communication sub-channel allocation and trajectory according to claim 4, characterized in that, The calculation in S42 shows how many ground combat nodes can use each subchannel simultaneously, as shown in equation (3) below: In the formula, N max G represents the maximum number of nodes that can simultaneously use the same sub-channel to transmit reconnaissance data back. N K represents the total number of ground combat nodes, and K represents the total number of sub-channels; The sub-channel classification equation in S43 is shown in equation (4) below: x(N max -1)+(K-0.1G N -x)N max =0.9G N (4) In the formula, x represents the number of N max - The number of sub-channels used simultaneously by one ground combat node, K-0.1G N -x indicates that N is used. max The number of sub-channels used simultaneously by each ground combat node.

6. The method for joint optimization of multi-UAV communication sub-channel allocation and trajectory according to claim 1, characterized in that, In step S5, based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model for maximizing the total data return rate of ground combat nodes is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, including: S51. Based on the sub-channel allocation results, the successive convex approximation method is used and relaxation variables are introduced to approximate the model that maximizes the total rate of reconnaissance data backhaul from ground combat nodes as a convex model, and the approximate optimized model is obtained. S52. Use the convex optimization toolbox to iteratively solve the approximate optimization model; wherein, the stopping condition of the iteration process is that the number of iterations reaches the maximum number of iterations, or the rate of change of the total rate of return of reconnaissance data from ground combat nodes between two consecutive iterations is less than a preset threshold.

7. The method for joint optimization of multi-UAV communication sub-channel allocation and trajectory according to claim 6, characterized in that, The approximate optimized model is shown in equations (5)-(11) below: In the formula, Q represents the UAV trajectory matrix, S represents the relaxation variable matrix, i∈τ represents the sub-period, u∈U represents the UAV, g∈G represents the ground combat node, and k∈κ represents the set of sub-channels. This indicates that during sub-cycle i, UAV u provides reconnaissance data uplink relay service to ground combat node g through sub-channel k. This indicates that during sub-cycle i, UAV u did not provide reconnaissance data uplink relay service to ground combat node g via sub-channel k. This represents the lower bound of the amount of reconnaissance data transmitted back by ground combat node g through subchannel k of UAV u during sub-period i, where B represents the subchannel bandwidth, and σ represents the data volume. 2 Let g' represent the additive white Gaussian noise power, and g' represent the ground combat node that will cause electromagnetic interference to the ground combat node g. This represents the set of ground combat nodes that use sub-channel k to transmit reconnaissance data back during sub-period i. ε represents the signal transmission power of the interfering node g′ during sub-period i, and ε represents the channel gain at a distance of 1m. R represents the slack variable corresponding to UAV u and interference node g′ in sub-period i. min This represents the minimum reconnaissance data transmission rate threshold for each ground combat node in any sub-cycle. This indicates the horizontal position of the drone u in the last sub-cycle I. T represents the horizontal position of the drone u in sub-period i. i v represents the duration of sub-period i. max This indicates the maximum instantaneous speed of the drone's flight. This represents the optimal horizontal position of UAV u in sub-period i, obtained in the t-th iteration during iterative optimization using the successive convex approximation method. g′ The horizontal position of the interfering node g′ is represented by h, and the flight altitude of the UAV is represented by h. q represents the signal transmission power of ground combat node g during sub-period i. g This indicates the horizontal position of the ground combat node g.

8. A device for joint optimization of multi-UAV communication sub-channel allocation and trajectory, wherein the device is used to implement the method for joint optimization of multi-UAV communication sub-channel allocation and trajectory as described in any one of claims 1-7, characterized in that, The device includes: The initialization module is used to initialize ground combat area data and data from multiple drones. The association module is used to divide the ground combat area into multiple war zones, each of which includes multiple ground combat nodes. The module associates each UAV with multiple ground combat nodes in a war zone through user association methods and determines the initial circular trajectory of each UAV. The building module is used to construct a model that maximizes the total rate of reconnaissance data transmission from ground combat nodes. The sub-channel allocation module is used to propose a sub-channel allocation method for multi-UAV assisted communication. Based on the initial circular trajectory and the multi-UAV assisted communication sub-channel allocation method, the module solves the model that maximizes the total rate of reconnaissance data backhaul from ground combat nodes to obtain the sub-channel allocation result. The flight trajectory optimization module is used to propose a multi-UAV assisted communication trajectory optimization method. Based on the sub-channel allocation results and the multi-UAV assisted communication trajectory optimization method, the model for maximizing the total data transmission rate of ground combat nodes is approximated as a convex model. The approximated optimization model is then solved to obtain the optimal UAV flight trajectory, thereby maximizing the total data transmission rate of ground combat nodes.

9. A device for joint optimization of multi-UAV communication sub-channel allocation and trajectory, characterized in that, The multi-UAV communication sub-channel allocation and trajectory joint optimization device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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