Method and device for setting covert communication based on unmanned aerial vehicle group network and storage medium

Abstract

This application provides a method, apparatus, and storage medium for covert communication setup based on an unmanned aerial vehicle (UAV) swarm network, relating to the field of wireless communication technology. It involves determining the location and number of ground nodes and the number of airborne nodes; establishing a downlink communication channel model from airborne nodes to ground nodes; establishing a covert communication constraint model for UAV base stations based on the detection error probability of eavesdroppers; establishing a network optimization model based on the constraint model, which includes constraints on multiple parameters; and obtaining the setup parameters for the UAV swarm network based on the network optimization model, with the objective of maximizing the lower bound of user communication rate: target association between UAV base stations and users, allocated bandwidth of UAV base stations, transmit power of airborne nodes, and deployment location. This application provides covert communication services to ground users through multiple UAV base stations, improving system communication capacity, and obtaining the optimal network configuration scheme using the network optimization model, thereby improving system communication quality.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method, apparatus and storage medium for setting up covert communication based on unmanned aerial vehicle (UAV) swarm networks. Background Technology

[0002] Due to their advantages such as low manufacturing cost, high deployment flexibility, and high line-of-sight link probability, drone-assisted wireless communication technology has been widely used in many fields and will play an important role in next-generation mobile communication networks. However, the open and broadcast nature of the wireless signal propagation environment makes it easy for drone transmissions to be illegally eavesdropped on or their transmission activities to be illegally detected, posing a risk of information leakage.

[0003] Current covert communication technologies for drones are primarily based on single-drone-single-user and single-drone-multiple-user scenarios. However, in practical applications, the battery power and communication resources of a single drone are often limited, making it difficult to simultaneously provide services to multiple ground users over a wide area, resulting in poor practicality. Drone swarm networks based on multiple drones and multiple users also suffer from inter-group signal interference, leading to a significant decrease in user communication quality or even communication outages, resulting in a poor user experience.

[0004] Furthermore, considering the advantages of using multiple drones to provide services to ground users: firstly, the distance between the drones and ground users can be relatively smaller, thus requiring less power to transmit compared to a single drone while achieving the same communication rate, which is beneficial for communication concealment; secondly, more drones can provide more communication resources, which helps improve service quality. However, covert communication systems based on drone swarm networks face the problem of inter-group signal interference, which often leads to a significant decrease in user communication quality or even communication interruption. Therefore, there is an urgent need to provide a covert communication setup scheme based on drone swarm networks that guarantees communication capacity. Summary of the Invention

[0005] This application provides a method, apparatus, and storage medium for setting up covert communication based on unmanned aerial vehicle (UAV) swarm networks, in order to solve the problems of insufficient communication capacity and poor communication quality.

[0006] Firstly, this application provides a method for setting up covert communication based on a drone swarm network. The drone swarm network includes air nodes and ground nodes. Air nodes include drone base stations and jammers, and ground nodes include users and eavesdroppers. The covert communication setting up method includes: determining the location and quantity information of ground nodes, as well as the quantity information of air nodes; establishing a downlink communication channel model from air nodes to ground nodes based on the location, quantity, and environmental parameters; establishing a covert communication constraint model for drone base stations based on the detection error probability of eavesdroppers under the downlink communication channel model; establishing a network optimization model corresponding to the drone swarm network based on the covert communication constraint model for drone base stations. The network optimization model includes constraints on the following parameters: the lower bound of user communication rate, the detection error probability of eavesdroppers, the sum of the values ​​of the correlation variables from drone base stations to each user, the maximum allocated bandwidth of drone base stations, the transmission power of air nodes, the flight altitude of air nodes, and the values ​​of the correlation variables from drone base stations to each user; and obtaining the setting parameters of the drone swarm network based on the network optimization model, with the goal of maximizing the lower bound of user communication rate. The setting parameters include: the target correlation from drone base stations to users, the allocated bandwidth of drone base stations, the transmission power of air nodes, and the deployment location.

[0007] Optionally, based on a network optimization model, with the goal of maximizing the lower bound of the user communication rate, the setting parameters of the UAV swarm network are obtained, including: iteratively executing steps one through three until the relative growth rate of the lower bound of the user communication rate is less than a threshold or the number of iterations is greater than or equal to a set threshold, to obtain the setting parameters of the UAV swarm network: Step 1: Based on the network optimization model, fix the location information and transmission power of the air nodes to obtain a first optimization model; maximize the lower bound of the minimum user communication rate in the first optimization model to obtain the target association from the UAV base station to the user and the allocated bandwidth of the UAV base station; Step 2: Substitute the target association and allocated bandwidth obtained in the current iteration into the network optimization model, and fix the location information of the air nodes to obtain a second optimization model; maximize the lower bound of the minimum user communication rate in the second optimization model to obtain the transmission power of the air nodes; Step 3: Substitute the target association, allocated bandwidth, and transmission power of the air nodes obtained in the current iteration into the network optimization model to obtain a third optimization model; maximize the lower bound of the minimum user communication rate in the third optimization model to obtain the target location information of the air nodes.

[0008] Optionally, the network optimization model satisfies the following formula:

[0009]

[0010] Where C1 to C7 represent the lower bound of the user communication rate, the detection error probability of the eavesdropper, the sum of the values ​​of the correlation variables from the UAV base station to each user, the maximum allocated bandwidth of the UAV base station, the transmit power of the air node, the flight altitude of the air node, and the constraints on the values ​​of the correlation variables from the UAV base station to each user, respectively; K represents the user set, N represents the eavesdropper set, M represents the UAV base station set, J represents the jammer set, and R... k Let η represent the lower bound of the user communication rate for the k-th user, and let η represent the lower bound threshold of the user communication rate. Let α represent the false detection probability of the nth eavesdropper, ε represent the correct detection probability of the eavesdropper tolerable by the drone swarm network, and α represent the false detection probability of the nth eavesdropper. k,m β represents the value of the correlation variable from the m-th drone base station to the k-th user. k,m p represents the bandwidth allocated from the m-th drone base station to the k-th user, F represents the total bandwidth of the drone swarm network, and p m This represents the transmit power of the m-th drone base station. p represents the adjustable peak transmit power of the jammer. max h represents the maximum transmit power of the air node. min and h max h represents the minimum and maximum flight altitudes of the airborne node, respectively. m h represents the flight altitude of the m-th drone base station. j This represents the flight altitude of the j-th jammer.

[0011] Optionally, the covert communication constraint model for UAV base stations satisfies the following formula:

[0012]

[0013]

[0014] in, p represents the detection error probability of the nth eavesdropper, M represents the number of drone base stations, and p m G represents the transmit power of the m-th UAV base station. m,n This represents the downlink channel gain from the m-th drone base station to the n-th eavesdropper. g represents the adjustable peak transmit power of the jammer. j,n Let ε represent the downlink channel gain from the j-th jammer to the n-th eavesdropper, J represent the number of jammers, ε represent the probability of correct eavesdropper detection tolerated by the UAV swarm network, and N represent the number of eavesdroppers.

[0015] Optionally, the downlink channel gain satisfies the following formula:

[0016]

[0017] g u,v =1 / L u,v

[0018] Among them, L u,v Let represent the downlink channel loss from the u-th air node to the v-th ground node. This represents the probability that the downlink from the u-th air node to the v-th ground node is a line-of-sight link. f represents the probability that the downlink from the u-th air node to the v-th ground node is a non-line-of-sight link. c Indicates the carrier frequency, d u,v Let μ represent the three-dimensional Euclidean distance from the u-th air node to the v-th ground node, c represent the speed of light, and μ represent the distance between the u-th air node and the v-th ground node. LoS and μ NLoS G represents the path loss coefficients for line-of-sight links and non-line-of-sight links, respectively. u,v This represents the downlink channel gain from the u-th air node to the v-th ground node.

[0019] Optional, and They respectively satisfy the following formulas:

[0020]

[0021]

[0022] Where a and b are environment-related modeling parameters, θ u,v Let U represent the elevation angle between the u-th air node and the v-th ground node, where U represents the set of air nodes and V represents the set of ground nodes.

[0023] Optionally, the lower bound of the user communication rate satisfies the following formula:

[0024]

[0025] Among them, R k This represents the lower bound of the user communication rate for the k-th user. This represents the lower bound of the communication rate provided by the m-th drone base station to the k-th user, where M represents the number of drone base stations, and α... k,m β represents the value of the correlation variable from the m-th drone base station to the k-th user. k,m p represents the bandwidth allocated by the m-th drone base station to the k-th user. m G represents the transmit power of the m-th UAV base station. k,m p represents the downlink channel gain from the m-th UAV base station to the k-th user. i G represents the transmit power of the i-th drone base station. k,jThis represents the downlink channel gain from the j-th jammer to the k-th eavesdropper. σ represents the adjustable peak transmit power of the jammer. 2 This represents the power of Gaussian white noise.

[0026] Secondly, this application provides a covert communication setup device based on an unmanned aerial vehicle (UAV) swarm network. The UAV swarm network includes air nodes and ground nodes. The air nodes include UAV base stations and jammers, and the ground nodes include users and eavesdroppers. The covert communication setup device includes: a determining module for determining the location and quantity information of the ground nodes, as well as the quantity information of the air nodes; a first generating module for establishing a downlink communication channel model from the air nodes to the ground nodes based on the location, quantity, and environmental parameters; a second generating module for establishing a covert communication constraint model for the UAV base station based on the detection error probability of the eavesdropper under the downlink communication channel model; and a third generating module for establishing a covert communication constraint model for the UAV base station based on the detection error probability of the eavesdropper under the downlink communication channel model. A constrained communication model for drone base stations is established, along with a network optimization model for the drone swarm network. This model includes constraints on the following parameters: lower bound of user communication rate, error probability of eavesdropper detection, sum of correlation variables from drone base stations to each user, maximum allocated bandwidth of drone base stations, transmit power of air nodes, flight altitude of air nodes, and correlation variables from drone base stations to each user. An output module, based on the network optimization model, aims to maximize the lower bound of user communication rate to obtain the configuration parameters for the drone swarm network. These parameters include: target correlation from drone base stations to users, allocated bandwidth of drone base stations, transmit power of air nodes, and deployment location.

[0027] Thirdly, this application provides an electronic device, including: a memory and a processor; the memory for storing program instructions; and the processor for calling the program instructions to execute the covert communication setup method as provided in any of the first aspects above.

[0028] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the covert communication setup method as provided in any of the first aspects above.

[0029] Fifthly, this application provides a computer program product, including a computer program; when the computer program is executed, it implements the covert communication setting method provided in the first aspect above.

[0030] This application provides a method, apparatus, and storage medium for covert communication setup based on unmanned aerial vehicle (UAV) swarm networks. The method involves determining the location and quantity information of ground nodes, as well as the quantity information of airborne nodes. Based on the location, quantity, and environmental parameters, a downlink communication channel model from airborne nodes to ground nodes is established. A covert communication constraint model for UAV base stations is established based on the detection error probability of an eavesdropper within the downlink communication channel model. A network optimization model for the UAV swarm network is then established based on this constraint model. This network optimization model includes constraints on the following parameters: a lower bound on the user communication rate, the detection error probability of an eavesdropper, the sum of the values ​​of the correlation variables from the UAV base station to each user, the maximum allocated bandwidth of the UAV base station, the transmission power of airborne nodes, the flight altitude of airborne nodes, and the values ​​of the correlation variables from the UAV base station to each user. Based on the network optimization model, and with the objective of maximizing the lower bound on the user communication rate, the setup parameters for the UAV swarm network are obtained. These parameters include: target correlation between the UAV base station and the user, the allocated bandwidth of the UAV base station, the transmission power of the airborne nodes, and their deployment locations. This application proposes a technical solution that uses multiple UAV base stations to provide covert communication services to ground users, which significantly improves the communication capacity of the communication system. By using a network optimization model, the optimal network configuration scheme is obtained, which significantly improves the overall communication quality of the communication system. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0032] Figure 1 This is a schematic diagram illustrating an application scenario provided in the embodiments of this application;

[0033] Figure 2 A flowchart illustrating the covert communication setup method based on an unmanned aerial vehicle (UAV) swarm network provided in this application embodiment;

[0034] Figure 3 This is a schematic diagram of the iterative process of the network optimization model provided in the embodiments of this application;

[0035] Figure 4 This is a schematic diagram of the chromosome coding structure of the genetic algorithm provided in the embodiments of this application;

[0036] Figure 5 A schematic diagram illustrating how the lower bound of the minimum user communication rate varies with the system's tolerable probability of correct detection ε, as provided in the embodiments of this application.

[0037] Figure 6 A schematic diagram illustrating how the lower bound of the minimum user communication rate varies with the number of users K, as provided in the embodiments of this application.

[0038] Figure 7 A schematic diagram illustrating how the lower bound of the minimum user communication rate varies with the number of UAV base stations M, as provided in the embodiments of this application.

[0039] Figure 8 A schematic diagram illustrating how the lower bound of the minimum user communication rate varies with the number of eavesdroppers N, as provided in the embodiments of this application.

[0040] Figure 9 A schematic diagram of the structure of a covert communication device based on an unmanned aerial vehicle (UAV) swarm network provided in an embodiment of this application;

[0041] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0042] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0043] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0044] Traditional technologies for achieving communication security mainly include key technology and physical layer security technology. Key technology ensures the security of transmitted messages by sharing a key between two communication nodes. However, this technology usually causes communication delays because the sending end performs encryption and the receiving end performs decryption. Physical layer security technology ensures the channel quality between the drone and the legitimate user by reasonably deploying the drone's location or planning its trajectory, reasonably allocating communication resources, or generating artificial noise, while minimizing the channel performance between the drone and the eavesdropper, thereby preventing the eavesdropper from accurately demodulating and decoding the messages transmitted by the drone.

[0045] However, the two technologies mentioned above can only solve the problem of "what is being transmitted". In some practical scenarios, simply protecting messages from being eavesdropped is far from enough, because if the drone's transmission behavior is detected by the eavesdropper, it can easily lead to further surveillance or even physical attacks.

[0046] Based on the above, this application aims to provide a covert communication setup method based on a drone swarm network that utilizes a novel covert communication technology, ensuring that the probability of an eavesdropper detecting the drone's transmission behavior is kept at a high level, thereby achieving a higher level of communication security. The covert communication setup method provided in this application will be explained below with reference to specific application scenarios and embodiments.

[0047] Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. For example... Figure 1 As shown, this application scenario 100 involves user 101, eavesdropper 102, drone base station 103, and jammer 104. The transmission link from jammer 104 to eavesdropper 102 is an interference link, the transmission link from drone base station 103 to user 101 is a communication link, and the transmission link from eavesdropper 102 to drone base station 103 is an eavesdropping link.

[0048] In this context, "user 101" refers to a person operating a terminal device, which can be a network-connected device such as a mobile phone, computer, or smart device. In this embodiment, there may be multiple users 101, and different users 101 may require different network resources. "Eavesdropper 102" refers to an entity illegally monitoring the transmission behavior between user 101 and the drone base station 103, posing a threat to communication security. There may be one or more eavesdroppers 102. User 101 and eavesdropper 102 may be located on the ground or in a location close to the ground, but this application does not limit this.

[0049] The drone base station 103 is an aerial base station consisting of base station equipment carried by an aerial drone, providing communication services to user 101. In this embodiment, there are multiple drone base stations 103, i.e., multiple drone base stations 103 composed of a drone swarm. The network communication capabilities formed by different numbers and / or different locations of drone base stations 103 may differ. This application aims to find a better configuration scheme for drone base stations 103. In addition, the jammer 104 is used to mix interference signals / false signals into downlink communication. It is a type of drone jamming device, designed to keep the error probability of the eavesdropper 102 detecting transmission behavior at a high level, thereby achieving a higher level of communication security. In the same application scenario, one or more jammers 104 can be set up.

[0050] Figure 2 This is a flowchart illustrating a method for setting up covert communication based on a drone swarm network, as provided in an embodiment of this application. Figure 2 As shown, the covert communication setup method includes:

[0051] S201: Determine the location and number of ground nodes, as well as the number of air nodes.

[0052] A drone swarm network comprises airborne nodes and ground nodes. Airborne nodes include drone base stations and jammers, while ground nodes include users and eavesdroppers. In one application scenario, the number and location information of users and eavesdroppers, as well as the number of drone base stations and jammers, are first determined.

[0053] Optionally, location information can be constructed using a 3D model. The model selects UAV base stations, jammers, users, and eavesdroppers as components of the 3D system model, and uses a Cartesian coordinate system to represent the spatial location information of each component. Specifically, in a covert communication system based on a UAV swarm network, there are K users, M UAV base stations, N eavesdroppers, and 1 UAV jammer. Let the user index set be K = {1, 2, ..., K}, the UAV base station index set be M = {1, 2, ..., M}, the eavesdropper index set be N = {1, 2, ..., N}, and the UAV jammer index set be j. Then the coordinates of the k-th user can be represented as x. k =(x k ,y k The coordinates of the m-th UAV base station can be represented as q, where q = 0. m =(x m ,y m ,h m The coordinates of the nth listener can be represented as w. n =(x n ,y n The coordinates of the UAV jammer are q, 0). j =(x j ,y j ,h j ).

[0054] S202: Based on location information, quantity information, and environmental parameter information, establish a downlink communication channel model from the air node to the ground node.

[0055] For ease of description, the air nodes in the embodiments are denoted as set U = {M,j}, and the ground nodes are denoted as set V = {K,N}. Generally, the air-to-ground channel (i.e., the transmission channel from the air node to the ground node) includes line-of-sight links and non-line-of-sight links. A line-of-sight link is a point-to-point straight-line transmission link, while a non-line-of-sight link is a transmission link without direct relative links.

[0056] Optionally, the probability that the air-to-ground channel is a line-of-sight link can be determined according to the following model:

[0057]

[0058] Where a and b are environment-related modeling parameters, θ u,vLet represent the elevation angle between the u-th air node and the v-th ground node, where U represents the set of air nodes and V represents the set of ground nodes. Therefore, the probability that the downlink from the u-th air node to the v-th ground node is a non-line-of-sight link is:

[0059]

[0060] Therefore, the channel loss between the u-th air node and the v-th ground node can be expressed as:

[0061]

[0062] Among them, L u,v Let represent the downlink channel loss from the u-th air node to the v-th ground node. This represents the probability that the downlink from the u-th air node to the v-th ground node is a line-of-sight link. f represents the probability that the downlink from the u-th air node to the v-th ground node is a non-line-of-sight link. c Indicates the carrier frequency, d u,v Let represent the three-dimensional Euclidean distance from the u-th air node to the v-th ground node. c represents the speed of light, μ LoS and μ NLoS Let represent the path loss coefficients for line-of-sight links and non-line-of-sight links, respectively. Then, let g be the channel gain g of the downlink channel gain from the u-th air node to the v-th ground node. u,v This can be expressed as:

[0063] g u,v =1 / L u,v Formula 4

[0064] S203: Based on the detection error probability of the eavesdropper in the downlink communication channel model, establish a covert communication constraint model for UAV base stations.

[0065] For example, the downlink communication channel model provided in the above embodiments is used to obtain the detection error probability of the eavesdropper. Specifically, in covert communication, the nth eavesdropper determines the detection error probability based on the signal sample y it observes. n [i], i = 1, 2, ..., L determines whether the drone base station is providing services to ground users, where y n [i] represents the signal received by the nth listener on the i-th channel, which can be represented as:

[0066]

[0067] Here, H0 and H1 represent the drone base station remaining silent and the drone base station transmitting data, respectively. m [i] and x j[i] represents the transmission message symbol of the m-th UAV base station and the jammer j (i.e., the j-th jammer; in this embodiment, one jammer is selected, so it is simply referred to as jammer j), and the two satisfy E[|x m [i]| 2 ]=1,E[|x j [i]| 2 ]=1,n[i]~CN(0,σ 2 ) represents the power σ 2 Gaussian white noise, p m p represents the transmit power of the m-th UAV base station. j Let j be the transmission power of jammer j, and it is in the range The upper part follows a uniform distribution, where g represents the adjustable peak transmit power of the jammer. j,n Let g represent the downlink channel gain from the j-th jammer to the n-th eavesdropper. m,n This represents the downlink channel gain from the m-th drone base station to the n-th eavesdropper.

[0068] For example, if the eavesdropper makes a decision based on the observed signal sample using an energy detection method, then:

[0069]

[0070] Among them, T n D1 and D0 represent the average received power received by the nth listener, respectively, and represent the binary decision made by the listener regarding whether the drone base station is serving ground users. To determine the detection threshold. Assuming the number of channels in use is sufficiently large, i.e., L→∞, then T n It can be simplified to:

[0071]

[0072] The false alarm probability of the nth listener is used as The false alarm probability is represented by ψ. n =Pr{D0|H1} represents the total detection error probability of the nth listener, which can be expressed as: It is indicated that, by utilizing the uniform distribution characteristic of the jammer's transmission power, the covert communication constraint model of the UAV base station satisfies the following formula, which can be expressed as:

[0073]

[0074] in, Listener n sets the optimal detection threshold This can achieve the minimum total detection error probability. This can be expressed as:

[0075]

[0076] Therefore, the constraint model based on covert communication using UAV base stations can be expressed as:

[0077]

[0078] in, p represents the detection error probability of the nth eavesdropper, M represents the number of drone base stations, and p m G represents the transmit power of the m-th UAV base station. m,n This represents the downlink channel gain from the m-th drone base station to the n-th eavesdropper. g represents the adjustable peak transmit power of the jammer. j,n Let ε represent the downlink channel gain from the j-th jammer to the n-th eavesdropper, J represent the number of jammers, ε represent the probability of correct eavesdropper detection tolerated by the UAV swarm network, and N represent the number of eavesdroppers.

[0079] S204: Based on the covert communication constraint model of UAV base stations, establish a network optimization model corresponding to the UAV swarm network. The network optimization model includes the following parameter constraints: lower bound of user communication rate, detection error probability of eavesdroppers, sum of the values ​​of the correlation variables from UAV base stations to each user, maximum allocated bandwidth of UAV base stations, transmission power of air nodes, flight altitude of air nodes, and values ​​of the correlation variables from UAV base stations to each user.

[0080] Based on the covert communication constraint model of the UAV base station obtained in S203 (Formula 10 in this embodiment), a network optimization model corresponding to the UAV swarm network is established. In the network optimization model, constraints are established for the above-mentioned parameters. For example, the lower bound of the user communication rate needs to be less than or equal to the communication rate of each user; the detection error probability of the eavesdropper needs to be determined based on the correct detection probability ε of the eavesdropper tolerable by the UAV swarm network, and the value of ε can be set according to the actual application scenario; the sum of the values ​​of the correlation variables from the UAV base station to each user needs to meet the set value; the maximum allocated bandwidth of the UAV base station needs to be less than the total spectrum bandwidth of the covert communication system; the transmission power of the air nodes (UAV base station and jammer) needs to be between their own upper and lower limit transmission power ranges; the flight altitude of the air nodes needs to be between their allowed upper and lower limit flight altitude ranges; and the values ​​of the correlation variables from the UAV base station to each user are limited to specific values.

[0081] S205: Based on the network optimization model, with the goal of maximizing the lower bound of user communication rate, the setting parameters of the UAV swarm network are obtained. The setting parameters include: target association between UAV base stations and users, allocated bandwidth of UAV base stations, transmission power of air nodes, and deployment location.

[0082] The above-mentioned setting parameters for the UAV swarm network are obtained using a network optimization model, and the UAV swarm network is configured according to these parameters for this application scenario. Among these, the target association from the UAV base station to the user refers to establishing a communication link between a UAV and a user. For example, if 1 or 0 represents whether the link is established, then: if the target association is 1, it indicates that the UAV serves the user; if the target association is 0, it indicates that the UAV does not serve the user, and no link is established between them.

[0083] Reasonable bandwidth allocation ensures normal data transmission within limited bandwidth resources, preventing network congestion due to excessive data volume. When deriving these settings based on network optimization models, the goal is to maximize the lower bound of user communication rates, aiming to improve the overall information transmission quality of the UAV swarm network to the greatest extent possible.

[0084] This application embodiment determines the location and quantity information of ground nodes, as well as the quantity information of air nodes; based on the location, quantity, and environmental parameters, it establishes a downlink communication channel model from air nodes to ground nodes; based on the detection error probability of a listener under the downlink communication channel model, it establishes a covert communication constraint model for UAV base stations; based on the covert communication constraint model for UAV base stations, it establishes a network optimization model corresponding to the UAV swarm network. The network optimization model includes constraints on the following parameters: the lower bound of user communication rate, the detection error probability of the listener, the sum of the values ​​of the correlation variables from the UAV base station to each user, the maximum allocated bandwidth of the UAV base station, the transmit power of air nodes, the flight altitude of air nodes, and the values ​​of the correlation variables from the UAV base station to each user; based on the network optimization model, with the goal of maximizing the lower bound of user communication rate, it obtains the setting parameters of the UAV swarm network, including: target correlation from UAV base stations to users, allocated bandwidth of UAV base stations, transmit power of air nodes, and deployment location. This application embodiment proposes a technical solution that uses multiple UAV base stations to provide covert communication services to ground users, significantly improving the communication capacity of the communication system, and obtaining the optimal network configuration scheme using the network optimization model, significantly improving the overall communication quality of the communication system.

[0085] Based on the above embodiments, optionally, the network optimization model satisfies the following formula:

[0086]

[0087] Where C1 to C7 represent the lower bound of the user communication rate, the detection error probability of the eavesdropper, the sum of the values ​​of the correlation variables from the UAV base station to each user, the maximum allocated bandwidth of the UAV base station, the transmit power of the air node, the flight altitude of the air node, and the constraints on the values ​​of the correlation variables from the UAV base station to each user, respectively; K represents the user set, N represents the eavesdropper set, M represents the UAV base station set, J represents the jammer set, and R... k Let η represent the lower bound of the user communication rate for the k-th user, and let η represent the lower bound threshold of the user communication rate. Let α represent the false detection probability of the nth eavesdropper, ε represent the correct detection probability of the eavesdropper tolerable by the drone swarm network, and α represent the false detection probability of the nth eavesdropper. k,m β represents the value of the correlation variable from the m-th drone base station to the k-th user. k,m p represents the bandwidth allocated from the m-th drone base station to the k-th user, F represents the total bandwidth of the drone swarm network, and p m This represents the transmit power of the m-th drone base station. p represents the adjustable peak transmit power of the jammer. max h represents the maximum transmit power of the air node. min and h max h represents the minimum and maximum flight altitudes of the airborne node, respectively. m h represents the flight altitude of the m-th drone base station. j This represents the flight altitude of the j-th jammer.

[0088] Optionally, when determining the lower bound of the user communication rate, the total spectrum bandwidth of the considered covert communication system based on the UAV swarm network is set to F, and the transmit power of the UAV base station and the jammer is uniformly distributed on this bandwidth, introducing the variable β. k,m Let represent the bandwidth allocated by the m-th drone base station to the k-th user. Then, the signal-to-interference-plus-noise ratio (SIR) of the k-th user when served by the m-th drone base station can be calculated as follows:

[0089]

[0090] Introducing 0-1 variables α k,m This represents the service relationship between a user and a drone base station. When the k-th user is served by the m-th drone base station, α... k,m =1, otherwise 0. Therefore, the communication rate expression for the k-th user served by the m-th drone base station can be calculated as:

[0091]

[0092] In part (a), the result is derived from Jensen's inequality theorem. Therefore, the lower bound of the communication rate of the k-th user satisfies the following formula:

[0093]

[0094] Among them, R k This represents the lower bound of the user communication rate for the k-th user. This represents the lower bound of the communication rate provided by the m-th drone base station to the k-th user, where M represents the number of drone base stations, and α... k,m β represents the value of the correlation variable from the m-th drone base station to the k-th user. k,m p represents the bandwidth allocated by the m-th drone base station to the k-th user. m G represents the transmit power of the m-th UAV base station. k,m p represents the downlink channel gain from the m-th UAV base station to the k-th user. i Let g represent the transmit power of the i-th jammer. k,j This represents the downlink channel gain from the j-th jammer to the k-th eavesdropper. σ represents the adjustable peak transmit power of the jammer. 2 This represents the power of Gaussian white noise.

[0095] Optionally, based on a network optimization model, with the goal of maximizing the lower bound of the user communication rate, the setting parameters of the UAV swarm network are obtained, including: iteratively executing the following first to third steps until the relative growth rate of the lower bound of the user communication rate is less than a threshold value or the number of iterations is greater than or equal to a set number threshold, thereby obtaining the setting parameters of the UAV swarm network. Figure 3 This is a schematic diagram of the iterative process of the network optimization model provided in this application embodiment. One iteration sequentially outputs: target association from the UAV base station to the user, allocated bandwidth of the UAV base station → transmit power of the air node → three-dimensional position (i.e., deployment location) of the air node. Specifically, taking the network optimization model shown in Formula 11 as an example, the first to third steps of this embodiment include:

[0096] The first step is to obtain the first optimization model by fixing the location information and transmission power of the air nodes based on the network optimization model; and to maximize the lower bound of the minimum user communication rate in the first optimization model to obtain the target association between the UAV base station and the user and the allocated bandwidth of the UAV base station.

[0097] For example, in the t-th iteration, the positions of the drone base station and the jammer are fixed. Transmission power of drone base stations and jammers The optimization problem shown in Equation 11 can be transformed into the following sub-problem of user association and bandwidth allocation:

[0098]

[0099] in, It is a constant.

[0100] In the first iteration, the location information and transmission power of the aerial nodes can be fixed to initial values. For example, the initial location of the drone base station can be set to a certain height directly above the cluster center of the ground users, and the transmission power can be set to the average transmission power.

[0101] The first step may specifically include the following steps 1.1 to 1.2:

[0102] Step 1.1: We note that the user association variable and bandwidth allocation variable are highly coupled in the above optimization problem. However, after determining the user association, the bandwidth allocation problem for each UAV base station becomes independent. Therefore, let K be the set of user sequence indexes served by the m-th UAV base station. m ={1,2,...,K m Then, the bandwidth allocation problem for the m-th drone base station is transformed into the following problem:

[0103]

[0104] The optimal closed-form solution to the above problem is:

[0105]

[0106]

[0107] Therefore, the lower bound of the optimal minimum user communication rate is:

[0108] Step 1.2: An exemplary method using a genetic algorithm to optimize the association between drone base stations and users. Figure 4 This is a schematic diagram of the chromosome coding structure of the genetic algorithm provided in the embodiments of this application, as shown below. Figure 4 The diagram illustrates a possible chromosome encoding scheme for an application scenario involving 8 ground users and 3 drone base stations. The gene index numbers on the chromosome (1, 2, or 3 in the boxes shown) correspond to the ground user numbers (user 1, ..., user 8 in the diagram), while the specific gene value represents the drone base station number associated with that ground user (drone base station 1, ..., drone base station 3 in the diagram). The specific sub-steps are as follows:

[0109] (1) The solution space of the model is represented by an integer encoding sequence. The length of the chromosome is the number of users K. Each gene on the chromosome represents the index number of the drone base station associated with the user corresponding to the gene sequence number.

[0110] (2) Generate a specified number of initial feasible solutions. In particular, in order to ensure the convergence of the iterative optimization algorithm, the first chromosome in the population is initialized to the user-associated solution obtained in the (t-1)th iteration, and the remaining chromosomes in the population are randomly generated.

[0111] (3) Based on the objective function of the model, the optimal user minimum communication rate lower bound in step 1.1 is used as the fitness evaluation function, and the fitness of each chromosome in the population is evaluated.

[0112] (4) Check if the number of iterations has reached the set upper limit. If it has, output the association result between the UAV base station and the user and the corresponding fitness value of the best chromosome in the population. If it has not reached the upper limit, continue to perform subsequent operations.

[0113] (5) An improved binary tournament selection factor is used to select parent chromosomes from the population for evolutionary operations. Unlike the traditional tournament selection factor, which directly selects chromosomes with higher fitness values ​​as parents, the improved binary tournament selection factor is characterized by selecting chromosomes with lower fitness values ​​as parents with a certain probability when selecting between two chromosomes. This has the advantage of increasing population diversity and enhancing the ability to seek the best.

[0114] (6) Based on the crossover probability, select two crossover factors to perform segment crossover on the chromosomes in the parent generation to generate new chromosomes as offspring; based on the mutation probability, select a single mutation factor to mutate the offspring chromosomes generated by the crossover operation to generate new chromosomes as offspring.

[0115] (7) Return to step (3) to evaluate the fitness value.

[0116] The second step is to substitute the target association and allocated bandwidth obtained in the current iteration into the network optimization model, and fix the location information of the air nodes to obtain the second optimization model; maximize the lower bound of the minimum user communication rate in the second optimization model to obtain the transmit power of the air nodes.

[0117] After obtaining the user association and bandwidth allocation results for the t-th iteration in step 1.1, the association between the fixed UAV base station and the user is then determined. (i.e., target association from drone base station to user), bandwidth allocation Deployment locations of drone base stations and jammers The optimization problem shown in step 11 is transformed into the following power control subproblem (i.e., the transmit power subproblem):

[0118]

[0119] The aforementioned power control subproblem can be solved approximately using a continuous convex approximation method. For a given local point... Its approximate problem is:

[0120]

[0121] in: The channel gain constant after fixing the UAV base station and the jammer can be determined according to the model shown in Formula 4.

[0122]

[0123] The above approximate problem is a convex problem, which can be solved using a solver.

[0124] The third step involves substituting the target association, allocated bandwidth, and transmit power of the air node obtained in the current iteration into the network optimization model to obtain the third optimization model; maximizing the lower bound of the minimum user communication rate in the third optimization model to obtain the target location information of the air node.

[0125] After obtaining the target user association and allocated bandwidth and transmission power for the t-th iteration in the first and second steps, the fixed UAV base station is associated with the user. Allocate bandwidth and transmission power The optimization problem shown in Equation 11 is transformed into the following sub-problems: the deployment of UAV base stations and UAV jammer locations:

[0126]

[0127] For example, the particle swarm optimization algorithm is used to solve the above sub-problem of the three-dimensional positioning of UAV base stations and UAV jammers, which specifically includes the following sub-steps:

[0128] (1) Initialize a population containing a specified number of particles. Each particle has two characteristics: position and velocity. The position of each particle represents a three-dimensional deployment scheme of UAV base station and UAV jammer. In particular, in order to ensure the convergence of the iterative optimization algorithm, the position of the first particle in the population is initialized to the UAV position deployment solution obtained in the (t-1)th iteration. The positions of the remaining particles in the population are randomly generated, and the velocities of all particles are initialized to 0.

[0129] (2) Based on the objective function and constraints of the model, design a fitness evaluation function and evaluate the fitness of each particle position in the population;

[0130] (3) Check if the number of iterations has reached the set upper limit. If it has, output the deployment solution of the UAV base station and UAV jammer corresponding to the optimal particle position in the population and the corresponding fitness value. If it has not reached the upper limit, continue to perform subsequent operations.

[0131] (4) Update the global extreme value and the individual extreme value based on the fitness assessment results of step (3);

[0132] (5) Update the individual particle velocity based on the particle velocity corresponding to the global extreme value and the particle velocity corresponding to the individual extreme value, and further update the individual particle position;

[0133] (6) Return to step (3) to evaluate the fitness value.

[0134] By iteratively optimizing the target association between the UAV base station and the user, the allocated bandwidth of the UAV base station, the transmission power of the air node, and the deployment location through the first to third steps, the process can be stopped when the relative growth of the lower bound η of the minimum user communication capacity is less than a certain threshold value, or when the number of iterations t exceeds the maximum number of iterations. Finally, the suboptimal target association between the UAV base station and the user, the allocated bandwidth of the UAV base station, the transmission power of the air node, and the deployment location are obtained.

[0135] In one specific embodiment, p max =1w,h min =200m, h max =500m, K=20, M=3, N=2. This application provides three other methods for comparison with the embodiments of this application: "Based Scheme 1", "Based Scheme 2" and "Based Scheme 3". Among them, "Baseline Scheme 1" uses K-means clustering to determine the association between UAV base stations and users. Each UAV base station allocates bandwidth equally to the users it serves. Then, it uses the second and third steps to iteratively optimize the transmission power and the deployment position of the air nodes until convergence. "Baseline Scheme 2" first uses K-means clustering to cluster users. Then, it deploys the horizontal position of the UAV base station at each cluster center and the horizontal position of the jammer at the geometric center of the listener. The height of the UAV base station and the jammer is set at a fixed height of 350 meters. Finally, it uses the second and third steps to iteratively optimize the association between the UAV base station and users and the transmission power until convergence. "Baseline Scheme 3" uses K-means clustering to determine the association between UAV base stations and users and the horizontal position of the UAV base station. The horizontal position of the UAV jammer is deployed at the geometric center of the listener. Then, through the second step, it uses exhaustive search at 5m intervals to iteratively optimize the power control and altitude position of the UAV base station and the jammer until convergence.

[0136] Figure 5 A schematic diagram illustrating the variation of the lower bound of the minimum user communication rate as a function of the system's tolerable correct detection probability ε, provided for embodiments of this application, shows an embodiment of this application (corresponding to...). Figure 5 The comparison between the proposed method and "Benchmark Scheme 1", "Benchmark Scheme 2", and "Benchmark Scheme 3" is presented. Figure 5As can be seen, the lower bound of the minimum user communication rate under the correct detection probability that any system can tolerate is better than the other three comparison methods in the embodiments of this application.

[0137] Figure 6 A schematic diagram illustrating the variation of the lower bound of the minimum user communication rate with the number of users K provided in this application embodiment shows the embodiment of this application (corresponding to...). Figure 6 The comparison between the proposed method and "Benchmark Scheme 1", "Benchmark Scheme 2", and "Benchmark Scheme 3" is presented. Figure 6 As can be seen, the embodiment of this application has a better lower bound on the minimum user communication rate than the other three comparison methods for any number of users.

[0138] Figure 7 A schematic diagram illustrating the variation of the lower bound of the minimum user communication rate with the number of UAV base stations M, as provided in the embodiments of this application, shows the embodiments of this application (corresponding to...). Figure 7 The comparison between the proposed method and "Benchmark Scheme 1", "Benchmark Scheme 2", and "Benchmark Scheme 3" is presented. Figure 7 As can be seen, the embodiment of this application has a better lower bound for the minimum user communication rate than the other three comparison methods under any number of UAV base stations.

[0139] Figure 8 This diagram illustrates how the lower bound of the minimum user communication rate varies with the number of eavesdroppers N, as provided in embodiments of this application. Figure 8 The embodiments of this application are shown in the figure (corresponding to...). Figure 8 The comparison between the proposed method and "Benchmark Scheme 1", "Benchmark Scheme 2", and "Benchmark Scheme 3" is presented. Figure 8 As can be seen, the embodiment of this application has a better lower bound on the minimum user communication rate than the other three comparison methods for any number of eavesdroppers.

[0140] according to Figures 5-8 Simulation comparisons of different systems' tolerance for correct detection probabilities, different numbers of users, different numbers of drone base stations, and different numbers of eavesdroppers show that the embodiments of this application have good performance and stable behavior in improving the lower bound of the minimum user communication rate problem.

[0141] The above embodiments provide a detailed description of the covert communication setup method based on UAV swarm networks provided in this application. The covert communication setup device, electronic device, storage medium, and program product based on UAV swarm networks provided in the embodiments of this application will be explained in detail below.

[0142] Figure 9This is a schematic diagram of a covert communication device based on a drone swarm network provided in an embodiment of this application. The drone swarm network includes air nodes and ground nodes. The air nodes include drone base stations and jammers, while the ground nodes include users and eavesdroppers. Figure 9 As shown, the covert communication device 900 based on a drone swarm network includes:

[0143] The determination module 901 is used to determine the location and quantity information of ground nodes, as well as the quantity information of air nodes;

[0144] The first generation module 902 is used to establish a downlink communication channel model from the air node to the ground node based on the location information, quantity information and environmental parameter information.

[0145] The second generation module 903 is used to establish a covert communication constraint model for UAV base stations based on the detection error probability of the eavesdropper under the downlink communication channel model.

[0146] The third generation module 904 is used to establish a network optimization model corresponding to the UAV swarm network based on the UAV base station covert communication constraint model. The network optimization model includes the following parameter constraints: lower bound of user communication rate, detection error probability of eavesdropper, sum of the values ​​of the correlation variables from UAV base station to each user, maximum allocated bandwidth of UAV base station, transmission power of air node, flight altitude of air node and values ​​of the correlation variables from UAV base station to each user.

[0147] The output module 905 is used to obtain the setting parameters of the UAV swarm network based on the network optimization model, with the goal of maximizing the lower bound of the user communication rate. The setting parameters include: target association between UAV base stations and users, allocated bandwidth of UAV base stations, transmission power of air nodes, and deployment location.

[0148] Optionally, the output module 905 can be used to iteratively execute steps one through three until the relative growth rate of the lower bound of the user communication rate is less than a threshold or the number of iterations is greater than or equal to a set threshold, to obtain the setting parameters of the UAV swarm network: Step 1: Based on the network optimization model, fix the location information and transmission power of the air nodes to obtain the first optimization model; maximize the lower bound of the minimum user communication rate in the first optimization model to obtain the target association from the UAV base station to the user and the allocated bandwidth of the UAV base station; Step 2: Substitute the target association and allocated bandwidth obtained in the current iteration into the network optimization model, and fix the location information of the air nodes to obtain the second optimization model; maximize the lower bound of the minimum user communication rate in the second optimization model to obtain the transmission power of the air nodes; Step 3: Substitute the target association, allocated bandwidth, and transmission power of the air nodes obtained in the current iteration into the network optimization model to obtain the third optimization model; maximize the lower bound of the minimum user communication rate in the third optimization model to obtain the target location information of the air nodes.

[0149] Optionally, the network optimization model satisfies the following formula:

[0150]

[0151] Where C1 to C7 represent the lower bound of the user communication rate, the detection error probability of the eavesdropper, the sum of the values ​​of the correlation variables from the UAV base station to each user, the maximum allocated bandwidth of the UAV base station, the transmit power of the air node, the flight altitude of the air node, and the constraints on the values ​​of the correlation variables from the UAV base station to each user, respectively; K represents the user set, N represents the eavesdropper set, M represents the UAV base station set, J represents the jammer set, and R... k Let η represent the lower bound of the user communication rate for the k-th user, and let η represent the lower bound threshold of the user communication rate. Let α represent the false detection probability of the nth eavesdropper, ε represent the correct detection probability of the eavesdropper tolerable by the drone swarm network, and α represent the false detection probability of the nth eavesdropper. k,m β represents the value of the correlation variable from the m-th drone base station to the k-th user. k,m p represents the bandwidth allocated from the m-th drone base station to the k-th user, F represents the total bandwidth of the drone swarm network, and p m This represents the transmit power of the m-th drone base station. p represents the adjustable peak transmit power of the jammer. max h represents the maximum transmit power of the air node. min and h max h represents the minimum and maximum flight altitudes of the airborne node, respectively. m h represents the flight altitude of the m-th drone base station. j This represents the flight altitude of the j-th jammer.

[0152] Optionally, the covert communication constraint model for UAV base stations satisfies the following formula:

[0153]

[0154]

[0155] in, p represents the detection error probability of the nth eavesdropper, M represents the number of drone base stations, and p m G represents the transmit power of the m-th UAV base station. m,n This represents the downlink channel gain from the m-th drone base station to the n-th eavesdropper. g represents the adjustable peak transmit power of the jammer. j,n Let ε represent the downlink channel gain from the j-th jammer to the n-th eavesdropper, J represent the number of jammers, ε represent the probability of correct eavesdropper detection tolerated by the UAV swarm network, and N represent the number of eavesdroppers.

[0156] Optionally, the downlink channel gain satisfies the following formula:

[0157]

[0158] g u,v =1 / L u,v

[0159] Among them, L u,v Let represent the downlink channel loss from the u-th air node to the v-th ground node. This represents the probability that the downlink from the u-th air node to the v-th ground node is a line-of-sight link. f represents the probability that the downlink from the u-th air node to the v-th ground node is a non-line-of-sight link. c Indicates the carrier frequency, d u,v Let μ represent the three-dimensional Euclidean distance from the u-th air node to the v-th ground node, c represent the speed of light, and μ represent the distance between the u-th air node and the v-th ground node. LoS and μ NLoS G represents the path loss coefficients for line-of-sight links and non-line-of-sight links, respectively. u,v This represents the downlink channel gain from the u-th air node to the v-th ground node.

[0160] Optional, and They respectively satisfy the following formulas:

[0161]

[0162]

[0163] Where a and b are environment-related modeling parameters, θ u,v Let U represent the elevation angle between the u-th air node and the v-th ground node, where U represents the set of air nodes and V represents the set of ground nodes.

[0164] Optionally, the lower bound of the user communication rate satisfies the following formula:

[0165]

[0166] Among them, R k This represents the lower bound of the user communication rate for the k-th user. This represents the lower bound of the communication rate provided by the m-th drone base station to the k-th user, where M represents the number of drone base stations, and α... k,m β represents the value of the correlation variable from the m-th drone base station to the k-th user. k,m p represents the bandwidth allocated by the m-th drone base station to the k-th user. m G represents the transmit power of the m-th UAV base station. k,m p represents the downlink channel gain from the m-th UAV base station to the k-th user. i G represents the transmit power of the i-th drone base station. k,j This represents the downlink channel gain from the j-th jammer to the k-th eavesdropper. σ represents the adjustable peak transmit power of the jammer. 2 This represents the power of Gaussian white noise.

[0167] The apparatus provided in this application embodiment can be used to execute the above-described method for processing tilted text images. Its implementation and technical effects are similar, and will not be described again here.

[0168] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 10 As shown, the electronic device 1000 includes:

[0169] Processor 1001, memory 1002, communication interface 1003 and system bus 1004.

[0170] The memory 1002 and the communication interface 1003 are connected to the processor 1001 via the system bus 1004 and communicate with each other. The memory 1002 is used to store computer execution instructions, the communication interface 1003 is used to communicate with other devices, and the processor 1001 is used to execute the computer execution instructions to execute the covert communication setting method as described in the above method embodiment.

[0171] Specifically, processor 1001 may include one or more processing units. For example, processor 1001 may be a CPU, a Digital Signal Processing (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0172] The memory 1002 can be used to store program instructions. The memory 1002 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback), etc. The data storage area may store data created during the use of the electronic device 1000 (such as audio data), etc. Furthermore, the memory 1002 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, Universal Flash Storage (UFS), etc. The processor 1001 executes various functional applications and data processing of the electronic device 1000 by running the program instructions stored in the memory 1002.

[0173] The communication interface 1003 can provide solutions for wireless communication, including 2G / 3G / 4G / 110G, applied to the electronic device 1000. The communication interface 1003 can receive electromagnetic waves via an antenna, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The communication interface 1003 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via the antenna. In some embodiments, at least some functional modules of the communication interface 1003 can be housed in the processor 1001. In some embodiments, at least some functional modules of the communication interface 1003 and at least some modules of the processor 1001 can be housed in the same device.

[0174] System bus 1004 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus 1004 can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0175] It should be noted that the number of memory 1002 and processor 1001 is not limited in this embodiment; there can be one or more of them. Figure 10 The illustration shows an example; the memory 1002 and the processor 1001 can be connected via wired or wireless means, such as a bus connection. In practical applications, the electronic device 1000 can be various forms of computers or mobile terminals. Computers include, for example, laptops, desktop computers, workbenches, servers, blade servers, mainframe computers, etc.; mobile terminals include, for example, personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0176] The electronic device in this embodiment can be used to execute the technical solutions in the above method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0177] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the covert communication setup method in the above-described method embodiments.

[0178] This application also provides a computer program product, including a computer program; when the computer program is executed, it implements the covert communication setting method as described in the above method embodiments.

[0179] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0180] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for setting up covert communication based on unmanned aerial vehicle (UAV) swarm networks, characterized in that, The drone swarm network includes air nodes and ground nodes. The air nodes include drone base stations and jammers. The ground nodes include users and eavesdroppers. The covert communication setup method includes: Determine the location and quantity information of the ground nodes, as well as the quantity information of the air nodes; Based on the location information, quantity information, and environmental parameter information, a downlink communication channel model from the air node to the ground node is established; Based on the detection error probability of the eavesdropper under the downlink communication channel model, a covert communication constraint model for the UAV base station is established; wherein, the covert communication constraint model for the UAV base station satisfies the following formula: ， ， in, Indicates the first The probability of a single listener making a detection error, where M represents the number of drone base stations. Indicates the first The transmission power of each drone base station, Indicates the first The drone base station to the first Downlink channel gain of the listener This indicates the adjustable peak transmit power of the jammer. Indicates the first The jammer to the first The downlink channel gain of each eavesdropper, where J represents the number of jammers. This represents the probability that a drone swarm network can tolerate a correctly detected eavesdropper, where N represents the number of eavesdroppers. The downlink channel gain satisfies the following formula: in, Indicates the first The first aerial node to the first Downlink channel loss of each ground node Indicates the first The first aerial node to the first The probability that the downlink of a ground node is a line-of-sight link. Indicates the first The first aerial node to the first The probability that the downlink of a ground node is a non-line-of-sight link. Indicates the carrier frequency. Indicates the first The first aerial node to the first The three-dimensional Euclidean distance between each ground node Represents the speed of light. and These represent the path loss coefficients for line-of-sight links and non-line-of-sight links, respectively. Indicates the first The first aerial node to the first Downlink channel gain of each ground node and They respectively satisfy the following formulas: ， ， in, and These are environmentally relevant modeling parameters. Indicates the first The first aerial node and the first The elevation angle between ground nodes Represents the set of nodes in the air. Represents the set of ground nodes; Based on the covert communication constraint model of the UAV base station, a network optimization model corresponding to the UAV swarm network is established. The network optimization model includes the following parameter constraints: lower bound of user communication rate, detection error probability of eavesdropper, sum of the values ​​of the correlation variables from the UAV base station to each user, maximum allocated bandwidth of the UAV base station, transmission power of the air node, flight altitude of the air node, and values ​​of the correlation variables from the UAV base station to each user. The first to third steps are executed iteratively until the relative growth rate of the lower bound of the user communication rate is less than a threshold or the number of iterations is greater than or equal to a set threshold, thus obtaining the setting parameters of the UAV swarm network: First step: Based on the network optimization model, the location information and transmission power of the airborne nodes are fixed to obtain a first optimization model; the lower bound of the minimum user communication rate in the first optimization model is maximized to obtain the target association from the UAV base station to the user and the allocated bandwidth of the UAV base station; Second step: The target association and allocated bandwidth obtained in the current iteration are substituted into the network optimization model, and the location information of the airborne nodes is fixed. A second optimization model is obtained; the lower bound of the minimum user communication rate in the second optimization model is maximized to obtain the transmit power of the air node; in the third step, the target association, the allocated bandwidth, and the transmit power of the air node obtained in the current iteration are substituted into the network optimization model to obtain a third optimization model; the lower bound of the minimum user communication rate in the third optimization model is maximized to obtain the target location information of the air node; wherein, the setting parameters include: the target association from the UAV base station to the user, the allocated bandwidth of the UAV base station, the transmit power of the air node, and the deployment location; the network optimization model satisfies the following formula: in, ~ These represent the lower bound of the user communication rate, the detection error probability of the eavesdropper, the sum of the values ​​of the correlation variables from the UAV base station to each user, the maximum allocated bandwidth of the UAV base station, the transmit power of the air node, the flight altitude of the air node, and the constraints on the values ​​of the correlation variables from the UAV base station to each user, respectively. Represents a set of users. Represents the set of listeners. This represents a collection of drone base stations. This represents a set of jammers. Indicates the first The lower bound of the user communication rate for each user. This represents the lower bound threshold of the user communication rate. Indicates the first The drone base station to the first The values ​​of the associated variables for each user Indicates the first The drone base station to the first Bandwidth allocated to each user This indicates the total bandwidth of the drone swarm network. This indicates the maximum transmit power of the air node. and These represent the minimum and maximum flight altitudes of the airborne nodes, respectively. Indicates the first The flight altitude of each drone base station Indicates the first The flight altitude of the jammer; The lower bound of the user communication rate satisfies the following formula: in, Indicates the first The drone base station to the first The lower bound of the communication rate provided by each user. Indicates the first The drone base station to the first Downlink channel gain for individual users Indicates the first The transmission power of each drone base station, Indicates the first The jammer to the first Downlink channel gain of the listener This represents the power of Gaussian white noise.

2. A covert communication device based on an unmanned aerial vehicle (UAV) swarm network, characterized in that, The drone swarm network includes air nodes and ground nodes. The air nodes include drone base stations and jammers. The ground nodes include users and eavesdroppers. The covert communication device includes: The determination module is used to determine the location and quantity information of the ground nodes, as well as the quantity information of the air nodes; The first generation module is used to establish a downlink communication channel model from the air node to the ground node based on the location information, quantity information and environmental parameter information; The second generation module is used to establish a covert communication constraint model for the UAV base station based on the detection error probability of the eavesdropper under the downlink communication channel model; wherein the covert communication constraint model for the UAV base station satisfies the following formula: ， ， in, Indicates the first The probability of a single listener making a detection error, where M represents the number of drone base stations. Indicates the first The transmission power of each drone base station, Indicates the first The drone base station to the first Downlink channel gain of the listener This indicates the adjustable peak transmit power of the jammer. Indicates the first The jammer to the first The downlink channel gain of each eavesdropper, where J represents the number of jammers. This represents the probability that a drone swarm network can tolerate a correctly detected eavesdropper, where N represents the number of eavesdroppers. The downlink channel gain satisfies the following formula: in, Indicates the first The first aerial node to the first Downlink channel loss of each ground node Indicates the first The first aerial node to the first The probability that the downlink of a ground node is a line-of-sight link. Indicates the first The first aerial node to the first The probability that the downlink of a ground node is a non-line-of-sight link. Indicates the carrier frequency. Indicates the first The first aerial node to the first The three-dimensional Euclidean distance between each ground node Represents the speed of light. and These represent the path loss coefficients for line-of-sight links and non-line-of-sight links, respectively. Indicates the first The first aerial node to the first Downlink channel gain of each ground node and They respectively satisfy the following formulas: ， ， in, and These are environmentally relevant modeling parameters. Indicates the first The first aerial node and the first The elevation angle between ground nodes Represents the set of nodes in the air. Represents the set of ground nodes; The third generation module is used to establish a network optimization model corresponding to the UAV swarm network based on the UAV base station covert communication constraint model. The network optimization model includes the following parameter constraints: lower bound of user communication rate, detection error probability of eavesdropper, sum of the values ​​of the correlation variables from UAV base station to each user, maximum allocated bandwidth of UAV base station, transmission power of air node, flight altitude of air node and values ​​of the correlation variables from UAV base station to each user. The output module is used to iteratively execute steps one through three until the relative growth rate of the lower bound of the user communication rate is less than a threshold or the number of iterations is greater than or equal to a set threshold, thereby obtaining the setting parameters of the UAV swarm network: Step one: Based on the network optimization model, fix the location information and transmission power of the airborne nodes to obtain a first optimization model; maximize the lower bound of the minimum user communication rate in the first optimization model to obtain the target association from the UAV base station to the user and the allocated bandwidth of the UAV base station; Step two: Substitute the target association and the allocated bandwidth obtained in the current iteration into the network optimization model, and fix the location of the airborne nodes. The information is used to obtain a second optimization model; the lower bound of the minimum user communication rate in the second optimization model is maximized to obtain the transmit power of the air node; the third step is to substitute the target association, the allocated bandwidth, and the transmit power of the air node obtained in the current iteration into the network optimization model to obtain a third optimization model; the lower bound of the minimum user communication rate in the third optimization model is maximized to obtain the target location information of the air node; wherein, the setting parameters include: the target association from the UAV base station to the user, the allocated bandwidth of the UAV base station, the transmit power of the air node, and the deployment location; the network optimization model satisfies the following formula: in, ~ These represent the lower bound of the user communication rate, the detection error probability of the eavesdropper, the sum of the values ​​of the correlation variables from the UAV base station to each user, the maximum allocated bandwidth of the UAV base station, the transmit power of the air node, the flight altitude of the air node, and the constraints on the values ​​of the correlation variables from the UAV base station to each user, respectively. Represents a set of users. Represents the set of listeners. This represents a collection of drone base stations. This represents a set of jammers. Indicates the first The lower bound of the user communication rate for each user. This represents the lower bound threshold of the user communication rate. Indicates the first The drone base station to the first The values ​​of the associated variables for each user Indicates the first The drone base station to the first Bandwidth allocated to each user This indicates the total bandwidth of the drone swarm network. This indicates the maximum transmit power of the air node. and These represent the minimum and maximum flight altitudes of the airborne nodes, respectively. Indicates the first The flight altitude of each drone base station Indicates the first The flight altitude of the jammer; The lower bound of the user communication rate satisfies the following formula: in, Indicates the first The drone base station to the first The lower bound of the communication rate provided by each user. Indicates the first The drone base station to the first Downlink channel gain for individual users Indicates the first The transmission power of each drone base station, Indicates the first The jammer to the first Downlink channel gain of the listener This represents the power of Gaussian white noise.

3. An electronic device, characterized in that, include: Memory, processor; The memory is used to store program instructions; The processor is configured to invoke the program instructions to execute the covert communication setup method as described in claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the covert communication setup method as described in claim 1.

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