A low-complexity method for deploying drones in a space-ground collaborative communication network

Abstract

The present invention relates to a low-complexity method for deploying drones in a space-ground collaborative communication network, belonging to the technical field of drone communication deployment. The method comprises the following steps: S1: multiple ground users send data to drone nodes, and the drone simultaneously sends interference signals to eavesdropping nodes to ensure the secure transmission of data from ground users to drones; S2: the drone transmits the collected information to a satellite, and uses the drone to send artificial noise to suppress eavesdroppers; S3: the entire system optimization problem is modeled and the original problem is solved to provide a feasible solution for the deployment of drones in a space-ground collaborative communication network. The method of the present invention makes full use of existing channel state information, takes into account the spatial position of the drone and the transmission power limit, and optimizes the drone transmission solution. The process is simple, has low complexity, and is highly practical.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle communication deployment, and relates to a low-complexity method for deploying unmanned aerial vehicles in a ground-to-space collaborative communication network. Background Art

[0002] In recent years, with the continuous advancement of computer technology and industrialization, lightweight communication equipment has seen explosive growth. However, the service capabilities of existing communication systems are limited. Addressing the communication needs of massive numbers of users in remote areas poses a significant challenge. To effectively serve ground users (nodes), researchers have proposed using low-cost, highly maneuverable, and controllable drones (UAVs) in remote areas to replace expensive infrastructure. These drones could serve as communication relays, providing access and data transmission services to ground users. Ground users transmit signals to UAVs, which then forward the received signals to satellites, ultimately forming a collaborative air-space communication network that provides communication services to users in remote areas. In a space-ground collaborative communication network, the three-dimensional (3D) spatial placement and power allocation of UAVs affect network transmission performance, making the design of UAV spatial deployment schemes crucial. Given that the 3D UAV deployment problem is NP-hard, the optimal solution can only be found through an exhaustive search in the full-dimensional space. This poses a significant challenge for lightweight UAVs with limited computing power. Furthermore, the challenges of UAV power allocation and secure data transmission complicate the problem significantly. Therefore, designing a low-complexity secure UAV transmission scheme within a space-ground collaborative communication network is crucial.

[0003] Currently, research on multi-layer networks for space-ground collaborative communication networks is limited. Low-complexity secure transmission solutions for space-ground collaborative communication networks are a relatively new area and a major research hotspot within integrated space-ground communication networks. Secure transmission primarily utilizes physical layer security methods, given the limited infrastructure in remote areas that makes it difficult to support traditional centralized key distribution encryption systems. Currently, most drone 3D positioning methods utilize sequential optimization, which involves sequentially optimizing unknown parameters and approaching the optimal solution through continuous iteration.

[0004] A key design goal of physical layer security is to maximize the so-called confidentiality ratio—the communication rate of a wireless channel—using a lightweight approach, provided that an eavesdropper cannot eavesdrop on the channel. Prior art discloses an analytical method for optimizing drone altitude to provide maximum coverage for ground users. Conversely, by fixing the altitude, the horizontal position of drones is optimized to minimize the number of drones required to cover a given set of ground users. In three-dimensional space, prior art discloses a drone-based cell layout optimization problem to maximize the number of covered users. Beyond drone layout optimization, leveraging the high maneuverability of drones in mobile drone-enabled wireless networks promises to unlock the full potential of drone-to-ground communications. Another prior art study also addresses drone trajectory optimization in mobile relay systems and point-to-point energy-saving systems, employing sequential convex optimization techniques to address non-convex trajectory optimization problems. While this prior art provides a general framework for trajectory optimization in two-dimensional space, it focuses solely on single drone and single ground user configurations.

[0005] Existing secure drone communication solutions consider relatively simple scenarios, often focusing on designing transmission schemes between drones and the ground. These solutions fail to fully consider the constraints between multi-layer networks and actual application scenarios, making them difficult to directly apply. Applications in future space-ground collaborative networks will require comprehensive consideration of the operating modes and characteristics of different mobile node types, along with their transmission rates and dynamic characteristics, to optimize node deployment and maximize the secure communication rate for users served by the nodes. Currently, few low-complexity, secure drone deployment and signal transmission solutions have been designed specifically for space-ground collaborative communication networks. Summary of the Invention

[0006] In view of this, the object of the present invention is to provide a low-complexity method for deploying drones in a space-ground collaborative communication network.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A low-complexity method for deploying drones in a space-ground collaborative communication network includes the following steps:

[0009] S1: Multiple users on the ground send data to the drone node, and the drone sends a jamming signal to the eavesdropping node to ensure the secure transmission of data from the ground user to the drone;

[0010] S2: The drone transmits the collected information to the satellite, and uses the drone to send artificial noise to suppress the eavesdropper;

[0011] S3: Model the entire system optimization problem and solve the original problem to provide a feasible solution for the deployment of drones in the space-ground collaborative communication network.

[0012] Furthermore, the stage of step S1 is defined as stage I, and the position coordinates of the UAV are u=(x U ,y U ,h U ), ground terminal node The position is (L i ,0)=(x i ,y i ,0), Ground eavesdropper The position of (L m ,0)=(x m ,y m ,0); is the information security transmission rate of node i in phase I:

[0013]

[0014] in, is the transmission power of ground terminal node i, B is the bandwidth allocated to phase I, is the channel gain, d i,U is the transmission distance between the UAV and node i, is the attenuation factor, is the noise power at the airborne end, is the channel from node i to eavesdropper m, is the channel gain, is the small-scale fading gain, is the channel from the drone to the eavesdropper m in phase I, is the noise power of the eavesdropping end, x U ∈[0,X U,max ], y U ∈[0,Y U,max ], h U ∈[H min ,H max ] are the feasibility domains of the UAV deployment space, is the artificial noise power in stage I, Transmit power for drone data transmission.

[0015] Furthermore, step S2 is defined as phase II, in which the secure communication rate is

[0016]

[0017] in, is the transmission power of the Phase II UAV, is the noise power at the satellite end, h U→S is the channel gain between the UAV and the satellite, is the channel from the drone to the eavesdropper m in phase II, is the artificial noise power of stage II.

[0018] Furthermore, the entire system optimization problem in step S3 is modeled as:

[0019]

[0020] in, is the limited power of the airborne communication payload.

[0021] Further, the entire system optimization problem is solved by the following steps:

[0022] make Assuming that the self-interference at the node is 0, the overall system optimization problem becomes:

[0023]

[0024] Formula (4) is divided into different sub-problems, namely:

[0025] ① Optimization subproblem: Given The original problem is optimized by solving the following problems:

[0026]

[0027] in

[0028]

[0029] ② Optimization subproblem: Given question The optimization solution is achieved by solving the following problems:

[0030]

[0031] ③ Optimization subproblem: Given question The optimization solution is achieved by solving the following problems:

[0032]

[0033] ④Optimize the position of UAV: h U

[0034] Given question By optimizing the drone's First, solve the problem of the height of the drone and solve the following problems to achieve optimization solution:

[0035]

[0036] ⑤Optimize the position of UAV: P U ={x U ,y U}

[0037] Given Solve the following problems to achieve optimization solution:

[0038]

[0039] make

[0040]

[0041] in

[0042]

[0043] Introducing the slack variable t i ≤||p U -p i || 2 , and in p U Expanding at [j], we get:

[0044]

[0045]

[0046] in,

[0047]

[0048] The original question becomes:

[0049]

[0050] The convex optimization tool is used to solve the convex problems in Equations (6), (7), (8), (9), and (16) in one cycle. After multiple iterations until the algorithm converges, the joint optimization solution for node deployment and power allocation is finally obtained.

[0051] The present invention provides a practical and rapid drone deployment solution for a space-ground collaborative communication network. Taking into account the distances between different nodes and drones and the varying eavesdropping threats they face, the method optimizes the drone's spatial position, transmit power, and interference signal transmit power for different ground users and eavesdropping node locations, thereby enabling secure communications for ground users. The method leverages existing channel state information and optimizes the drone's transmission scheme, taking into account the drone's spatial position and transmit power limitations. This approach is simple, low-complexity, and highly practical.

[0052] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0054] Figure 1 This is the overall flow chart of the UAV node optimization deployment technical solution of the present invention;

[0055] Figure 2 This is a schematic diagram of the simulation results of the UAV deployment position of the present invention;

[0056] Figure 3 This is a schematic diagram of the safety speed simulation results after the optimized deployment of the UAV of the present invention. DETAILED DESCRIPTION

[0057] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0058] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0059] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0060] like Figure 1 As shown in FIG, a low-complexity method for deploying drones in a space-ground collaborative communication network provided by the present invention includes the following steps:

[0061] Step 1: Multiple users on the ground send data to the drone node. At the same time, the drone sends an interference signal to the eavesdropping node to ensure the secure transmission of data from the ground user to the drone.

[0062] Among them, the position coordinates of the UAV are u=(x U ,y U ,h U ), ground terminal node The position is (L i ,0)=(x i ,y i ,0), Ground eavesdropper The position of (L m ,0)=(x m ,y m ,0). is the information security transmission rate of node i in phase I:

[0063]

[0064] in, is the transmission power of ground terminal node i, B is the bandwidth allocated to phase I, is the channel gain, d i,U is the transmission distance between the UAV and node i, is the attenuation factor. is the noise power at the airborne end, is the channel from node i to eavesdropper m, is the channel gain, is the small-scale fading gain, is the channel from the drone to the eavesdropper m in phase I, is the noise power of the eavesdropping end, x U ∈[0,X U,max ], y U ∈[0,Y U,max ], h U ∈[H min ,H max ] are the feasibility domains of the UAV deployment space, is the artificial noise power in stage I, Transmit power for drone data transmission.

[0065] Step 2: The drone transmits the collected information to the satellite. To ensure the security of the system, during this transmission phase, it is proposed to use the drone to send artificial noise to suppress eavesdroppers and ensure the secure transmission of information.

[0066] The secure communication rate in Phase II is

[0067]

[0068] in, is the transmission power of the Phase II UAV, is the noise power at the satellite end, h U→S is the channel gain between the UAV and the satellite, is the channel from the drone to the eavesdropper m in phase II, is the artificial noise power in phase II,

[0069] Step 3: Model the entire system optimization problem and solve the original problem to provide a feasible solution for the deployment of drones in the space-ground collaborative communication network;

[0070] The system optimization problem can be modeled as:

[0071]

[0072] in, is the limited power of the airborne communication load. Constraints (f) and (g) are non-convex sets, which makes the above problem an NP-hard problem. The optimal solution can only be obtained through a complex exhaustive search method. Therefore, it is proposed to simplify the problem to facilitate the solution. Assuming that the self-interference at the node is 0, the above optimization problem becomes:

[0073]

[0074] It can be broken down into different sub-problems:

[0075] ① Optimization subproblem

[0076] Given The original problem can be optimized by solving the following problems:

[0077]

[0078] in

[0079]

[0080] The above problem has been converted into a convex problem and can be quickly solved using the commonly used convex optimization toolbox CVX.

[0081] ② Optimization subproblem

[0082] Given question The optimization solution can be achieved by solving the following problems

[0083]

[0084] Since all constraints in the above equation are convex, they can be solved directly using convex optimization tools convex optimization solvers.

[0085] ③ Optimization subproblem

[0086] Similarly, given question The optimization solution can be achieved by solving the following problems

[0087]

[0088] This problem can be solved directly using convex optimization solvers.

[0089] ④Optimize the position of UAV: h U

[0090] Given question By optimizing the drones First, solve the problem of the height of the drone and solve the following problems to achieve optimization solution:

[0091]

[0092] Convex optimization solvers can be used to solve the problem. ⑤ Optimize the position of UAV: P U ={x U ,y U}

[0093] Given Solve the following problems to achieve optimization solution:

[0094]

[0095] make

[0096]

[0097] in

[0098]

[0099] Introducing the slack variable t i ≤||p U -p i || 2 , and in p U Expanding at [j], we can get:

[0100]

[0101]

[0102] in,

[0103]

[0104] The original question becomes:

[0105]

[0106] The solution can be found using related convex optimization tools.

[0107] The specific algorithm can be implemented by using a convex optimization tool to solve the convex problems in equations (6), (7), (8), (9), and (16) in one cycle. After multiple iterations until the algorithm converges, a joint optimization solution for node deployment and power allocation can be obtained.

[0108] like Figure 2 (a) and (b) are the final deployment results of eavesdroppers, ground communication nodes, and drones in different positions (distributions). It is not difficult to see that drones eventually tend to be close to ground nodes, which is consistent with experience. Figure 3 This is the final safety rate result after optimization of phase I and phase II. After a finite number of iterations, the proposed algorithm can effectively converge and finally achieve the optimization of the system safety rate.

[0109] It can be seen that when there are eavesdropping nodes in the air-space collaborative communication network, the present invention proposes to use drones to build a secure transmission link, and optimizes the design of the drone deployment optimization plan based on the channel conditions, and finally provides a low-complexity drone deployment plan support for the ground node secure communication system under the air-space collaborative communication network, which has important theoretical significance and application value.

[0110] In summary, it can be seen that when there are eavesdropping nodes in the air-space collaborative communication network, the present invention proposes to use drones to build a secure transmission link, and optimizes the design of the drone deployment optimization plan based on the channel conditions. Ultimately, it provides low-complexity drone deployment solution support for the ground node secure communication system under the air-space collaborative communication network, which has important theoretical significance and application value.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A low-complexity method for deploying drones in a space-ground collaborative communication network, characterized by: The following steps are involved: S1: Multiple users on the ground send data to the drone node, and the drone sends a jamming signal to the eavesdropping node to ensure the secure transmission of data from the ground user to the drone. S2: The drone transmits the collected information to the satellite, and uses the drone to send artificial noise to suppress the eavesdropper; Step S2 is defined as Phase II, in which the secure communication rate is in, is the transmission power of the Phase II UAV, is the noise power at the satellite end, h U→S is the channel gain between the UAV and the satellite, is the channel from the drone to the eavesdropper m in phase II, is the artificial noise power of phase II; S3: Model the entire system optimization problem and solve the original problem to provide a feasible solution for the deployment of drones in the space-ground collaborative communication network.

2. The low-complexity ground-to-space collaborative communication network UAV deployment method according to claim 1, characterized in that: The stage of step S1 is defined as stage I, and the position coordinates of the drone are u=(x U ,y U ,h U ), ground terminal node The position is (L i ,0)=(x i ,y i ,0), Ground eavesdropper The position of (L m ,0)=(x m ,y m ,0); is the information security transmission rate of node i in phase I: in, is the transmission power of ground terminal node i, B is the bandwidth allocated to phase I, is the channel gain, d i,U is the transmission distance between the UAV and node i, is the attenuation factor, is the noise power at the airborne end, is the channel from node i to eavesdropper m, is the channel gain, is the small-scale fading gain, is the channel from the drone to the eavesdropper m in phase I, is the noise power of the eavesdropping end, x U ∈[0,X U,max ], y U ∈[0,Y U,max ], h U ∈[H min ,H max ] are the feasibility domains of the UAV deployment space, is the artificial noise power in stage I, Transmit power for drone data transmission.

3. The low-complexity space-ground collaborative communication network UAV deployment method according to claim 1 is characterized by: The entire system optimization problem is solved by the following steps: make Assuming that the self-interference at the node is 0, the overall system optimization problem becomes: Formula (4) is divided into different sub-problems, namely: ① Optimization subproblem: Given The original problem is optimized by solving the following problems: in ② Optimization subproblem: Given question The optimization solution is achieved by solving the following problems: ③ Optimization subproblem: Given question The optimization solution is achieved by solving the following problems: ④Optimize the position of UAV: h U Given question By optimizing the drone's First, solve the problem of the height of the drone and solve the following problems to achieve optimization solution: ⑤Optimize the position of UAV: P U ={x U ,y U } Given Solve the following problems to achieve optimization solution: make in Introducing the slack variable t i ≤||p U -p i || 2 , and in p U Expanding at [j], we get: in, The original question becomes: The convex optimization tool is used to solve the convex problems in Equations (6), (7), (8), (9), and (16) in one cycle. After multiple iterations until the algorithm converges, the joint optimization solution for node deployment and power allocation is finally obtained.

Images