Joint optimization method and device for anti-jamming communication based on unmanned aerial vehicle and electronic equipment

Abstract

The present application belongs to the field of unmanned aerial vehicle communication technology, considering that when wireless sensors communicate with unmanned aerial vehicles, a mobile malicious node on the ground interferes with the communication of unmanned aerial vehicles, assuming that each sensor and unmanned aerial vehicle adopts frequency division multiplexing for communication, so there is no mutual interference between each sensor, and thus a communication model under malicious interference is obtained. Under this model, in order to maximize the throughput of unmanned aerial vehicles under malicious interference, an optimization problem is obtained. Since the optimization problem is a non-convex problem and has a large variable space, it is difficult to obtain the optimal solution. In order to solve this problem, the path discretization method is used to transform the original problem into a discrete equivalent problem, then the successive convex approximation and block coordinate descent techniques are applied to obtain a local optimal solution, and it is proved by the convex optimization principle that the local optimal solution can be approximately replaced by the global optimal solution, and finally the convergence and effectiveness of the algorithm are verified and shown by simulation results.

Description

Technical Field

[0001] This invention belongs to the field of anti-jamming communication technology for unmanned aerial vehicles (UAVs), and particularly relates to a joint optimization method, device, and electronic equipment based on anti-jamming communication for UAVs. Background Technology

[0002] Unmanned Aerial Vehicle (UAV) communication has attracted increasing attention in recent years due to its high maneuverability, on-demand deployment, and excellent line-of-sight transmission channels. Because the high maneuverability of UAVs provides new degrees of freedom, optimizing UAV flight trajectories through algorithm design can significantly improve system performance.

[0003] Typically, Wireless Sensor Networks (WSNs) consist of numerous sensor nodes (SNs) that are battery-powered and deployed (systematically or randomly) across a specific geographical area to measure and transmit measurement information. Due to the collaborative working method and flexible deployment of wireless sensors, WSNs have found widespread application.

[0004] In UAV relay systems, UAV resource optimization and path optimization have always been crucial design considerations. UAVs, as a future-oriented factor in data collection, can support on-demand, reliable, and energy-efficient data gathering. Considering ground-based wireless interference, algorithms need to be designed to enable effective communication for UAVs even under malicious interference, in order to achieve better communication conditions.

[0005] Convex optimization algorithms, as an effective optimization tool, have attracted widespread attention. In convex optimization algorithms, a mathematical model of the application scenario is established, and a convex optimization problem is derived through reasonable derivation. Convex optimization tools are then used to solve and optimize the objective formula. Since the local optimum of a convex optimization problem is the global optimum, the optimization strategy can be quickly obtained. Using convex optimization algorithms to solve the UAV resource optimization problem effectively ensures UAV communication. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a joint optimization method, apparatus, and electronic device based on UAV anti-jamming communication. By introducing the relevant principles of convex optimization, a convex optimization algorithm for UAV resource optimization is constructed, which significantly improves UAV throughput compared to traditional flight schemes.

[0007] This invention is achieved through the following technical solution:

[0008] A joint optimization method based on UAV anti-jamming communication includes the following steps:

[0009] S101. Construct a communication model for UAV coverage of wireless sensors, and analyze the uplink throughput formula for wireless sensor data transmission based on the coverage communication model.

[0010] S102. Set up malicious ground interference and combine it with the UAV coverage communication model to obtain the throughput formula of the UAV communication model under malicious interference.

[0011] S103. Based on the communication transmission model of the UAV under malicious interference obtained in step S102, and combined with the resources of the UAV and wireless sensors, the optimization problem is obtained.

[0012] S104. Based on the resource optimization problem obtained in step S103, and combined with the linearization principle of convex optimization, the non-convex problem in the model is transformed into a convex problem.

[0013] S105. Based on the convex problem obtained in step S104, joint optimization is performed using the block coordinate descent algorithm to obtain resource optimization strategies for the UAV and wireless sensors.

[0014] Preferably, in step S101, multiple wireless sensors are used as transmitters, and the UAV is used as a receiver to construct a UAV line-of-sight communication model:

[0015] Without sacrificing versatility, the UAV utilizes a frequency division multiple access (FDMA) scheme to simultaneously receive wireless sensor information; the channel from the UAV to the wireless sensor is dominated by the line-of-sight (LAS) channel, therefore the time-slot channel gain of the UAV coverage communication model is:

[0016]

[0017] Where H is the UAV's flight altitude, q[n] is the two-dimensional coordinates of the UAV projected onto the ground in the nth time slot, w is the two-dimensional coordinates of the ground sensor, and β0 is the channel energy of the reference distance.

[0018] Preferably, the throughput formula for the uplink of wireless sensor data transmission in step S101 is as follows:

[0019]

[0020] Where, x i [n] represents the proportion of the total bandwidth allocated to the i-th wireless sensor, B represents the total bandwidth of all wireless sensors, and p i [n] represents the power allocated to each user by the drone.

[0021] Preferably, the throughput of the S102 UAV's communication model under malicious interference is:

[0022]

[0023] Where, p m h represents the power of malicious interference. m [n] represents the free-space path loss model for malicious interference, and n0 is the power spectral density of Gaussian white noise.

[0024] Preferably, the optimization problem obtained in step S103 is:

[0025]

[0026] st0≤x i [n]≤1

[0027]

[0028]

[0029] ||q[n]-w i ||≤V max

[0030] Where U represents the number of wireless sensors, i represents the serial number of the wireless sensor, N represents the total flight time slots of the UAV, and P max It is the maximum transmission power of all wireless sensors, V max That is the maximum flight speed of the drone. These are the drone trajectory strategy, wireless sensor transmission power, and transmission bandwidth.

[0031] Preferably, in step S104, the problem is a non-convex problem regarding the UAV path. Since the objective function is a non-convex function regarding the UAV trajectory, a linearization process is performed on the objective function using the Taylor formula, introducing a slack variable L[n], which is related to I[n] and (L... f [n],I f [n]) is any feasible point;

[0032] The objective function can be expressed as:

[0033]

[0034] Where A = -1 / (L) f [n]+(L f [n]) 2 I f [n], C = -1 / (I f [n]+(I f [n]) 2 L f [n]; η represents a slack variable;

[0035] Due to the non-convexity of the constraints, the constraints also need to be linearized for the expression ||q[n]-J[n]||. 2 Performing a first-order Taylor expansion, where J[n] represents the location of the malicious interference, yields a lower boundary:

[0036] Where (x[n], y[n]) is the location of the drone, (x m [n],y m [n]) represents the location of ground disturbance; a slack variable d is introduced. m [n], such that

[0037] p m β0d m [n] -1 ≤I i (n)

[0038] d m [n]≤q u [n]

[0039] 0≤d m [n]

[0040] Therefore, the non-convex optimization problem is transformed into a convex optimization problem as follows:

[0041]

[0042]

[0043] p i [n]h i [n]≥L i [n] -1

[0044] ||q[n]-w i ||≤V max

[0045] n0+p m β0d i [n] -1 ≤I i (n)

[0046] d m [n]≤q u [n]

[0047] 0≤d m [n]

[0048] Preferably, step S105, obtaining the optimization algorithm steps for the UAV under malicious interference, is as follows:

[0049] Step 1: Obtain the wireless sensor bandwidth and power allocation strategy by initializing the drone trajectory;

[0050] Step two: Optimize the drone trajectory by using the wireless sensor bandwidth and power allocation strategy obtained in step one.

[0051] Step 3: Use the drone trajectory obtained in the previous step as the initial value to further optimize the wireless sensor bandwidth and power allocation strategy;

[0052] Step four: Utilize the wireless sensor bandwidth and power allocation strategy obtained in the previous step to further optimize the drone trajectory.

[0053] Step 5: Repeat steps 3 and 4 until the optimized throughput converges.

[0054] The present invention also provides an electronic device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the steps of any of the methods described above.

[0055] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0056] Compared with the prior art, the present invention has the following beneficial technical effects:

[0057] This invention considers the communication between wireless sensors and drones, specifically the interference from a mobile malicious node on the ground. Assuming frequency division multiplexing (FDM) is used for communication between the sensors and the drone, there is no mutual interference between the sensors, thus deriving a communication model under malicious interference. Under this model, maximizing the throughput of the drone under malicious interference leads to an optimization problem. Since this optimization problem is non-convex and has a large variable space, obtaining the optimal solution is difficult. To address this issue, a path discretization method is used to transform the original problem into a discrete equivalent problem. Then, successive convex approximation and block coordinate descent techniques are applied to obtain a local optimum. The convex optimization principle proves that the obtained local optimum can approximate the global optimum. Finally, simulation results confirm and demonstrate the effectiveness of the algorithm and its superiority over other algorithms. Attached Figure Description

[0058] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0059] Figure 1This is a flowchart of a joint optimization method for anti-jamming communication based on unmanned aerial vehicles (UAVs) proposed in this invention.

[0060] Figure 2 The diagram shows the instantaneous speeds of the UAV of this invention at maximum flight speeds of 60m / s, 45m / s, and 30m / s.

[0061] Figure 3 This invention relates to a bandwidth allocation optimization strategy for a UAV with a flight time of 120 time slots and maximum flight speeds of 60m / s, 45m / s, and 30m / s.

[0062] Figure 4 This invention relates to a wireless sensor power allocation optimization strategy for UAVs with a flight time of 120 time slots and maximum flight speeds of 60m / s, 45m / s, and 30m / s, respectively.

[0063] Figure 5 It refers to the throughput obtained by the UAV of this invention when proposing the algorithm and other different flight trajectories. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0065] See Figure 1 A joint optimization method based on UAV anti-jamming communication includes the following steps:

[0066] S101: Construct a communication model for UAV-covered wireless sensors, and derive the throughput formula for uplink sensor data transmission based on the UAV-covered communication model.

[0067] Specifically, a communication transmission model for the UAV under malicious interference is constructed, using multiple wireless sensors as transmitters and the UAV as receiver. Considering uplink transmission, the UAV acts as an aerial base station, and the ground sensors are denoted as U. Without sacrificing versatility, the UAV simultaneously receives data from the wireless sensors using a frequency division multiple access (FDMA) scheme. Since time and trajectory are continuous and difficult to process, for simplification, the time level is divided into equal time slots, i.e., t = nδ. t ,n=1,...,N, where nδ t The time period is the length of the time interval. Ground sensor w iThe two-dimensional coordinates of i = 1, ..., U are static. Using the path discretization method, the trajectory of the UAV can be represented as: q[n] = {x[n], y[n]} T Let ||.|| denote the 2-norm of a vector.

[0068] Due to the high altitude, the channel from the UAV to the user is dominated by the line-of-sight channel. Therefore, the time-slot channel gain of the UAV coverage communication model is:

[0069]

[0070] Where H is the UAV's flight altitude, q[n] is the UAV's two-dimensional coordinates in the nth time slot, and w i [n] represents the two-dimensional coordinates of the i-th ground sensor. β0 is the channel energy at the reference range.

[0071] Assuming the frequencies are orthogonal, x i [n] represents the proportion of the total bandwidth allocated to the i-th wireless sensor. The drone allocates p power to each user. i [n]. B represents the total bandwidth of all wireless sensors, therefore the uplink throughput of sensor data transmission is:

[0072]

[0073] S102: Set up malicious ground interference and combine it with the UAV coverage communication model to obtain the UAV communication transmission model under malicious interference.

[0074] Specifically, the drone is affected by malicious interference. Therefore, the throughput of the drone's uplink transmission model under malicious interference is:

[0075]

[0076] Where, p m h represents the power of malicious interference. m [n] represents the free-space path loss model for malicious interference. n0 is the power spectral density of Gaussian white noise.

[0077] S103. Based on the uplink transmission model of the UAV under malicious interference obtained in steps S101 and S102, and combined with the optimization problem of the UAV and wireless sensor resources.

[0078] Specifically, for the optimization variable being the drone trajectory strategy Wireless sensor receiving bandwidth and wireless sensor transmission power Therefore, the optimization problem is formulated as follows:

[0079]

[0080] st0≤x i [n]≤1

[0081]

[0082]

[0083] ||q[n]-q[n-1]||≤V max

[0084] S104: Based on the optimization problem obtained in step S103, and combined with the principle of convex optimization, the non-convex problem in the model is transformed into a convex problem.

[0085] Specifically, the drone trajectory is optimized using the block coordinate descent method, first fixing the sensor power. and drone bandwidth allocation At this point, the problem is non-convex with respect to the UAV trajectory. Since the objective function is also non-convex, we linearize the objective function using Taylor's formula, introducing slack variables L[n] and I[n]. For any feasible point (L... f [n],I f [n]), the objective function can be expressed as:

[0086]

[0087] Where A = -1 / (L) f [n]+(L f [n]) 2 I f [n], B = -1 / (I f [n]+(I f [n]) 2 L f [n], where η represents a slack variable. Due to the non-convexity of the constraints, linearization is also required for the expression ||q[n]-J[n]||. 2 Performing a first-order Taylor expansion, where J[n] represents the location of the malicious interference, we obtain:

[0088]

[0089] Where (x[n], y[n]) is the location of the drone, (x m [n],y m [n]) represents the two-dimensional coordinates of malicious interference. A slack variable d is introduced. m [n], such that

[0090] p mβ0d m [n] -1 ≤I i (n)

[0091] d m [n]≤q u [n]

[0092] 0≤d m [n]

[0093] Therefore, the non-convex optimization problem is transformed into a convex optimization problem as follows:

[0094]

[0095]

[0096] p i [n]h i [n]≥L i [n] -1

[0097] ||q[n]-q[n-1]||≤V max

[0098] n0+p m β0d m [n] -1 ≤I i (n)

[0099] d m [n]≤q u [n]

[0100] 0≤d m [n]

[0101] S105: Based on the convex problem obtained in step S104, joint optimization is performed using the block coordinate descent method to obtain resource optimization strategies for the UAV and wireless sensors. Specifically, according to the block coordinate descent method, the UAV trajectory is first fixed, and the communication rate function R can be found. i [n] Regarding the bandwidth of wireless sensors x i [n] and power allocation p i[n] The strategy is a perspective function. The wireless sensor bandwidth and power optimization problem is a convex optimization problem, where both the objective function and constraint functions are convex. This problem can be directly solved using CVX. According to the block coordinate descent method, the optimized wireless sensor bandwidth and power strategy will be used as initial conditions to optimize the UAV trajectory. Then, the obtained UAV trajectory will again be used as the initial trajectory to optimize the wireless sensor bandwidth and power allocation, and so on until the UAV throughput and wireless sensor resource allocation strategies converge, resulting in a joint optimization strategy for the UAV under malicious interference. This algorithm utilizes the block coordinate descent method, and the entire algorithm solves the problem iteratively. In summary, the proposed algorithm steps for solving the problem are:

[0102] Step 1: Obtain the wireless sensor bandwidth and power allocation strategy by initializing the drone trajectory.

[0103] Step two involves optimizing the wireless sensor bandwidth and power allocation strategy obtained in step one to further optimize the drone trajectory.

[0104] Step 3: Use the drone trajectory obtained in the previous step as the initial value to further optimize the wireless sensor bandwidth and power allocation strategy.

[0105] Step four involves applying the wireless sensor bandwidth and power allocation strategy obtained in the previous step to further optimize the drone's trajectory.

[0106] Step 5: Repeat steps 3 and 4 until the optimized throughput converges.

[0107] It should be noted that the following beneficial effects can be achieved by adopting the above embodiments of the present invention:

[0108] The parameters are set as follows: Total communication bandwidth is B = 1MHz. Noise power spectral density is n0 = -160dBm / Hz. The UAV is flying at a level altitude of H = 100m. The channel power gain at a reference distance d0 = 1m is β0 = -60dB. Without loss of generality, the six ground user locations are defined as: w1 = [700, 100], w2 = [200, 450], w3 = [-300, 450], w4 = [-600, 200], w5 = [-450, -450], w6 = [400, -500]. It is assumed that the interference point is a counter-clockwise circular path centered at the origin with a radius of 700 and a starting point of [-700, 0] to interfere with the UAV.

[0109] In this invention, to maintain maximum throughput at all times, the drone communicates as closely as possible to the sensor. When the interference signal moves, the drone moves in accordance with the intensity of the interference. When malicious interference moves counterclockwise, the drone follows suit in the same counterclockwise direction, always maintaining the greatest possible distance from the malicious interference. To maximize throughput, the drone's path balances the interference source and the user, constantly finding the optimal communication point.

[0110] In this invention, the closer the sensor is to the drone, the greater the communication bandwidth and power. When the drone is closest to the sensor, both the communication bandwidth and power of the sensor reach their maximum. Simulation results confirm and demonstrate the effectiveness of this algorithm and its superiority over other algorithms.

[0111] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in the present invention, and such modifications or substitutions should all be covered within the scope of protection of the present invention.

Claims

1. A joint optimization method based on UAV anti-jamming communication, characterized in that, Solving the drone resource allocation problem using convex optimization includes the following steps: S101. Construct a communication model for UAVs covering wireless sensors, assume malicious ground interference, and derive the throughput formula for the UAV's communication model under malicious interference based on the UAV's communication model covering wireless sensors; the throughput of the UAV's communication model under malicious interference is: in, p m Power of malicious interference, h m [ n ] represents the free-space path loss model for malicious interference. n 0 represents the power spectral density of Gaussian white noise. x i [ n ] indicates that it is assigned to the first The proportion of bandwidth of each wireless sensor to the total bandwidth B This represents the total bandwidth of all wireless sensors. p i [ n [Power allocated to each user for the drone;] The time-slot channel gain for the UAV coverage communication model is expressed as follows: in, H It is the drone's flight altitude. q [ n [This is the drone in the] n Two-dimensional coordinates projected onto the ground for each time slot. w These are the two-dimensional coordinates of the ground sensor. It is the channel energy at the reference distance; S102. Based on the communication model of the UAV under malicious interference obtained in step S102, and combined with the resources of the UAV and wireless sensors, the optimization problem is obtained. S103. Based on the resource optimization problem obtained in step S103, and combined with the linearization principle of convex optimization, the non-convex problem in the model is transformed into a convex problem. S104. Based on the convex problem obtained in step S104, joint optimization is performed using the block coordinate descent algorithm to obtain resource optimization strategies for the UAV and wireless sensors.

2. The joint optimization method based on UAV anti-jamming communication according to claim 1, characterized in that, Step S102 yields the following optimization problem: in, U Indicates the number of wireless sensors. i Indicates the serial number of the wireless sensor. N Indicates the total flight timeslots of the drone. P max It is the maximum transmission power of all wireless sensors. V max That is the maximum flight speed of the drone. These are the drone trajectory strategy, wireless sensor transmission power, and transmission bandwidth, respectively.

3. The joint optimization method based on UAV anti-jamming communication according to claim 2, characterized in that, Step S103: This problem is non-convex with respect to the UAV path. Since the objective function is also non-convex, it is necessary to linearize the objective function using Taylor's formula and introduce slack variables. L [ n ],and I [ n ], ( L f [ n ], I f [ n ]) is any feasible point; The objective function is expressed as: in, , ; η Represent a slack variable; Therefore, the non-convex optimization problem is transformed into a convex optimization problem as follows: in, d m [n] is the introduced slack variable, used below x , y Let x and y represent the coordinates of a two-dimensional coordinate system, respectively. Then, the flight trajectory of the UAV can be represented as: For the expression Perform a first-order Taylor expansion. J [n] represents the location of the malicious interference, resulting in: Here, the superscript f represents any feasible value of the variable.

4. The joint optimization method based on UAV anti-jamming communication according to claim 3, characterized in that, Step S104, the optimization algorithm steps for the UAV under malicious interference are as follows: Step 1: Obtain the wireless sensor bandwidth and power allocation strategy by initializing the drone trajectory; Step two: Optimize the drone trajectory by using the wireless sensor bandwidth and power allocation strategy obtained in step one. Step 3: Use the drone trajectory obtained in the previous step as the initial value to further optimize the wireless sensor bandwidth and power allocation strategy; Step 4: Further optimize the drone trajectory using the wireless sensor bandwidth and power allocation strategy obtained in the previous step; Step 5: Repeat steps 3 and 4 until the optimized throughput converges.

5. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to perform the steps of the method according to any one of claims 1 to 4.

6. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 4.

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