Sensitivity integrated waveform and unmanned aerial vehicle trajectory design method for realizing safe mobile edge calculation

Abstract

The invention discloses a communication and sensing integrated waveform and unmanned aerial vehicle trajectory design method for realizing secure mobile edge computing, and aims to minimize the total user energy consumption aiming at the condition that potential eavesdroppers exist in a mobile edge computing network architecture in which a plurality of ground users transmit uplink information to an unmanned aerial vehicle. And carrying out optimization design on the flux-inductance integrated waveform and the unmanned aerial vehicle trajectory. The method comprises the following steps: firstly constructing an optimization problem conforming to a scene, then decoupling an original optimization problem into sub-problems about a common-inductance integrated waveform and a UAV flight path based on a Block Coordinate Description (BCD) method, performing convex approximate fitting for each non-convex sub-problem, and finally solving by adopting an iterative algorithm to obtain a common-inductance integrated waveform and a UAV flight path. And thus, a global suboptimal solution of the whole optimization problem, an optimal sensing integrated waveform and a UAV flight trajectory are found.

Description

Technical Field

[0001] The present invention relates to the field of communication and sensing integration, and specifically to a method for designing a communication and sensing integrated waveform and a UAV trajectory for realizing secure mobile edge computing. Background Art

[0002] The statements in this section merely provide background technical information related to the present disclosure, and these statements may constitute prior art. In the process of implementing the present invention, the inventors found that at least the following problems exist in the prior art.

[0003] Wireless sensing performance is an important development direction for future 6G, and communication technology and sensing technology are gradually moving towards integration. Mobile edge computing is a classic technology to solve the problem of limited user resources but the need to process time-sensitive tasks. As a core node of future mobile communication networks, unmanned aerial vehicles (UAVs) have a wide range of application forms and prospects, and have attracted much attention from all walks of life.

[0004] In the literature [C. Chen, J. Yao, M. Jin, and Q. Guo, "Beamforming and computing capacity allocation for ISAC-assisted secure mobile edge computing," IEEE Wireless Commun. Lett., pp. 1-1, Sep. 2024.], multiple users transmit information uplink to a base station carrying a mobile edge computing server for computing, and at the same time, there is a potential eavesdropper eavesdropping. The duplex base station sends radar signals to interfere with the potential eavesdropper to achieve the purpose of secure communication.

[0005] The literature [Y. Xu, T. Zhang, Y. Liu, and D. Yang, "UAV-enabled integrated sensing, computing, and communication: A fundamental trade-off," IEEE Wireless Commun. Lett., vol. 12, no. 5, pp. 843-847, May 2023.] unloads information from a UAV in the air to a ground access point for computing, and at the same time, sends radar sensing signals to potential targets to complete the sensing task, taking a trade-off between the communication rate and the sensing beam pattern gain.

[0006] In [N. Huang, C. Dou, Y. Wu, L. Qian, B. Lin, and H. Zhou, "Unmanned-aerial-vehicle-aided integrated sensing and computation with mobile-edge computing," IEEE Internet Things J., vol. 10, no. 19, pp. 16830-16844, Oct. 2023.], the authors use a UAV to send radar signals to sense targets and offload the received reflected radar information to an edge server for computation.

[0007] In [Y. Liu, B. Zhang, D. Guo, H. Wang, and G. Ding, "Joint precoding design and location optimization in joint communication, sensing and computing of UAV systems," IEEE Trans. Cogn. Commun. Netw., vol. 10, no. 2, pp. 541-552, Apr. 2024.], the UAV sends sensing signals to ground sensing targets to collect sensing data and then transmits it to a base station with a server on the ground. In this paper, it is considered that the UAV hovers in the air for information transmission and reception, and the communication sensing channel is a Rice channel.

[0008] The above solutions have the following problems:

[0009] 1. The security research on integrated communication and sensing has not been applied to the UAV communication scenario, and only the communication mode between a ground duplex base station and ground users has been considered.

[0010] 2. The problem of secure transmission has not been considered in the UAV communication scenario.

[0011] 3. In the multi-user mobile edge computing scenario, the user scheduling strategy has not been considered.

[0012] This is the case for the patent with application number 202411217273.1 and patent name "A Mobile Edge Computing System, Its Optimization Model Modeling Method and Its Optimization Method". By constructing a mobile edge computing system consisting of N edge servers and M mobile devices, and comprehensively considering the channel gain between the edge server and the mobile device, the signal-to-noise ratio of the edge server and the mobile device, the data transmission rate of the edge server and the mobile device, the total delay and total energy consumption of edge computing, and the task cost of edge computing, an optimization problem model is constructed to solve the problem that the existing technology is not applicable to the environment of multiple edge servers and multiple mobile devices. However, this patent does not consider the security of communication, and the server is fixed at a certain point, lacking deployment flexibility.

[0013] How to ensure secure communication, give full play to the flexibility of UAV deployment, and minimize user energy consumption as much as possible is the main problem to be solved in this patent. Summary of the Invention

[0014] Aiming at the above problems, the purpose of the present invention is to solve a part of the problems in the prior art, or at least alleviate these problems.

[0015] A method for designing a joint communication and sensing waveform and UAV trajectory for secure mobile edge computing, including:

[0016] S1: Construct a communication system model:

[0017] Set the UAV S as the edge server base station, and at the same time initialize the flight trajectory of S so that it serves K users existing on the ground; the UAV adopts a user scheduling strategy, and at the same time considers the existence of a potential eavesdropper E. To ensure the security of information transmission, the UAV S emits radar waves to interfere with E to achieve secure mobile edge computing.

[0018] S2: Construct an optimization mathematical model:

[0019] With the goal of minimizing the energy consumption of ground users, construct an optimization mathematical model with constraints on user offloading ratio, user scheduling, joint communication and sensing waveform, and UAV flight trajectory.

[0020] S3: For the optimization mathematical model, based on the BCD method, decouple and synthesize sub-problems only about user offloading ratio, user scheduling, joint communication and sensing waveform, and UAV flight trajectory, and for each non-convex sub-problem, use a convex approximation fitting method to convert it into a convex problem for solution; among them, when solving the non-convex problem about the joint communication and sensing waveform, use the semi-definite relaxation method (SDR) that abandons the rank-one constraint to convert the non-convex problem into a convex problem, then use the convex optimization solver CVX toolbox for solution, and finally obtain the solution that satisfies the rank-one constraint through Gaussian randomization.

[0021] S4: Solve for the user scheduling, offloading ratio, integrated communication and sensing waveform, and UAV flight trajectory using an iterative algorithm.

[0022] The optimization mathematical model is as follows:

[0023]

[0024] In the formula represents the set of optimization variables, where α k [n] is the offloading ratio of user k at time slot n, θ k [n] is the user scheduling situation of the UAV, w[n] is the integrated communication and sensing waveform, q s [n] is the UAV trajectory, Ε k is the energy consumption of user k, represents the starting and ending positions of the UAV, δ t defines the length of each flight time slot of the UAV, V max defines the maximum flight speed of the UAV at any time, T represents the total flight time of the UAV, respectively represent the time for user k to calculate the remaining data locally, the time for user k to offload data, and the time for the UAV server to calculate the data offloaded by user k, γ sk and γ ek are the signal-to-interference-plus-noise ratio of the UAV receiving user k and the signal-to-interference-plus-noise ratio of the eavesdropper receiving user k respectively, Γ s and Γ e and Γ sen define the UAV communication performance threshold, eavesdropping threshold, and radar beam pattern gain threshold respectively, P[n] represents the radar beam pattern gain, defines the square of the distance from the UAV to the eavesdropper at time slot n, P max and Ε UAV and Ε max define the maximum transmission power of the UAV, the total energy consumption of the UAV, and the maximum energy of the UAV respectively;

[0025] Among them, C1 - C4 are the constraints on user scheduling and offloading ratio, C5 - C6 limit the starting and ending positions and maximum flight distance of the UAV, C7 - C8 are the constraints on the time for calculating data and offloading data, C9 - C10 are the signal-to-noise ratio constraints of the UAV and the eavesdropper, which are the requirements for secure transmission; C11 - C12 are the constraints on the integrated communication and sensing waveform, requiring the radar signal to interfere with the eavesdropper, but the power cannot exceed the maximum value; C13 is the energy consumption constraint of the UAV, which limits the total energy consumption of the UAV for flight, calculation, and transmitting radar signals.

[0026] Perform a decoupling operation on the mathematical model to obtain the sub-problem as follows:

[0027]

[0028] Among them, represents the energy consumption of the user for calculating the data of the non-unloaded part, represents the energy consumption when the user performs offloading; this problem is a standard convex optimization problem and can be solved by the interior point method;

[0029]

[0030] Among them, represents the energy consumption when the user performs local computing; this problem is a standard convex optimization problem and can be solved by the interior point method;

[0031]

[0032] Among them, Tr represents the operation of finding the trace of a matrix, represents the operation of finding the expectation; represents the integrated sensing and communication beam matrix;

[0033]

[0034] Furthermore, for the sub-problem define the objective function as minimizing the eavesdropping signal-to-noise ratio and introduce an auxiliary variable η k carry out optimization and improvement to obtain the following mathematical model:

[0035]

[0036] Among them, H se [n] represents the channel coefficient matrix from the UAV to the eavesdropper, represents the noise power received by the eavesdropper, and g i represents the channel coefficient from the eavesdropper to the i-th user; when ignoring the rank-one constraint of C6, the entire convex optimization problem is a semidefinite programming (SDP) problem and can be solved by the SDR optimization method, and then use Gaussian randomization to process this non-rank-one solution to obtain an approximate solution that satisfies the rank-one condition.

[0037] Furthermore, for the sub-problem adopt the following steps for optimization and improvement:

[0038] By introducing the following four required auxiliary variables:

[0039]

[0040]

[0041] Among them, the fourth one is a non-convex constraint, which is rewritten by SCA to obtain:

[0042]

[0043] Among them, v 0 represents the average rotor induced velocity during UAV hovering, H is the flight altitude of the UAV, q k and q e are respectively defined as the coordinate position of the user and the coordinate position of the eavesdropper. represents the position of the UAV at the appropriate m-th time slot, v 1 [n] and v 2 [n] represent two introduced auxiliary variables;

[0044] It is rewritten by the Successive Convex Approximation (SCA) method to obtain:

[0045]

[0046] Among them, represents taking the partial derivative of ;

[0047] For other non-convex constraints, the same method is adopted to obtain:

[0048]

[0049] Among them, defines the noise power of the signal received by the UAV, p k is the transmission power of the user, β 0 represents the channel gain at a reference distance of 1 meter, D k defines the total amount of data of the user, B represents the channel bandwidth, τ k [n] is an introduced auxiliary variable, defines the propulsion power of the UAV after introducing the auxiliary variable, P 0 and P i are respectively the profile power and the induced power in the UAV hovering state, U tip represents the tip speed of the rotor blade; d 0 , ρ, s and A are respectively the fuselage drag ratio, air density, rotor solidity and rotor disk area; X[n], X k1 [n], X k3 [n] are newly introduced slack variables, which need to satisfy:

[0050]

[0051]

[0052] Based on the above transformation, a new optimization problem can be obtained, which is expressed as:

[0053]

[0054] where represents the optimization variable, and \(c\) k [n], \(\gamma\) 0 , \(X\) k [n] are introduced auxiliary variables; the problem is a standard convex optimization problem, which is solved using the convex optimization solver CVX.

[0055] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the integrated communication and sensing waveform and UAV trajectory design method for realizing secure mobile edge computing are implemented.

[0056] The unit for realizing the integrated communication and sensing waveform and UAV trajectory of secure mobile edge computing includes:

[0057] An initialization module sets the UAV S as the edge server base station, and simultaneously initializes the flight trajectory of S so that it serves K users existing on the ground. In order to better reduce the energy consumption of users, the UAV adopts a user scheduling strategy. At the same time, considering the existence of a potential eavesdropper E, in order to ensure the security of information transmission, the UAV emits radar waves to interfere with E to achieve secure mobile edge computing;

[0058] A mathematical model module constructs an optimization mathematical model with user scheduling, user offloading ratio, integrated communication and sensing waveform, and UAV flight trajectory as constraints with the goal of minimizing the energy consumption of ground users;

[0059] A mathematical model decoupling module is used to decouple the mathematical model based on the BCD method into sub-problems only about the offloading ratio, user scheduling, integrated communication and sensing waveform, and UAV flight trajectory. For each non-convex sub-problem, it is converted into a convex problem for solution by using a convex approximation fitting method;

[0060] A sub-problem solving module is used to solve the offloading ratio, user scheduling, integrated communication and sensing waveform, and UAV flight trajectory by using an iterative algorithm.

[0061] The present invention has the following beneficial effects:

[0062] The present invention considers the existence of a potential eavesdropper and sends the communication and sensing integrated waveform as a strong interference signal to the potential eavesdropper to interfere with its eavesdropping behavior. At the same time, considering that the server is carried by the UAV, the user can offload the data to the server for calculation to give full play to the advantages of the flexible deployment and high mobility of the UAV. Therefore, by optimizing sub-problems 1.1 and 2.1, the optimal user offloading ratio and user scheduling can be obtained, and a strategy with the minimum user energy consumption can be obtained; by optimizing the improved sub-problem 3.2, the optimal communication and sensing integrated beamforming can be obtained, and the eavesdropping signal-to-noise ratio of the eavesdropper can be reduced to the lowest level to achieve the goal of secure communication; by optimizing the improved sub-problem 4.2, the optimal UAV flight trajectory can be obtained, better channel conditions can be obtained, and the user energy consumption can be further reduced; finally, by cyclically optimizing the four sub-problems until convergence, a local optimal solution and a global sub-optimal solution, that is, the minimum user energy consumption, can be obtained. The present invention combines the flexible and mobile characteristics of the UAV with the characteristics that the strong radar signal can be used as interference to construct a UAV-assisted communication and sensing integrated communication system, and designs the communication and sensing integrated waveform and the UAV flight trajectory through theoretical derivation and simulation verification to achieve the minimization of user power. Description of the Drawings

[0063] Figure 1 It is the communication system model of the present invention;

[0064] Figure 2 It is the flow block diagram of the operation of the present invention

[0065] Figure 3 It is the optimal flight trajectory diagram of the UAV;

[0066] Figure 4 It is the algorithm iteration convergence diagram;

[0067] Figure 5 It is the user scheduling strategy diagram;

[0068] Figure 6 It is the simulation beam gain diagram, where (a) in the figure is the beam gain diagram about the x-axis, and (b) is the beam gain diagram about the y-axis. Detailed Embodiment

[0069] The following further describes the present invention in conjunction with the drawings. The embodiments of the present invention are only used to illustrate the present invention rather than limit the present invention. Without departing from the technical idea of the present invention, various substitutions and changes made according to the common general knowledge and conventional means in the art shall be included within the scope of the present invention.

[0070] As Figure 2 shown, a method for designing a communication and sensing integrated waveform and a UAV trajectory for realizing secure mobile edge computing includes:

[0071] S1: Construct a communication system model.

[0072] Set the drone S as the edge server base station, and at the same time initialize the flight trajectory of S so that it serves K users existing on the ground; in order to better reduce the energy consumption of users, the drone adopts a user scheduling strategy, and at the same time considers the existence of a potential eavesdropper E. To ensure the security of information transmission, the drone S emits radar waves to interfere with E to achieve secure mobile edge computing.

[0073] As Figure 1 shown, in this invention, a joint communication and sensing waveform and drone trajectory design method for realizing secure mobile edge computing is mainly studied. The drone S flies from the starting point to the ending point, carrying an edge computing server and receiving the information uploaded by K users on the ground. Considering that the drone knows the specific location information of all users and the eavesdropper, the two-dimensional position coordinates of the eavesdropper are q e = [x e , y e T , the two-dimensional position coordinates of the k-th user are q k = [x k , y k T , the horizontal position coordinates of the drone at the n-th time slot are q s [n] = [x s [n], y s [n]] T , the Euclidean distance from the drone to the k-th user is expressed as And the Euclidean distance from the drone to the eavesdropper is The drone flies at a fixed height H, and the transmitter is equipped with a uniform planar array antenna (UPA) of M = M x × M y to send radar sensing signals, and is also equipped with a single antenna to receive signals.

[0074] The conjugate transpose of the radar transmitting array steering vector can be expressed as:

[0075]

[0076] Where and depend on the vertex and the relevant azimuth angle of the angle of departure of the signal from the drone to the eavesdropper. d x and d y are the spacings of the antennas along the x-axis and y-axis respectively, taking half-wavelength M x represents the number of antennas on the x-axis, M y represents the number of antennas on the y-axis, and j is the imaginary symbol. Among them, Kronecker product operation, () H Denotes the conjugate transpose operation of a matrix.

[0077] The channel from the UAV S to user k can be expressed as:

[0078]

[0079] The channel from the UAV S to the eavesdropper can be expressed as:

[0080]

[0081] The channel from the eavesdropper to user k can be expressed as:

[0082]

[0083] β 0 Denotes the channel gain at a reference distance of 1 meter.

[0084] w[n]s represents the radar signal transmitted by the UAV, where represents the radar signal, which follows a Gaussian random distribution with a mean of 0 and a variance of 1. represents the integrated communication and sensing waveform. Assuming that the UAV has the technology to eliminate self-interference, the information received by the UAV can be expressed as:

[0085]

[0086] The information received by the eavesdropper can be expressed as:

[0087]

[0088] where, d k represents the information sent by user k. is white noise that follows a Gaussian distribution. At this time, the received signal SINR of the UAV can be expressed as:

[0089]

[0090] The received signal SINR of the eavesdropper can be expressed as:

[0091]

[0092] where, P k is the transmit power of user k, h i [n] represents the channel coefficient from the UAV to the i-th user, g i represents the channel coefficient from the eavesdropper to the i-th user. represents the conjugate transpose of the channel coefficient from the UAV to the eavesdropper. defines the noise power of the signal received by the UAV.

[0093] S2: Construct an optimization mathematical model.

[0094] With the goal of minimizing the energy consumption of ground users, an optimization mathematical model is constructed with user scheduling, user offloading ratio, integrated communication and sensing waveform, and UAV flight trajectory as constraints.

[0095] The optimization mathematical model is as follows:

[0096]

[0097] In the formula represents the set of optimization variables, where α k [n] is the offloading ratio of user k at time slot n, θ k [n] is the user scheduling situation of the UAV, w[n] is the integrated communication and sensing waveform, q s [n] is the UAV trajectory, Ε k is the energy consumption of user k, represents the starting and ending positions of the UAV, δ t defines the length of each flight time slot of the UAV, V max defines the maximum flight speed of the UAV at any time, T represents the total flight time of the UAV, respectively represent the time for user k to calculate the remaining data locally, the time for user k to offload data, and the time for the UAV server to calculate the data offloaded by user k, γ sk 、γ ek are respectively the signal-to-interference-plus-noise ratio of the UAV receiving user k and the signal-to-interference-plus-noise ratio of the eavesdropper receiving user k, Γ s 、Γ e 、Γ sen respectively define the UAV communication performance threshold, the eavesdropping threshold, and the radar beam pattern gain threshold, P[n] represents the radar beam pattern gain, defines the square of the distance from the UAV to the eavesdropper at time slot n, P max 、Ε UAV 、Ε max respectively define the maximum transmission power of the UAV, the total energy consumption of the UAV, and the maximum energy of the UAV;

[0098] Among them, C1 to C4 are the constraints on the user scheduling and offloading ratios, C5 to C6 limit the starting and ending positions and the maximum flight distance of the UAV, C7 to C8 are the constraints on the calculation data and offloading data times, and C9 to C10 are the signal-to-noise ratio constraints of the UAV and the eavesdropper, which are the requirements for secure transmission; C11 to C12 are the constraints on the integrated communication and sensing waveform, requiring the radar signal to interfere with the eavesdropper, but the power cannot exceed the maximum value; C13 is the energy consumption constraint of the UAV, which limits the total energy consumption of the UAV for flight, calculation, and transmitting radar signals.

[0099] The beam pattern gain P[n] of the radar can be expressed as:

[0100] P[n] = a H (q s [n], q e )Q[n]a(q s [n], q e )

[0101] Among them, w H [n] represents the conjugate transpose of the integrated communication and sensing waveform, represents the operation of taking the expectation.

[0102] The energy consumption of the UAV can be expressed as:

[0103]

[0104] Among them, Tr represents the operation of taking the trace of the matrix, represents the energy consumption of the UAV for calculating the data uploaded by user k at time slot n, P fly represents the propulsion power of the UAV.

[0105] S3: For the optimization mathematical model, based on the BCD method, decouple and synthesize sub-problems only related to user scheduling, offloading ratio, integrated communication and sensing waveform, and UAV flight trajectory, and for each non-convex sub-problem, use the convex approximation fitting method to convert it into a convex problem for solution; among them, when solving the non-convex problem about the integrated communication and sensing waveform, use the SDR method that abandons the rank-one constraint to convert the non-convex problem into a convex problem, then use the CVX toolbox for solution, and finally obtain the solution that satisfies the rank-one constraint through Gaussian randomization.

[0106] Regarding the non-convexity and high coupling of the optimization problem corresponding to the system model, an approximate fitting method based on the Block Coordinate Descent (BCD) algorithm and successive convex approximation is used to decouple and transform the original optimization problem. The solution to the problem of the present invention will be based on the following steps, and the specific steps are as follows:

[0107] Based on the Block Coordinate Descent (BCD) algorithm, the problem is decoupled to obtain sub-problems as follows:

[0108]

[0109] where, represents the energy consumption of the user for calculating the data that has not been offloaded, represents the energy consumption when the user performs offloading; this problem is a standard convex optimization problem and can be solved by the interior point method.

[0110]

[0111] where, represents the energy consumption when the user performs local computing; this problem is a standard convex optimization problem and can be solved by the interior point method.

[0112] Let represent the integrated sensing and communication beam matrix.

[0113]

[0114] where, Tr represents the operation of finding the trace of a matrix, represents the operation of finding the expectation.

[0115]

[0116] For problem and problem both are standard convex optimization problems and can be solved using the interior point method. For problem and problem the Successive Convex Approximation (SCA) method needs to be used to convexly approximate some non-convex constraints, so that the original problem becomes a convex optimization problem that can be solved.

[0117] For sub-problem Since the radar signal does not directly affect the user's energy consumption, but indirectly improves the received signal-to-noise ratio of the UAV by interfering with the eavesdropper, thereby reducing energy consumption. Therefore, the objective function can be defined as minimizing the eavesdropping signal-to-noise ratio, expanding the solution space of UAV trajectory optimization, and introducing an auxiliary variable η k , and the mathematical model is:

[0118]

[0119] When ignoring the rank-one constraint of C6, the entire convex optimization problem is a Semidefinite Programming (SDP) problem and can be solved using the SDR optimization method, and then the Gaussian randomization is used to process this non-rank-one solution to obtain an approximate solution that satisfies the rank-one condition.

[0120] For the sub - problem The following steps are taken for optimization and improvement:

[0121] By introducing the following four auxiliary variables that meet the requirements:

[0122]

[0123] Among them, the fourth one is a non - convex constraint, which is rewritten by SCA to obtain:

[0124]

[0125] Among them, v 0 represents the average rotor induced velocity during UAV hovering, H is the flight altitude of the UAV, q k , q e are respectively defined as the coordinate positions of the user and the eavesdropper.

[0126] Since the array steering vector a H (q s [n], q e ) is not affine, which leads to some non - convex constraints, so it is rewritten by the SCA method to obtain:

[0127]

[0128] Among them, can be detailed as the following eight expressions:

[0129]

[0130] For other non - convex constraints, the same method is adopted to obtain:

[0131]

[0132]

[0133] Among them, defines the noise power of the signal received by the UAV, p k is the transmission power of the user, β 0 represents the channel gain at a reference distance of 1 meter, D k defines the total amount of data of the user, P 0 and P i are respectively the profile power and induced power in the UAV hovering state, U tip represents the tip speed of the rotor blade; d 0 , ρ, s and A are respectively the fuselage drag ratio, air density, rotor solidity and rotor disk area; X[n], X k1[n], X k3 [n] is a newly introduced slack variable, which needs to satisfy:

[0134]

[0135] Based on the above transformation, a new optimization problem can be obtained, which is expressed as:

[0136]

[0137] Among them, represents the optimization variable; the problem is a standard convex optimization problem, and the convex optimization solver CVX is used to solve it.

[0138] S4: Use the iterative algorithm to solve the user scheduling, offloading ratio, integrated communication and sensing waveform, and UAV flight trajectory.

[0139] Design the iterative algorithm process, serially connect the above convex optimization problems, and the specific steps are shown in Table 1.

[0140] Table 1

[0141]

[0142] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps of the method for designing the integrated communication and sensing waveform and UAV trajectory for secure mobile edge computing.

[0143] The unit for implementing the integrated communication and sensing waveform and UAV trajectory for secure mobile edge computing includes:

[0144] Initialization module, set the UAV S as the edge server base station, and at the same time initialize the flight trajectory of S, so that it serves K users existing on the ground. In order to better reduce the energy consumption of users, the UAV adopts a user scheduling strategy, and at the same time considers the existence of a potential eavesdropper E. In order to ensure the security of information transmission, the UAV emits radar waves to interfere with E to achieve secure mobile edge computing;

[0145] Mathematical model module, aiming at minimizing the energy consumption of ground users, constructs an optimization mathematical model with user scheduling, user offloading ratio, integrated communication and sensing waveform, and UAV flight trajectory as constraints;

[0146] Mathematical model decoupling module, used to decouple and synthesize the mathematical model into sub-problems only about the offloading ratio, user scheduling, integrated communication and sensing waveform, and UAV flight trajectory based on the BCD method. For each non-convex sub-problem, it is converted into a convex problem for solution by using a convex approximation fitting method;

[0147] The sub - problem solving module is used to solve the offloading ratio, user scheduling, integrated communication - sensing waveform, and UAV flight trajectory by using an iterative algorithm.

[0148] Figure 1 is the communication system model diagram of the present invention;

[0149] Figure 2 is the flowchart of the iterative algorithm proposed by the present invention to solve the optimization problem. Its detailed process corresponds to the specific solution steps of the above - mentioned optimization problem.

[0150] Figure 3 is the simulation verification of the present invention based on the proposed solution. Through Figure 2 the process, finally obtain Figure 3 the optimal flight trajectory of the UAV shown.

[0151] Figure 4 simulates and verifies the convergence of the solution of the present invention. It can be seen from the simulation diagram that the algorithm proposed by the solution of the present invention is effective.

[0152] Figure 5 simulates and verifies the user scheduling strategy of the present invention. It can be seen from the simulation diagram that the user scheduling strategy of the present invention first selects User 1, then User 2, and finally User 3. According to the relationship between the positions of users and the UAV trajectory, the rationality of the user scheduling strategy can be seen.

[0153] Figure 6 is the simulation verification of the present invention based on the proposed solution. In the figure, (a) is the beam gain diagram about the x - axis, and (b) is the beam gain diagram about the y - axis. It can be seen from the simulation diagram that the beam gain diagram of the present invention. It can be seen from this figure that at the positions of the x - axis and y - axis where the eavesdropper is located, the beam pattern gain is the largest. It conforms to the design of the proposed solution.

[0154] This application proposes a design method for integrated communication - sensing waveform and UAV trajectory to achieve secure mobile edge computing. With the goal of minimizing user energy consumption, it dynamically plans and designs the offloading ratio of users, user scheduling strategies, integrated communication - sensing waveforms, and UAV trajectories.

[0155] In step one, first, according to the basic theories of wireless communication and physical - layer security, a UAV - assisted wireless communication system model is constructed.

[0156] Step 2 and Step 3 improve the mathematical theory derivation and problem-solving analysis in the model building process. Aiming at the non-convexity and high coupling of the optimization problem corresponding to the system model, an approximate fitting method based on the Block Coordinate Descent (BCD) algorithm and successive convex approximation is adopted to decouple and transform the original optimization problem. (For the specific process, please refer to the specific implementation manners of the present invention).

[0157] In Step 4, based on the iterative idea, the sub-problems obtained in Step 2 and Step 3 are iterated in a loop, so that the total user energy consumption continuously approaches a fixed value, and this fixed value is the minimum total user energy consumption required by the present invention.

[0158] The present invention first constructs a system model, including a communication model, a sensing model, and a computing model, then proposes an optimization problem model, and uses the Block Coordinte Descent (BCD) method to decouple the original optimization problem into sub-problems regarding the integrated communication and sensing waveform and the UAV flight trajectory. For each non-convex sub-problem, convex approximation fitting is performed, and finally an iterative algorithm is used to solve the problem, so as to find the global sub-optimal solution of the entire optimization problem and the optimal integrated communication and sensing waveform and the UAV flight trajectory.

[0159] Users can offload data to the edge computing server carried by the UAV to reduce energy consumption. At the same time, the UAV emits strong radar signals to interfere with potential eavesdroppers, thus achieving the goal of secure mobile edge computing.

[0160] The present invention is mainly applied to the UAV-assisted wireless communication scenario. Aiming at the scenario where users have tight resources, need to process time-sensitive tasks, and there are also potential eavesdroppers affecting the quality and security of wireless communication, the characteristics of the flexible mobility of the UAV are combined with the characteristics that strong radar signals can be used as interference to construct a UAV-assisted integrated communication and sensing communication system. Through theoretical derivation and simulation verification, an integrated communication and sensing waveform and a UAV flight trajectory are designed to minimize the user power. The present invention utilizes the flexible deployment characteristics of the UAV and simultaneously sends strong radar signals to interfere with potential eavesdroppers to achieve the goal of secure mobile edge computing. Combining the UAV with the integrated communication and sensing has high innovation and originality.

Claims

1. A synaesthesia-integrated waveform and drone trajectory design method for implementing secure mobile edge computing, characterized in that: include: S1: Build a communication system model: Set the drone S as the edge server base station and initialize the flight trajectory of S to serve K users on the ground. The drone adopts a user scheduling strategy and considers the existence of a potential eavesdropper E. In order to ensure the security of information transmission, the drone S emits radar waves to interfere with E to achieve secure mobile edge computing. S2: Construct optimization mathematical model: With the goal of minimizing the energy consumption of ground users, an optimization mathematical model with user unloading ratio, user scheduling, synaesthesia integrated waveform and UAV flight trajectory as constraints is constructed; S3: For the optimization mathematical model, the BCD method is used to decouple the model into sub-problems related to user offloading ratio, user scheduling, synaesthesia integrated waveform and UAV flight trajectory, and each non-convex sub-problem is converted into a convex problem by using a convex approximation fitting method for solution; when solving the non-convex problem about the synaesthesia integrated waveform, the semi-definite relaxation method (SDR) with rank-one constraint discarded is used to convert the non-convex problem into a convex problem, and then the convex optimization solution tool CVX is used to solve the problem, and finally the solution is obtained by Gaussian randomization to obtain a solution that satisfies the rank-one constraint; S4: Use iterative algorithms to solve user scheduling, unloading ratio, synaesthesia integrated waveform, and UAV flight trajectory.

2. The method for designing synaesthesia-integrated waveforms and drone trajectories for implementing secure mobile edge computing according to claim 1, characterized in that: The optimization mathematical model is: C13:E UAV ≤E max In the formula represents the set of optimization variables, where α k [n] is the offloading ratio of user k in time slot n, θ k [n] is the user dispatching status of the drone, w[n] is the synaesthesia integrated waveform, q s [n] is the drone trajectory, Ε k is the energy consumption of user k, represents the starting and ending positions of the drone, δ t Defines the length of each flight time slot of the drone, V max It defines the maximum flight speed of the drone at any time, and T represents the total flight time of the drone. They represent the time for user k to calculate the remaining data locally, the time for user k to unload data, and the time for the drone server to calculate the data unloaded by user k, γ sk , γ ek are the signal-to-interference-noise ratio of the drone receiving user k and the signal-to-interference-noise ratio of the eavesdropper receiving user k, Γ s , Γ e , Γ sen The UAV communication performance threshold, eavesdropping threshold and radar beam pattern gain threshold are defined respectively. P[n] represents the radar beam pattern gain, d e 2 [n] is defined as the square of the distance from the drone to the eavesdropper in time slot n, P max , UAV , max The maximum transmission power of the UAV, the total energy consumption of the UAV and the maximum energy of the UAV are defined respectively; Among them, C1~C4 are constraints on user scheduling and unloading ratios, C5~C6 limit the starting and ending positions of the UAV flight and the maximum flight distance, C7~C8 are constraints on the calculation and unloading data time, C9~C10 are constraints on the signal-to-noise ratio of the UAV and the eavesdropper, which are requirements for secure transmission; C11~C12 are constraints on the synaesthesia integrated waveform, requiring that the radar signal has an interference effect on the eavesdropper, but the power cannot exceed the maximum value; C13 is the energy consumption constraint of the UAV, which limits the total energy consumption of the UAV flying, calculating and transmitting radar signals.

3. The method for designing synaesthesia-integrated waveforms and drone trajectories for implementing secure mobile edge computing according to claim 2, characterized in that: Decoupling the mathematical model, we get the subproblem As shown below: in, It indicates the energy consumption of the data that the user calculates without uninstalling. represents the energy consumption when the user is unloading; this problem is a standard convex optimization problem and can be solved by the interior point method; in, represents the energy consumption when the user performs local computing; this problem is a standard convex optimization problem and can be solved using the interior point method; Among them, Tr represents the operation of finding the matrix trace, Indicates the desired operation; represents the synaesthesia integration beam matrix; C7:E UAV ≤E max。 4. The method for designing synaesthesia-integrated waveforms and drone trajectories for implementing secure mobile edge computing according to claim 3, characterized in that: For sub-problems The objective function is defined as minimizing the eavesdropping signal-to-noise ratio while introducing the auxiliary variable η k After optimization and improvement, the following mathematical model is obtained: C4:η k [n]≥γ ek [n] C6:rank(Q[n])=1 Among them, H se [n] represents the channel coefficient matrix from the drone to the eavesdropper, represents the noise power received by the eavesdropper, g i represents the channel coefficient from the eavesdropper to the i-th user; when the rank-one constraint C6 is ignored, the entire convex optimization problem is a semidefinite programming (SDP) problem, which can be solved by the SDR optimization method, and then the non-rank-one solution is processed by Gaussian randomization to obtain an approximate solution that satisfies the rank-one condition.

5. The method for designing synaesthesia-integrated waveforms and drone trajectories for implementing secure mobile edge computing according to claim 3, characterized in that: For sub-problems The following steps are used for optimization and improvement: By introducing the following four auxiliary variables that meet the needs: Among them, the fourth one is a non-convex constraint, which can be rewritten through SCA to obtain: Where v0 represents the average rotor induced speed of the UAV when it is hovering, H is the flight altitude of the UAV, and q k ,q e are defined as the user's coordinate position and the eavesdropper's coordinate position respectively, represents the position of the UAV at the appropriate mth time slot, v1[n] and v2[n] represent the two auxiliary variables introduced; Rewrite it using the continuous convex approximation (SCA) method to get: in, express right Find partial derivatives; For other non-convex constraints, the same method is used to obtain: in, The noise power of the signal received by the drone is defined as p k is the user's transmit power, β0 is the channel gain when the reference distance is 1 meter, and D k defines the total amount of data for the user, B represents the channel bandwidth, τ k [n] is an auxiliary variable introduced, It defines the propulsion power of the UAV after the introduction of auxiliary variables, P0 and P i are the blade power and induced power of the UAV in hovering state, U tip represents the tip speed of the rotor blade; d0, ρ, s and A are the fuselage drag ratio, air density, rotor solidity and rotor disk area respectively; X[n], X k1 [n], X k3 [n] is a newly introduced slack variable that needs to satisfy: Based on the above transformation, a new optimization problem can be obtained, which can be expressed as: in, represents the optimization variable, c k [n],γ0,X k [n] is an auxiliary variable introduced; question It is a standard convex optimization problem, which is solved using the convex optimization solving tool CVX.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for designing synaesthesia-integrated waveforms and drone trajectories for implementing secure mobile edge computing as described in any one of claims 1 to 5 are implemented.

7. A unit for realizing synaesthesia integration of waveforms and drone trajectories for secure mobile edge computing, characterized in that, include: Initialization module, setting drone S as the edge server base station, and initializing the flight trajectory of S so that it can serve K users on the ground. In order to better reduce the energy consumption of users, the drone adopts a user scheduling strategy. At the same time, considering the existence of a potential eavesdropper E, in order to ensure the security of information transmission, the drone emits radar waves to interfere with E, realizing secure mobile edge computing; The mathematical model module aims to minimize the energy consumption of ground users and constructs an optimization mathematical model with user scheduling, user unloading ratio, synaesthesia integrated waveform and UAV flight trajectory as constraints; The mathematical model decoupling module is used to decouple the mathematical model into sub-problems related to unloading ratio, user scheduling, synaesthesia integrated waveform and UAV flight trajectory based on the BCD method. For each non-convex sub-problem, a convex approximation fitting method is used to convert it into a convex problem for solution; The sub-problem solving module is used to solve the unloading ratio, user scheduling, synaesthesia integrated waveform and UAV flight trajectory using iterative algorithms.

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