Unmanned aerial vehicle air calculation method and device based on physical layer security

Abstract

The invention belongs to the technical field of air computing, and particularly discloses an unmanned aerial vehicle air computing method and device based on physical layer security, and the method comprises the steps: constructing an unmanned aerial vehicle air computing model; constructing a mean square error function of the unmanned aerial vehicle and a mean square error function of an eavesdropper; taking minimization of a mean square error at the unmanned aerial vehicle as an optimization target, taking a receiving coefficient of the unmanned aerial vehicle, a transmission coefficient of a sensor and a track of the unmanned aerial vehicle as optimization variables, and applying a constraint to a mean square error function of an eavesdropper to enable the mean square error function to be greater than or equal to a preset threshold value; constructing a target optimization problem; solving the constructed target optimization problem by adopting an alternating optimization algorithm to obtain an optimal transmission coefficient, an optimal receiving coefficient of the sensor and an optimal path of the unmanned aerial vehicle; and transmitting the wireless signal based on the optimal transmission coefficient, the optimal receiving coefficient of the sensor and the optimal path of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of aerial computing, and particularly to a method and device for unmanned aerial vehicle (UAV) aerial computing based on physical layer security. Background Art

[0002] Aerial computing technology utilizes the superposition characteristics of wireless channels to perform calculations of mathematical functions by using the signal superposition characteristics of wireless multiple access channels, and completes the calculations during the transmission process. Multiple nodes access the data center node in a non-orthogonal multiple access manner. The receiving node can receive the transmitted superimposed signal, and by processing this superimposed signal, the required data can be directly obtained. However, in actual situations, the transmission process is affected by noise, and since the power of the wireless signals sent by sensors is limited, the receiving factor, transmission coefficient, and UAV trajectory are constrained and designed under different conditions to control the power of the entire system, so as to obtain the minimum mean square error at the receiving base station, and thus obtain more accurate calculation values and improve the receiving accuracy of wireless signals.

[0003] In recent years, research on aerial computing has focused on the power allocation problem, with the goal of optimizing system performance by minimizing the mean square error. However, the performance of aerial computing is usually limited by the worst channel condition among all the links between users and the fusion center; this also means that even if most links have good channel conditions, the poor quality of individual links may still significantly affect the overall performance. To solve this problem, UAVs can be used as the fusion center.

[0004] UAVs can meet the specific application requirements of low latency, reliable command control, and high-speed data transmission. Related research covers multiple fields, including low-latency communication, reliable command control, high-speed data transmission, spectrum efficiency, and network coordination. In aerial computing, using UAVs as the fusion center has the advantages of flexible deployment and dynamic adjustment, can effectively avoid ground obstacles and interference, and optimize the channel conditions. However, ensuring the security of UAV communication and preventing eavesdroppers from intercepting the messages sent by users to the UAV is a challenging problem.

[0005] In the actual aerial computing transmission process, due to the broadcast nature of wireless channels, there will inevitably be malicious eavesdropping nodes during the signal transmission process. The eavesdropping nodes steal the messages sent by sensors, resulting in a high risk of signal eavesdropping during the transmission process.

[0006] Traditional communication technologies usually adopt the "communicate first, then calculate" mode, that is, first transmit the data in dispersed terminal devices to the central server through a wireless network, and then complete centralized computing on the server; this method completely separates the communication and computing processes, which can meet the requirements in early simple application scenarios, but with the increase of large-scale device access and complex intelligent tasks, its limitations become more and more obvious. Summary of the Invention

[0007] In order to overcome the deficiencies of the prior art, the present invention provides a UAV air computing method based on physical layer security. The UAV air computing method jointly optimizes the receiving coefficient of the UAV, the transmission coefficient of the sensor, and the trajectory of the UAV, so as to improve the quality of the wireless signal received by the UAV, while ensuring secure communication and improving the security of wireless signal transmission.

[0008] The second object of the present invention is to provide a UAV air computing device based on physical layer security.

[0009] The technical solution of the present invention to solve the above technical problems is:

[0010] A UAV air computing method based on physical layer security includes the following steps:

[0011] Step S1: Construct a UAV air computing model including a UAV, a sensor, and an eavesdropper;

[0012] Step S2: According to the wireless signal received by the UAV, construct the mean square error function of the UAV; according to the wireless signal received by the eavesdropper, construct the mean square error function of the eavesdropper;

[0013] Step S3: Taking the minimization of the mean square error at the UAV as the optimization objective, taking the receiving coefficient a0[n] of the UAV, the transmission coefficient b k [n] of the sensor, and the trajectory q[n] of the UAV as optimization variables, and imposing constraints on the mean square error function of the eavesdropper to make it greater than or equal to a preset threshold, so as to construct the target optimization problem;

[0014] Step S4: Use the alternating optimization algorithm to solve the constructed target optimization problem to obtain the optimal transmission coefficient of the sensor, the optimal receiving coefficient of the sensor, and the optimal path of the UAV;

[0015] Step S5: Transmit the wireless signal based on the optimal transmission coefficient of the sensor, the optimal receiving coefficient of the UAV, and the optimal path of the UAV.

[0016] Furthermore, in step S1, the UAV air computing model consists of a UAV, K sensors, and an eavesdropper. Each sensor is equipped with a single antenna, and the eavesdropper is also equipped with a single antenna. Each sensor is used to send wireless signals to the UAV, and the UAV receives the superimposed signals of all the wireless signals sent by the sensors. At the same time, the eavesdropper can also intercept the wireless signals sent by the sensors.

[0017] Assume that the flight time of the UAV is T, and the flight time T is divided into n ∈ Ν = {1, 2,..., N} time slots. Then each time slot is Assume that the flight altitude of the UAV remains unchanged at H0, and the horizontal position of the UAV is represented as q[n] = [x[n], y[n]]. T ; Then the following constraints are imposed on the UAV's trajectory:

[0018]

[0019] q[0] = q0, q[N + 1] = q F ; (2)

[0020] where: L = Vt s represents the maximum displacement of the UAV within one time slot, where V is the maximum speed of the UAV; q0 and q F represent the initial position and the final position of the UAV, respectively.

[0021] Furthermore, in step S2, the construction steps of the mean square error function of the UAV and the mean square error function of the eavesdropper are as follows:

[0022] Step S201: Calculate the average value of all the wireless signals sent by the sensors:

[0023]

[0024] where, x k [n] represents the wireless signal sent by sensor k;

[0025] Step S202: Calculate the wireless signals received by the UAV and the wireless signals received by the eavesdropper. Among them

[0026] The wireless signal received by the UAV is:

[0027]

[0028] where: b k [n] is the transmission coefficient of the wireless signal sent by sensor k; n0[n] is the additive Gaussian white noise with a mean of 0 and a variance of σ 2 for the UAV, that is, n0[n] ∼ CN(0, σ 2); h k [n] is the channel coefficient from sensor k to the UAV, where,

[0029]

[0030] In the formula: w k = [x k , y k T is the position of sensor k; ρ0 represents the channel gain at the reference distance d0 = 1m between sensor k and the UAV;

[0031] The wireless signal received by the eavesdropper is:

[0032]

[0033] In the formula: n e [n] is the additive white Gaussian noise with a mean of 0 and a variance of σ 2 at the eavesdropper, that is, n e [n] ~ CN(0, σ 2 ); g k [n] is the channel coefficient from the sensor to the eavesdropper, where,

[0034]

[0035] In the formula: w e = [x e , y e T is the position of the eavesdropper; ρ e represents the channel gain at the reference distance d0 = 1m from the eavesdropper to the UAV;

[0036] Step S203: Impose a constraint on the transmission power of sensor k in any time slot n;

[0037]

[0038] In the formula: P max is the maximum transmission power of sensor k;

[0039] Step S204: Obtain the estimation functions of the UAV and the eavesdropper, where,

[0040] The estimation function of the UAV is:

[0041]

[0042] The estimation function of the eavesdropper is:

[0043]

[0044] In the formula: a0[n] and a​​e [n] respectively represent the reception coefficients of the UAV and the eavesdropper;

[0045] Step S205: Obtain the mean square error functions of the UAV and the eavesdropper, where

[0046] The mean square error function of the UAV is:

[0047]

[0048] The mean square error function of the eavesdropper is:

[0049]

[0050] Step S206: Construct an objective optimization problem by using the obtained mean square error functions of the UAV and the eavesdropper.

[0051] Furthermore, in step S3, the constructed objective optimization problem (P0) is:

[0052]

[0053] In the formula: ξ is the difference threshold of the mean square error function of the eavesdropper in each time slot.

[0054] Furthermore, in step S4, the objective optimization problem is decomposed into sub-problem one, sub-problem two, and sub-problem three, and the optimization variables are alternately fixed by the alternating optimization algorithm for iterative solution; where

[0055] The said sub-problem one is: Fix the reception coefficient a0[n] of the UAV / the reception coefficient a e [n], the transmission coefficient b of sensor k k [n] and the trajectory q[n] of the UAV to obtain the optimal solution of a e [n] / a0[n]:

[0056] The said sub-problem two is: Fix the reception coefficient a0[n] of the UAV and the trajectory q[n] of the UAV, and optimize the transmission coefficient b k [n] of sensor k;

[0057] The said sub-problem three is: Fix the reception coefficient a0[n] of the UAV and the transmission coefficient b k [n] of sensor k, and optimize the UAV trajectory q[n].

[0058] Furthermore, the specific solution process of the said sub-problem one is:

[0059] Fix the reception coefficient a0[n] of the UAV, the transmission coefficient b k [n] of sensor k and the UAV trajectory q[n], and by calculating MSEe ({a e [n],b k [n],q[n]})' to obtain the optimal solution of a e [n]:

[0060]

[0061] Substitute equation (12) into MSE e ({a e [n],b k [n],q[n]}) and simplify to get:

[0062]

[0063] Fix the receiving coefficient a e [n] of the eavesdropper, the transmission coefficient b k [n] of sensor k and the trajectory q[n] of the UAV to obtain the optimal solution of a0[n]:

[0064]

[0065] Substitute equation (13) into equation (11), and the original target optimization problem (P0) is rewritten as:

[0066]

[0067] Subsequently, based on the target optimization problem (P1), solve sub-problem two and sub-problem three.

[0068] Furthermore, for sub-problem two, the specific solution process is as follows:

[0069] Fix the receiving coefficient a0[n] at the UAV and the trajectory q[n] of the UAV, and optimize the transmission coefficient b k [n] of sensor k; transform equation (13) into:

[0070]

[0071] For perform the first-order Taylor expansion on b k [n] and simplify to get:

[0072] Therefore, sub-problem two is:

[0073]

[0074] Solve the above sub-problem two.

[0075] Furthermore, for sub-problem three, the specific solution process is as follows:

[0076] Fix the reception coefficient a0[n] of the UAV and the transmission coefficient b k [n] of the sensor k, and optimize the trajectory q[n] of the UAV;

[0077] Expand each term in to obtain:

[0078]

[0079] Introduce auxiliary variables:

[0080] L0 = {l k [n] = ||q[n] - w k || 2 , n ∈ N, k ∈ K}

[0081] Let to obtain

[0082] Let and perform the first-order Taylor expansion of f k [n] with respect to ||q[n] - w k || 2 to obtain the lower bound and after processing the lower bound to obtain:

[0083]

[0084] From the above, we get:

[0085]

[0086] Secondly, for the auxiliary variable L0 = {l k [n] = ||q[n] - w k || 2 , n ∈ N, k ∈ K}, the constraint is l k [n] ≤ ||q[n] - w k || 2 , where ||q[n] - w k || 2 is a convex constraint for q[n], but the original constraint l k [n] ≤ ||q[n] - w k || 2 is a non-convex constraint; similarly, when q (r) [n] is given, the Taylor expansion gives:

[0087] ||q[n] - w k || 2 ≥ ||q (r) [n] - wk || 2 +2(q (r) [n] - w k ) T (q[n] - q (r) [n])

[0088] After arrangement, we get:

[0089] l k [n] ≤ ||q (r) [n] - w k || 2 +2(q (r) [n] - w k ) T (q[n] - q (r) [n]),

[0090] Then the sub - problem three is:

[0091]

[0092] Solve the above sub - problem three.

[0093] Furthermore, in step S4, by using the alternating optimization algorithm to solve the receiving coefficient a0[n] at the UAV, the transmission coefficient b k [n] of sensor k and the trajectory q[n] of the UAV, the solution of the target optimization problem (P1) is obtained. Among them, the optimization process of the alternating optimization algorithm is as follows:

[0094] Step S401: Set the preset difference threshold ξ and the number K of sensors, initialize the transmission coefficient b k [n] of sensor k, substitute and calculate the initial values of a0[n] and a e [n]; Initialize the trajectory q[n] of the UAV, and calculate the initial MSE0({a0[n], b k [n], q[n]}) according to equation (9);

[0095] Step S402: Let t = 1,..., T, and start the t - th round of iteration;

[0096] Step S403: Fix Calculate the optimal solution at this time according to equation (14)

[0097] Step S404: Fix Calculate the optimal solution at this time according to equation (17)

[0098] Step S405: Fix Calculate the optimal solution q at this time according to Equation (19). (t+1) [n];

[0099] Step S406: Given to calculate the mean square error of the UAV, and at the same time calculate the mean square error of the eavesdropper;

[0100] Step S407: Calculate the relative difference between the mean square error of the UAV and the mean square error of the eavesdropper, and determine whether the relative difference is less than the difference threshold ξ;

[0101] If the relative difference is greater than the difference threshold ξ, let t = t + 1, and repeat Step S403 - Step S406;

[0102] If the relative difference is less than the difference threshold ξ, exit the loop and output which is the desired result.

[0103] A UAV air computing device for a UAV air computing method based on physical layer security, comprising:

[0104] Model construction module: used to construct a UAV air computing model;

[0105] Objective function construction module: used to construct an objective optimization function with minimizing the mean square error function at the UAV as the optimization objective and the reception coefficient, transmission coefficient at the UAV, and the trajectory of the UAV as the optimization variables;

[0106] Alternating optimization module: used to solve the constructed objective optimization function by using the alternating optimization algorithm to obtain the optimal solution.

[0107] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0108] 1. The UAV air computing method based on physical layer security of the present invention models the problem of minimizing the mean square error at the UAV by deriving the functional relationship between system parameters such as the reception coefficient of the UAV, the transmission coefficient of the sensor, and the trajectory of the UAV and the mean square error, and uses the alternating optimization algorithm to optimize the transmission coefficient and the trajectory of the UAV, so as to minimize the mean square error to ensure secure information transmission.

[0109] 2. The UAV air computing method based on physical layer security of the present invention uses the UAV as a fusion center, which can effectively avoid ground obstacles and interference and optimize the channel conditions; and can minimize the mean square error at the UAV end by optimizing the transmission coefficient of the sensor and the trajectory of the UAV, so that the UAV can receive as many accurate signals as possible. At the same time, by processing the mean square error at the eavesdropper end, the eavesdropper can only steal as few signals as possible to ensure secure communication.

[0110] 3. The UAV air computing method based on physical layer security of the present invention adopts air computing technology. The sensor sends wireless signals to the UAV, and the UAV can quickly receive the superimposed signals sent by all sensors. However, at the same time, the existence of eavesdroppers needs to be considered, and a threshold is set to enable the eavesdroppers to steal as few signals as possible to ensure secure transmission. In this scenario, the alternating optimization algorithm is used to optimize the transmission coefficient of the sensor, the reception coefficient of the UAV, and the trajectory of the UAV, and the problem of minimizing the mean square error of the UAV is modeled to ensure the signal quality received by the legitimate receiver and reduce the signal quality received by the eavesdropper, thereby improving the security performance of wireless signal transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 It is a schematic flowchart of the UAV air computing method based on physical layer security of the present invention.

[0112] Figure 2 It is a schematic diagram of the UAV air computing model.

[0113] Figure 3 It is a comparison diagram of the minimum mean square error at the UAV at different flight times.

[0114] Figure 4 It is a comparison diagram of the minimum mean square error at the UAV at different transmission powers P.

[0115] Figure 5 It is a structural block diagram of the UAV air computing device based on physical layer security of the present invention.

[0116] Figure 6 It is a schematic structural diagram of the computer device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0117] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0118] Those skilled in the art of the present technology can understand that unless specifically stated, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "including" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more related listed items.

[0119] Those skilled in the art can understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention pertains. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as herein.

[0120] Those skilled in the art can understand that the "client", "terminal", and "terminal device" used herein include both devices with a wireless signal receiver that only has the ability to receive and no ability to transmit, and devices with receiving and transmitting hardware that can conduct two-way communication on a two-way communication link. Such devices can include: cellular or other communication devices such as personal computers, tablet computers, etc., which have a single-line display or a multi-line display or a cellular or other communication device without a multi-line display; PCS (Personal Communications Service), which can combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant), which can include a radio frequency receiver, a pager, Internet / intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; conventional laptop and / or palm computers or other devices, which are conventional laptop and / or palm computers or other devices with and / or including a radio frequency receiver. The "client", "terminal", and "terminal device" used herein can be portable, transportable, installed in a vehicle (air, sea, and / or land), or suitable for and / or configured to operate locally, and / or operate in a distributed manner at any other location on the earth and / or in space. The "client", "terminal", and "terminal device" used herein can also be a communication terminal, an Internet access terminal, a music / video playback terminal, for example, it can be a PDA, a MID (Mobile Internet Device), and / or a mobile phone with music / video playback function, or can also be devices such as a smart TV, a set-top box, etc.

[0121] The hardware referred to by names such as "server", "client", and "service node" in the present invention is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components disclosed by the von Neumann principle, including a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. A computer program is stored in its memory, and the central processing unit loads the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input / output devices to complete specific functions.

[0122] It should be noted that the concept of "server" in the present invention can similarly be extended to apply to the case of a server cluster. According to the network deployment principles understood by those skilled in the art, the various servers should be logically divided. Physically, these servers can either be independent of each other but can be called through an interface, or can be integrated into a single physical computer or a set of computer clusters. Those skilled in the art should understand this flexibility and should not be restricted by this when implementing the network deployment method of the present invention.

[0123] One or several technical features of the present invention, unless expressly specified, can either be deployed on the server and accessed by the client remotely invoking the online service interface provided by the server, or can be directly deployed and run on the client for access.

[0124] The neural network models cited or possibly cited in the present invention, unless expressly specified, can either be deployed on a remote server and remotely invoked on the client, or can be deployed on a client with sufficient device capabilities for direct invocation. In some embodiments, when it runs on the client, its corresponding intelligence can be obtained through transfer learning to reduce the requirements for the client's hardware operating resources and avoid over-occupying the client's hardware operating resources.

[0125] All kinds of data involved in the present invention, unless expressly specified, can either be remotely stored on the server or stored on the local terminal device, as long as it is suitable for being invoked by the technical solution of the present invention.

[0126] Those skilled in the art should be aware that although the various methods of the present invention are described based on the same concept and thus show commonality with each other, these methods can be independently executed unless otherwise specified. Similarly, for each embodiment disclosed by the present invention, they are all proposed based on the same inventive concept. Therefore, for concepts with the same expression, as well as concepts that are only appropriately transformed for convenience although the concept expressions are different, they should be equivalently understood.

[0127] For each of the embodiments to be disclosed in the present invention, unless explicitly stated as mutually exclusive, the relevant technical features involved in each embodiment can be cross - combined to flexibly construct new embodiments, as long as such combination does not deviate from the inventive spirit of the present invention and can meet the requirements in the prior art or solve certain deficiencies in the prior art. Those skilled in the art should be aware of this flexibility.

[0128] See Figure 1 and Figure 2 , the UAV air - computing method based on physical - layer security of the present invention includes the following steps:

[0129] Step S1: Construct a UAV air - computing model including an unmanned aerial vehicle (UAV), sensors, and an eavesdropper (EVE);

[0130] In this embodiment, the UAV air - computing model consists of one UAV, K sensors, and one eavesdropper. Each sensor is equipped with a single antenna; the eavesdropper is equipped with a single antenna; each sensor is used to send wireless signals to the UAV; the UAV receives the superimposed signals of all the wireless signals sent by the sensors; at the same time, the eavesdropper can also intercept the wireless signals sent by the sensors;

[0131] Assume that the flight time of the UAV is T, and the flight time T is divided into n ∈ Ν = {1, 2,..., N} time slots, then each time slot is Assume that the flight altitude of the UAV remains unchanged at H0, and the horizontal position of the UAV is represented as q[n]=[x[n], y[n]] T ; then the following constraints are imposed on the trajectory of the UAV:

[0132]

[0133] q[0]=q0, q[N + 1]=q F ; (2)

[0134] where: L = Vt s represents the maximum displacement of the UAV within one time slot, where V is the maximum speed of the UAV; q0 and q F represent the initial position and the final position of the UAV respectively.

[0135] Step S2: Construct the mean - square error function of the UAV according to the wireless signals received by the UAV; construct the mean - square error function of the eavesdropper according to the wireless signals received by the eavesdropper;

[0136] In this embodiment, the steps for constructing the mean - square error function of the UAV and the mean - square error function of the eavesdropper are as follows:

[0137] Step S201: Calculate the average value of the wireless signals sent by all sensors:

[0138]

[0139] where x k [n] represents the wireless signal sent by sensor k;

[0140] Step S202: Calculate the wireless signal received by the UAV and the wireless signal received by the eavesdropper. Among them,

[0141] The wireless signal received by the UAV is:

[0142]

[0143] In the formula: b k [n] is the transmission coefficient of the wireless signal sent by sensor k; n0[n] is the additive white Gaussian noise with a mean of 0 and a variance of σ 2 for the UAV, that is, n0[n] ~ CN(0, σ 2 ); h k [n] is the channel coefficient from sensor k to the UAV. Among them,

[0144]

[0145] In the formula: w k = [x k , y k T is the position of sensor k; ρ0 represents the channel gain at the reference distance d0 = 1m between sensor k and the UAV;

[0146] The wireless signal received by the eavesdropper is:

[0147]

[0148] In the formula: n e [n] is the additive white Gaussian noise with a mean of 0 and a variance of σ 2 at the eavesdropper, that is, n e [n] ~ CN(0, σ 2 ); g k [n] is the channel coefficient from the sensor to the eavesdropper. Among them,

[0149]

[0150] In the formula: w e = [x e , y e T is the position of the eavesdropper; ρ e ​​Denote the channel gain at the reference distance \(d_0 = 1m\) from the eavesdropper to the UAV;

[0151] Step S203: Impose a constraint on the transmission power of sensor \(k\) in any time slot \(n\);

[0152]

[0153] where: \(P\) max is the maximum transmission power of sensor \(k\);

[0154] Step S204: Obtain the estimation functions of the UAV and the eavesdropper, where

[0155] The estimation function of the UAV is:

[0156]

[0157] The estimation function of the eavesdropper is:

[0158]

[0159] where: \(a_0[n]\) and \(a\) e [n] represent the receiving coefficients of the UAV and the eavesdropper respectively;

[0160] Step S205: Obtain the mean square error functions of the UAV and the eavesdropper, where

[0161] The mean square error function of the UAV is:

[0162]

[0163] The mean square error function of the eavesdropper is:

[0164]

[0165] Step S3: With the objective of minimizing the mean square error at the UAV, taking the receiving coefficient \(a_0[n]\) of the UAV, the transmission coefficient \(b\) k [n] of the sensor and the trajectory \(q[n]\) of the UAV as optimization variables, construct an objective optimization problem; at the same time, by constraining the mean square error function of the eavesdropper to be greater than or equal to a preset threshold (i.e., the difference threshold of the mean square error function of the eavesdropper in each time slot), the information stealing ability of the eavesdropper is reduced and the communication security is enhanced;

[0166]

[0167] where: \(\xi\) is the difference threshold of the mean square error function of the eavesdropper in each time slot.

[0168] Step S4: Solve the constructed target optimization problem using the alternating optimization algorithm to obtain the optimal transmission coefficient of the sensor, the optimal reception coefficient of the sensor, and the optimal path of the UAV;

[0169] The specific idea is as follows: Initialize the transmission coefficient of the sensor, the reception coefficient of the UAV, and the reception coefficient of the eavesdropper. According to the transmission coefficient of the sensor before adjustment, the reception coefficient of the UAV, the reception coefficient of the eavesdropper, the mean square error function of the UAV, and the mean square error function of the eavesdropper, determine the adjustment values of the transmission coefficient of the sensor, the reception coefficient of the UAV, and the reception coefficient of the eavesdropper after adjustment, and adjust the transmission coefficient of the sensor and the reception coefficient of the UAV; According to the corresponding transmission coefficient after adjustment, the reception coefficient of the UAV after adjustment, and the mean square error function of the UAV after adjustment, obtain the optimal transmission coefficient of the sensor, the optimal reception coefficient of the UAV, and the optimal trajectory of the UAV.

[0170] First, decompose the target optimization problem (P0) into sub-problem one, sub-problem two, and sub-problem three, and alternately fix the optimization variables through the alternating optimization algorithm for iterative solution; among them,

[0171] The sub-problem one is: Fix the reception coefficient a0[n] of the UAV / the reception coefficient a e [n] of the eavesdropper, the transmission coefficient b k [n] of sensor k, and the trajectory q[n] of the UAV to obtain the optimal solution of a e [n] / a0[n]:

[0172] The sub-problem two is: Fix the reception coefficient a0[n] of the UAV and the trajectory q[n] of the UAV, and optimize the transmission coefficient b k [n] of sensor k;

[0173] The sub-problem three is: Fix the reception coefficient a0[n] of the UAV and the transmission coefficient b k [n] of sensor k, and optimize the UAV trajectory q[n].

[0174] Among them, the specific solution process of the sub-problem one is as follows:

[0175] 1), Fix the reception coefficient a0[n] of the UAV, the transmission coefficient b k [n] of sensor k, and the trajectory q[n] of the UAV. By taking the first derivative of MSE e ({a e [n], b k [n], q[n]}), obtain the optimal solution of a e [n]:

[0176]

[0177] Substitute Equation (12) into MSE e ({a e [n], b k [n], q[n]}) and organize to obtain:

[0178]

[0179] 2), Fix the receiving coefficient a e [n] of the eavesdropper, the transmission coefficient b k [n] of sensor k, and the trajectory q[n] of the UAV to obtain the optimal solution of a0[n]:

[0180]

[0181] Substitute Equation (13) into Equation (11), and the original objective optimization problem (P0) is rewritten as:

[0182]

[0183] Subsequently, based on the objective optimization problem (P1), sub-problem two and sub-problem three are solved.

[0184] Among them, for sub-problem two, the specific solution process is as follows:

[0185] Optimize the transmission coefficient b k [n] of sensor k, fix the receiving coefficient a0[n] at the UAV and the trajectory q[n] of the UAV. Among them, since Equation (13) is a non-convex constraint, Equation (13) is transformed as:

[0186]

[0187] For in b k [n], perform the first-order Taylor expansion and organize to obtain:

[0188]

[0189] Therefore, sub-problem two is:

[0190]

[0191] Solve this sub-problem two.

[0192] Among them, for sub-problem three, the specific solution process is as follows:

[0193] Fix the receiving coefficient a0[n] of the UAV and the transmission coefficient b k [n] of sensor k, and optimize the UAV trajectory q[n];

[0194] Expand each term in to obtain:

[0195]

[0196] Introduce an auxiliary variable:

[0197] L0 = {l k [n] = ||q[n] - w k || 2 , n ∈ N, k ∈ K}

[0198] Let can be rewritten as This term is a convex constraint for l k [n];

[0199] Let

[0200] Then the second term in is -f k [n ] , which is a non - convex constraint for q[n], resulting in the optimization problem (P1) being a non - convex problem.

[0201] Although f k [n] is a non - convex constraint for q[n], it is a convex constraint for ||q[n] - w k || 2 ; therefore, given q (r) [n], perform a first - order Taylor expansion of f k [n] with respect to ||q[n] - w k || 2 to obtain a lower bound, denoted as After processing, we get:

[0202]

[0203] What is obtained in this processing step is a concave function, then is a convex function, thus making the optimization problem a convex optimization problem;

[0204] From the above, we have:

[0205]

[0206] Secondly, for the auxiliary variable L0 = {l k [n] = ||q[n] - w k || 2 , n ∈ N, k ∈ K}, the constraint condition is l k[n] ≤ ||q[n] - w k || 2 , where ||q[n] - w k || 2 is a convex constraint for q[n], but the original constraint l k [n] ≤ ||q[n] - w k || 2 is a non - convex constraint; similarly, when performing Taylor expansion given q (r) [n], we can get ||q[n] - w k || 2 ≥ ||q (r) [n] - w k || 2 + 2(q (r) [n] - w k ) T (q[n] - q (r) [n]), then this constraint can be rewritten as l k [n] ≤ ||q (r) [n] - w k || 2 + 2(q (r) [n] - w k ) T (q[n] - q (r) [n]),

[0207] Finally, sub - problem three can be rewritten as:

[0208]

[0209] Solve the above - mentioned sub - problem three.

[0210] In summary, by using the alternating optimization algorithm to solve the reception coefficient a0[n] at the UAV, the transmission coefficient b k [n] of sensor k and the trajectory q[n] of the UAV, the solution of the target optimization problem (P1) is obtained. Among them, the optimization process of the alternating optimization algorithm is as follows:

[0211] Step S401: Set the preset difference threshold ξ, the number K of sensors, initialize the transmission coefficient b k [n] of sensor k, substitute and calculate the initial values of a0[n] and a e [n]; initialize the trajectory q[n] of the UAV, and calculate the initial MSE0({a0[n], b k [n], q[n]}) according to Equation (9);

[0212] Step S402: Let t = 1,..., T, and start the t - th round of iteration;

[0213] Step S403: Fix Calculate the optimal solution at this time according to Equation (14)

[0214] Step S404: Fix Calculate the optimal solution at this time according to Equation (17)

[0215] Step S405: Fix Calculate the optimal solution q at this time according to Equation (19) (t+1) [n];

[0216] Step S406: Given to calculate the mean square error between the UAV and the eavesdropper (i.e., );

[0217] Step S407: Calculate the relative difference between the mean square error of the UAV and the mean square error of the eavesdropper (i.e., and determine whether the relative difference is less than the difference threshold ξ;

[0218] If the relative difference is greater than the difference threshold ξ, let t = t + 1, and repeat steps S403 - S406;

[0219] If the relative difference is less than the difference threshold ξ, exit the loop, and the output is the desired result.

[0220] Step S5: Transmit the wireless signal based on the optimal transmission coefficient of the sensor, the optimal reception coefficient of the UAV, and the optimal path of the UAV.

[0221] In the simulation, in addition to the optimization scheme proposed in the present invention, three cases of no optimization, only power optimization, and only trajectory optimization are also considered as reference schemes to verify the effectiveness of the scheme of the present invention. A minimum mean square error function at the UAV is constructed in MATLAB, and the following parameters are used: the number of sensors k = 3, the height of the UAV H = 100m, the reference channel power gain of the UAV ρ0 = 10 -3 , the reference channel power gain of the eavesdropper ρ e = 10 -3 , the noise power σ 2 = 10 -9 w, the maximum speed of the UAV v max = 20m / s, the initial power p0 = 1w, the position of the eavesdropper is [0, -300], the starting position of the UAV is [0, 500], the ending position is [0, -500], the positions of the three sensors are [-50, 300], [0, 0], [50, -300] respectively, and the difference threshold ξ = 0.2.

[0222] First, consider simulating at different flight times T. The range of the flight time T considered is between 100 s and 200 s, and simulations are carried out under four cases: without optimization, only power optimization, only trajectory optimization, and both power optimization and trajectory optimization. The simulation results are as Figure 3 shown:

[0223] From Figure 3 the simulation results, it can be seen that except for the non-optimized scheme, the minimum mean square error at the UAV in the other schemes decreases with the increase of the flight time T, and it can be seen from Figure 3 that the scheme of both trajectory optimization and power optimization has a better effect. The scheme of the present invention can achieve the minimum mean square error MSE compared with the other two benchmark schemes. This is because the scheme of the present invention dynamically balances the transmission power and the distances of all links, and fully utilizes the joint optimization ability of trajectory design and power control, thus indicating that the joint design of trajectory and power control is crucial for improving system performance.

[0224] Secondly, consider simulating at different transmission powers P. The range of the transmission power P considered is between 1 W and 9 W, and simulations are carried out under four cases: without optimization, only power optimization, only trajectory optimization, and both power optimization and trajectory optimization. The simulation results are as Figure 4 shown:

[0225] From Figure 4 the simulation results, it can be seen that as the transmission power P increases, the minimum mean square error at the UAV in all schemes decreases; and within this range, the scheme of the present invention is always superior to the other three benchmark schemes, which illustrates the effectiveness of the scheme of the present invention.

[0226] In summary, the UAV aerial computing method based on physical layer security of the present invention adopts aerial computing technology. The sensor sends wireless signals to the UAV, and the UAV can quickly receive the superimposed signals sent by all sensors. However, at the same time, the existence of eavesdroppers needs to be considered, and a threshold needs to be set to make the eavesdropper steal as few signals as possible to ensure secure transmission. In this scenario, the alternating optimization algorithm is used to optimize the transmission coefficient of the sensor, the reception coefficient of the UAV, and the trajectory of the UAV, model the problem of minimizing the mean square error of the UAV, ensure the signal quality received by the legitimate receiver, reduce the signal quality received by the eavesdropper, and improve the security performance of wireless signal transmission.

[0227] Please refer to Figure 5, a UAV air computing device based on physical layer security provided to meet one of the purposes of the present invention, including a model construction module, an objective function construction module, and an alternating optimization module: wherein, the model construction module is used to construct a UAV air computing model; the objective function construction module is used to construct an objective optimization function with minimizing the mean square error function at the UAV as the optimization objective and the receiving coefficient at the UAV, the receiving coefficient at the eavesdropper, the transmission coefficient, and the trajectory of the UAV as the optimization variables; the alternating optimization module is used to solve the constructed objective optimization function by using an alternating optimization algorithm to obtain the optimal solution.

[0228] Based on any embodiment of the present invention, please refer to Figure 6 , another embodiment of the present invention further provides an electronic device, which can be implemented by a computer device, as Figure 6 shown, the internal structure schematic diagram of the computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected through a system bus. Among them, the computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions. The database can store a control information sequence. When the computer-readable instructions are executed by the processor, the processor can implement a UAV air computing method based on physical layer security. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device can store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the UAV air computing method based on physical layer security of the present invention. The network interface of the computer device is used to connect and communicate with the terminal. Those skilled in the art can understand that Figure 6 the structure shown in

[0229] is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0229] In this embodiment, the processor is used to execute Figure 5 the specific functions of each module in

[0230] The present invention also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of the method for UAV aerial computing based on physical layer security according to any embodiment of the present invention.

[0231] The present invention also provides a computer program product, including a computer program / instructions, which, when executed by one or more processors, implement the steps of the method for UAV aerial computing based on physical layer security according to any embodiment of the present invention.

[0232] Those of ordinary skill in the art can understand that all or part of the processes of implementing the above embodiments of the method of the present invention can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a computer-readable storage medium such as a magnetic disk, an optical disc, a read-only memory (ROM), or a random access memory (RAM), etc.

[0233] The above is a preferred embodiment of the present invention. However, the embodiments of the present invention are not limited by the above content. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A UAV aerial computing method based on physical layer security, characterized in that: The following steps are involved: Step S1: construct a drone aerial computing model including drones, sensors and eavesdroppers; Step S2: constructing a mean square error function of the drone based on the wireless signal received by the drone; According to the wireless signal received by the eavesdropper, the mean square error function of the eavesdropper is constructed; Step S3: Taking minimizing the mean square error at the drone as the optimization goal, the drone's receiving coefficient a0[n], the sensor's transmission coefficient b k [n] and the trajectory of the drone q[n] are optimization variables, and constraints are imposed on the mean square error function of the eavesdropper to make it greater than or equal to the preset threshold, thereby constructing the target optimization problem; Step S4: using an alternating optimization algorithm to solve the constructed target optimization problem, and obtaining the optimal transmission coefficient of the sensor, the optimal receiving coefficient of the sensor, and the optimal path of the UAV; Step S5: Transmitting the wireless signal based on the optimal transmission coefficient of the sensor, the optimal reception coefficient of the drone, and the optimal path of the drone.

2. The UAV aerial computing method based on physical layer security according to claim 1 is characterized in that: In step S1, the drone aerial computing model consists of a drone, K sensors and an eavesdropper, wherein each sensor is equipped with a single antenna; the eavesdropper is equipped with a single antenna; each sensor is used to send a wireless signal to the drone; the drone receives the superposition signal of the wireless signals sent by all sensors; at the same time, the eavesdropper also steals the wireless signals sent by the sensors; Assume that the flight time of the drone is T, and divide the flight time T into n∈N={1,2,...,N} time slots, then each time slot is Assume that the flight altitude of the drone remains unchanged and is maintained at H0, and the horizontal position of the drone is expressed as q[n] = [x[n], y[n]] T ; then the following constraints are imposed on the trajectory of the drone: q[0]=q0,q[N+1]=q F ; (2) Where: L = Vt s represents the maximum displacement of the drone in a time slot, where V is the maximum speed of the drone; q0 and q F They represent the initial position and final position of the UAV respectively.

3. The UAV aerial computing method based on physical layer security according to claim 2 is characterized in that: In step S2, the construction steps of the mean square error function of the drone and the mean square error function of the eavesdropper are: Step S201: Calculate the average value of the wireless signals sent by all sensors: Among them, x k [n] represents the wireless signal sent by sensor k; Step S202: Calculate the wireless signal received by the drone and the wireless signal received by the eavesdropper, where The wireless signal received by the drone is: Where: b k [n] is the transmission coefficient of the wireless signal sent by sensor k; n0[n] is the mean of the drone, which is 0 and the variance is σ 2 Additive Gaussian white noise, that is, n0[n]~CN(0,σ 2 );h k [n] is the channel coefficient from sensor k to the drone, where Where: w k =[x k ,y k ] T is the position of sensor k; ρ0 represents the channel gain at the reference distance d0=1m between sensor k and the UAV; The wireless signal received by the eavesdropper is: Where: n e [n] is the eavesdropper with a mean of 0 and a variance of σ 2 Additive Gaussian white noise, that is, n e [n]~CN(0,σ 2 );g k [n] is the channel coefficient from the sensor to the eavesdropper, where Where: w e =[x e ,y e ] T is the position of the eavesdropper; e represents the channel gain at the reference distance d0=1m from the eavesdropper to the drone; Step S203: impose constraints on the transmission power of sensor k in any time slot n; Where: P max is the maximum transmission power of sensor k; Step S204: Obtain estimation functions of the drone and the eavesdropper, where: The estimation function of the drone is: The eavesdropper's estimation function is: Where: a0[n] and a e [n] represents the reception coefficients of the drone and the eavesdropper, respectively; Step S205: Obtain the mean square error function of the drone and the eavesdropper, where: The mean square error function of the drone is: The mean square error function of the eavesdropper is: Step S206: construct a target optimization problem by obtaining the mean square error function of the drone and the eavesdropper.

4. The UAV aerial computing method based on physical layer security according to claim 3 is characterized in that: In step S3, the target optimization problem (P0) is constructed as: Where: ξ is the difference threshold of the mean square error function of the eavesdropper in each time slot.

5. The UAV aerial computing method based on physical layer security according to claim 4 is characterized in that: In step S4, the target optimization problem is decomposed into subproblem 1, subproblem 2 and subproblem 3, and the optimization variables are alternately fixed by an alternating optimization algorithm to perform an iterative solution; wherein, The sub-problem 1 is: fixed drone's reception coefficient a0[n] / eavesdropper's reception coefficient a e [n], transmission coefficient b of sensor k k [n] and the trajectory of the UAV q[n], we get a e The optimal solution of [n] / a0[n] is: The second sub-problem is: fix the receiving coefficient a0[n] of the drone, the trajectory q[n] of the drone, and the transmission coefficient b to the sensor k k [n] Perform optimization; The sub-problem 3 is: the reception coefficient a0[n] of the fixed drone and the transmission coefficient b of the sensor k k [n], optimize the UAV trajectory q[n].

6. The UAV aerial computing method based on physical layer security according to claim 5 is characterized in that: The specific solution process of sub-problem 1 is as follows: Fixed drone reception coefficient a0[n], sensor k transmission coefficient b k [n] and the UAV trajectory q[n], by finding the MSE e ({a e [n],b k [n],q[n]}), we get a e The optimal solution for [n] is: Substitute formula (12) into MSE e ({a e [n],b k [n],q[n]}), and sort them out to get: Fixed eavesdropper's receiving coefficient a e [n], transmission coefficient b of sensor k k [n] and the trajectory of the UAV q[n], we get the optimal solution for a0[n]: Substituting equation (13) into equation (11), the original target optimization problem (P0) is rewritten as: Then, based on the target optimization problem (P1), subproblems 2 and 3 are solved.

7. The UAV aerial computing method based on physical layer security according to claim 6 is characterized in that: For sub-problem 2, the specific solution process is: The reception coefficient a0[n] at the fixed drone, the trajectory q[n] of the drone, and the transmission coefficient b to the sensor k k [n] is optimized; formula (13) is transformed into: right b k [n] Perform a first-order Taylor expansion and rearrange to obtain: Therefore, sub-problem 2 is: Solve the above sub-problem 2.

8. The UAV aerial computing method based on physical layer security according to claim 7 is characterized in that: For sub-problem 3, the specific solution process is: Fixed drone reception coefficient a0[n], sensor k transmission coefficient b k [n], optimize the trajectory q[n] of the UAV; Will Expand each item in to get: Introduce auxiliary variables: L0={l k [n]=||q[n]-w k || 2 ,n∈N,k∈K} make get make And for f k [n]About||q[n]-w k || 2 Perform a first-order Taylor expansion and obtain the lower bound And for the lower bound After processing, we get: From the above: Secondly, for the auxiliary variable L0={l k [n]=||q[n]-w k || 2 ,n∈N,k∈K}, the constraint is l k [n]≤||q[n]-w k || 2 , where ||q[n]-w k || 2 For q[n], it is a convex constraint, but the original constraint l k [n]≤||q[n]-w k || 2 is a non-convex constraint; similarly, given q (r) [n], and then Taylor expansion is performed to obtain: ||q[n]-w k || 2 ≥||q (r) [n]-w k || 2 +2(q (r) [n]-w k ) T (q[n]-q (r) [n]) After finishing, we get: Then the third sub-problem is: Solve the above sub-problem 3.

9. The UAV aerial computing method based on physical layer security according to claim 8 is characterized in that: In step S4, the receiving coefficient a0[n] at the drone and the transmission coefficient b of sensor k are solved by the alternating optimization algorithm. k [n] and the trajectory q[n] of the UAV, and obtain the solution of the target optimization problem (P1), wherein the optimization process of the alternating optimization algorithm is: Step S401: Set the preset difference threshold ξ, the number of sensors K, and initialize the transmission coefficient b of sensor k k [n], substitute and calculate a0[n] and a e Initialize the trajectory q[n] of the UAV, and calculate the initial MSE0({a0[n],b k [n],q[n]}); Step S402: Let t = 1, ..., T, and start the tth iteration; Step S403: Fixing According to formula (14), the optimal solution at this time is calculated Step S404: Fixing According to formula (17), the optimal solution at this time is calculated Step S405: Fixing According to formula (19), the optimal solution q is calculated at this time (t+1) [n]; Step S406: Given To calculate the mean square error of the drone, and at the same time calculate the mean square error of the eavesdropper; Step S407: Calculate the relative difference between the mean square error of the drone and the mean square error of the eavesdropper, and determine whether the relative difference is less than the difference threshold ξ; If the relative difference is greater than the difference threshold ξ, set t=t+1 and repeat steps S403 to S406; If the relative difference is less than the difference threshold ξ, then exit the loop and output This is the desired result.

10. A drone air computing device used in the drone air computing method based on physical layer security as claimed in any one of claims 1 to 9, characterized in that: include: Model building module: used to build the UAV aerial computing model; Objective function construction module: used to construct an objective optimization function with minimization of the mean square error function at the drone as the optimization objective, and the reception coefficient at the drone, the reception coefficient at the eavesdropper, the transmission coefficient and the trajectory of the drone as the optimization variables; Alternating optimization module: used to solve the constructed target optimization function using the alternating optimization algorithm to obtain the optimal solution.

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