Multi-jammer assisted unmanned aerial vehicle concealed relay-edge calculation method and system

Abstract

The invention provides a multi-jammer assisted unmanned aerial vehicle concealed relay-edge calculation method and system, and the method comprises the steps: carrying out the deduction of a concealed relay rate from equipment to an unmanned aerial vehicle and a concealed relay rate from the unmanned aerial vehicle to a server in an unmanned aerial vehicle concealed relay and edge calculation network; under the assistance of multiple interference units, deducing a first minimum detection error rate for watching equipment communication and a second minimum detection error rate for watching unmanned aerial vehicle communication under the assistance of the multiple interference units; and on the basis of the obtained hidden relay rate, the first minimum detection error rate and the second minimum detection error rate, a joint optimization model is constructed and solved by taking the calculation amount of the maximum edge calculation amount minimum equipment as a target. According to the method, the concealment and the calculation efficiency in an unmanned aerial vehicle edge calculation network are improved to the maximum extent, limited wireless communication and hardware calculation resources can be fully utilized, illegal guard detection is resisted, and meanwhile the calculation requirement of edge equipment is met.

Description

Technical Field

[0001] This invention belongs to the field of UAV communication technology, and in particular relates to a UAV covert relay-edge computing method and system assisted by multiple jammers. Background Technology

[0002] In recent years, with the popularization of the Industrial Internet of Things (IIoT), the computing demands on ground equipment have reached unprecedented levels. Edge computing, as a technology that helps resource-constrained devices handle computationally intensive and latency-sensitive tasks, has attracted widespread attention in the IIoT. In recent years, this technology has been widely applied to terrestrial cellular networks, achieving low power consumption and low latency with its powerful computing capabilities. However, due to high costs and lack of flexibility, the deployment of servers based on traditional ground base stations is limited in underserved areas such as remote mountainous regions. Drones, due to their low cost and high mobility, have emerged as a viable solution to bridge these gaps. As aerial servers, drones can be deployed flexibly and on demand, making them an ideal choice for scenarios where ground infrastructure servers are insufficient.

[0003] While integrating drones into edge computing networks can significantly improve both communication and computing performance, the openness of drone air-to-ground channels often leads to serious stealth problems. To address this issue, covert communication technology has become a major research focus. In 2025, Deng et al. introduced a fixed-position antenna into a drone communication network and improved covert communication performance through additional antenna beamforming design. Also in 2025, Zhou et al. initially combined drone edge computing and covert communication technologies to establish a drone-enabled covert edge computing network to minimize energy consumption.

[0004] While the aforementioned covert communication schemes for UAV-enabled edge computing achieve good covertness, this work only considers the scenario where all data is offloaded to the UAV for computation. In reality, computation can occur simultaneously on the device, the UAV, and the ground server, and computational performance can be further improved through appropriate offloading strategies. Furthermore, existing covert communication schemes primarily rely on simple models involving a single jammer, neglecting the more general scenario where single or multiple jammers can be deployed in a wireless network to further enhance data offloading covertness. Further research is needed on more general UAV covert relay and edge computing methods and systems assisted by multiple jammers. Summary of the Invention

[0005] This invention addresses the issue of unmanned aerial vehicle (UAV) edge computing networks with unauthorized surveillance and multiple devices. It proposes a UAV covert relay and edge computing method and system assisted by multiple jammers to improve the network's covertness and computational efficiency.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A multi-jammer assisted UAV covert relay-edge computing method includes the following steps: In the covert relay and edge computing network for drones, the covert relay rate from the device to the drone and the covert relay rate from the drone to the server are derived based on the device location, drone location, and server location, respectively. With the assistance of multiple jammers, based on the device location, jammer location, and guard location, the first minimum detection error rate of the guard against device communication with the assistance of multiple jammers is derived. With the assistance of multiple jammers, based on the location of the UAV and the location of the guard, the second minimum detection error rate of the guard against UAV communication with the assistance of multiple jammers is derived. Based on the obtained covert relay rate, first minimum detection error rate and second minimum detection error rate, a joint optimization model is constructed with the goal of maximizing edge computing power and minimizing device computing power. This model includes time slot segmentation, UAV trajectory, device transmission power, UAV transmission power and computing task allocation. Solve the joint optimization model to obtain the optimized time slot segmentation scheme, UAV flight trajectory, equipment transmission power, UAV transmission power, and computational task allocation scheme.

[0007] Furthermore, the step of determining the covert relay rate includes: based on the device location, the drone location, and the server location, a first channel gain from the device to the drone and a second channel gain from the drone to the server; Based on the first channel gain and the device transmit power, the signal-to-noise ratio at the UAV is calculated, and the covert relay rate from the device to the UAV is derived. Based on the second channel gain and the UAV's transmit power, the signal-to-noise ratio at the server is calculated, and the covert relay rate from the UAV to the server is derived accordingly.

[0008] Furthermore, the derivation yields the first minimum detection error rate for monitoring device communication with the assistance of multiple jammers, which includes: Based on the device location, jammer location, and guard location, calculate the third channel gain from the device to the guard and the fourth channel gain from each jammer to the guard. Based on the third channel gain, the fourth channel gain, the device transmit power, and the jammer transmit power, the expression for the detection and received signal of the guard against device communication and its average power are obtained; according to the likelihood ratio detection principle, the closed-form expression for the first minimum detection error rate of the guard against device communication with the assistance of multiple jammers is derived.

[0009] Furthermore, the step of deriving the second minimum detection error rate of the guard against UAV communication with the assistance of multiple jammers includes: calculating the fifth channel gain from the UAV to the guard based on the UAV position and the guard position; Based on the fifth channel gain of the UAV's transmit power and the jammer's transmit power, the expression for the guard's detection and reception signal for UAV communication and its average power are obtained; Based on the likelihood ratio detection principle, a closed-form expression for the minimum detection error rate of the guard for UAV communication is derived with the assistance of multiple interfering devices.

[0010] Furthermore, the first channel gain is calculated based on the Nakagami-m fading channel model, and the third, fourth, and fifth channel gains are calculated based on a path loss model that includes small-scale fading.

[0011] Furthermore, the optimization objective of the joint optimization model is to maximize the computational load of the device with the smallest edge computing completion among all devices by optimizing time slot segmentation, UAV trajectory, device transmission power, UAV transmission power, and computational task allocation.

[0012] Furthermore, the joint optimization model includes the following constraints: UAV mobility constraints, covert relay throughput constraints, average device power consumption constraints, maximum transmit power constraints of devices and UAVs, time slot segmentation coefficient constraints, computing power constraints of each computing node, and covert communication performance constraints based on the minimum detection error rate.

[0013] Furthermore, the joint optimization model is expressed as follows:

[0014] in, Represents the set of optimization variables. Representing the The horizontal position of the drone in each time slot Representing the The transmit power of the UAV in each time slot Representing the The first time slot The transmission power of each device Representing the first The first time slot The time slot division coefficients calculated by the device relay, drone relay, and server for each device. Representing the first The first time slot The computational load of each device locally, the computational load relayed to the drone, and the computational load relayed to the server; Representing the Edge computing power per device; The total number of time slots; Representing the The horizontal position of the drone in each time slot; Represents the time slot length. This represents the maximum speed of the drone; Representing the The first time slot The computational load of relaying data from one device to the drone. Representing the The first time slot The computational load of relaying data from one device to the server; Represents the bandwidth of the spectrum; Representing the The first time slot The hidden relay rate of each device, Representing the The rate of covert relay of unmanned aerial vehicles in each time slot; Representing the The first time slot Calculate the power of each device; Representing the Maximum average power consumption of each device; and Representing the first Maximum transmission power of individual devices and drones; Total number of devices; Representing the first The maximum CPU frequency of individual devices, drones, and servers. Representing the first The number of CPU loops required for each device, drone, and server to calculate each bit of data; Represents the maximum permissible probability of exposure; Represents the first under the assistance of multiple jammers The time slot guard is targeting the first The expression for the first minimum detection error rate of communication between devices; Represents the first under the assistance of multiple jammers The expression for the second minimum detection error rate of time-slot monitoring for UAV communication; This represents the maximum permissible probability of exposure.

[0015] Furthermore, the joint optimization model is solved using an iterative algorithm based on the interior point method and continuous convex approximation, including: Initialize the time slot segmentation, UAV trajectory, device transmit power, UAV transmit power, and initial values ​​of computational complexity, and use these as local points for iteration; By using the continuous convex approximation method, the joint optimization problem is transformed into a convex problem by iterating local points in terms of time slot segmentation, UAV trajectory, equipment transmit power, UAV transmit power, and computational complexity. The interior point method is used to solve the convex problem, and the optimized solution is used as a new iterative local point. Determine whether the performance increase of the current iteration compared to the previous iteration is less than a preset threshold. If so, stop the iteration and output the current solution as the optimization result; otherwise, continue to the next iteration.

[0016] On the other hand, the present invention provides a multi-jammer assisted UAV covert relay-edge computing system, comprising: Relay rate calculation module: It is used in UAV covert relay and edge computing networks to derive the covert relay rate from device to UAV and the covert relay rate from UAV to server based on device location, UAV location and server location, respectively. The first detection and analysis module is used to derive the first minimum detection error rate of the guard against the device communication under the assistance of multiple jammers, based on the device location, jammer location and guard location. The second detection and analysis module is used to derive the second minimum detection error rate of the guard against drone communication under the assistance of multiple jammers, based on the drone's location and the guard's location. Joint optimization modeling module: It is used to construct a joint optimization model based on the obtained covert relay rate, first minimum detection error rate and second minimum detection error rate, with the goal of maximizing edge computing power and minimizing device computing power. This model includes time slot segmentation, UAV trajectory, device transmission power, UAV transmission power and computing task allocation. The optimization solution module is used to solve the joint optimization model to obtain the optimized time slot segmentation scheme, UAV flight trajectory, equipment transmission power, UAV transmission power and computing task allocation scheme.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes a method and system for covert relay and edge computing of UAVs assisted by multiple jammers, which maximizes the covertness and computing efficiency in UAV edge computing networks, can make full use of limited wireless communication and hardware computing resources, resist the detection of illegal guards, and meet the computing needs of edge devices. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1This is a schematic diagram of a UAV covert relay and edge computing network assisted by multiple jammers, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the method implemented in this invention; Figure 3 This is a schematic diagram comparing the computational load of an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.

[0022] Example 1 In this embodiment, as Figure 1 The diagram illustrates a multi-jammer-assisted drone covert relay and edge computing network as considered in a specific embodiment of the present invention. This network includes a drone, One ground device, one server A friendly jammer and an unauthorized monitor are employed. Ground equipment relays computational data to drones, which then relay it to a server. Simultaneous computation occurs on the ground equipment, drones, and servers to achieve efficient edge computing of the ground equipment data. Relays between the equipment and drones, and between drones and the server, are subject to unauthorized detection by the monitor, while the friendly jammer sends artificial noise to interfere with the unauthorized monitor. To improve edge computing performance and the stealth and security of the drone network while maintaining relay stealth, a joint design is needed for time slot segmentation, drone trajectory, equipment transmit power, drone transmit power, and computational load, maximizing edge computing load while minimizing the edge computing load on the devices.

[0023] Figure 2 A multi-jammer assisted UAV covert relay-edge computing method provided in this disclosure includes the following steps: Step 1: In the covert relay and edge computing network for drones, based on the device location, drone location, and server location, the covert relay rate from the device to the drone and the covert relay rate from the drone to the server are derived respectively. Step 2: With the assistance of multiple jammers, based on the device location, jammer location, and guard location, derive the first minimum detection error rate of the guard against device communication with the assistance of multiple jammers. Step 3: With the assistance of multiple jammers, based on the drone's location and the guard's location, the second minimum detection error rate of the guard against drone communication with the assistance of multiple jammers is derived. Step 4: Based on the obtained covert relay rate, first minimum detection error rate and second minimum detection error rate, construct a joint optimization model that includes time slot segmentation, UAV trajectory, device transmission power, UAV transmission power and computing task allocation with the goal of maximizing edge computing power and minimizing device computing power; Step 5: Solve the joint optimization model to obtain the optimized time slot segmentation scheme, UAV flight trajectory, equipment transmission power, UAV transmission power and computation task allocation scheme.

[0024] In the above embodiments, step 1 includes: Based on the device location, drone location, and server location, the first channel gain from the device to the drone and the second channel gain from the drone to the server; Based on the first channel gain and the device transmit power, the signal-to-noise ratio at the UAV is calculated, and the covert relay rate from the device to the UAV is derived. Based on the second channel gain and the UAV's transmit power, the signal-to-noise ratio at the server is calculated, and the covert relay rate from the UAV to the server is derived accordingly.

[0025] The first channel gain from the device to the drone in step 1 is:

[0026] in Represents the equipment number. Represents the time slot number. This represents the total number of time slots. Representing the The first time slot Channel gain from individual devices to drones, Represents the air-to-ground channel power gain per unit distance. Represents the air-to-ground communication road damage index; Representing the The first time slot The distance from the device to the drone Representing the The horizontal position of the drone in each time slot Representing the The horizontal position of each device Represents the drone's flight altitude; Let represent the random fading coefficients of the Nakagami-m channel, and its probability density function is:

[0027] in represent The probability density function of the modulus, Represents the fading coefficient. Represents the scaling factor. Represents the gamma function. Represents a random variable.

[0028] The second channel gain from the drone to the server in step 1 is:

[0029] in Representing the Channel gain from the drone to the server in each time slot, Representing the The distance from the drone to the server in each time slot. This represents the horizontal position of the server.

[0030] The channel gain from the drone to the server is:

[0031] in Representing the Channel gain from the drone to the server in each time slot, Representing the The distance from the drone to the server in each time slot. This represents the horizontal position of the server.

[0032] The signal-to-noise ratio at the drone location is:

[0033] in Representing the The first time slot The signal-to-noise ratio of the drone during communication between devices Representing the The first time slot The transmission power of each device Represents background noise power; Its probability density function is:

[0034] in represent The probability density function.

[0035] The signal-to-noise ratio at the server in step 1 is:

[0036] in Representing the Signal-to-noise ratio at each time slot server Representing the The transmit power of the UAV in each time slot.

[0037] The concealed relay rate of the device is:

[0038] in Representing the The first time slot The device's concealed relay rate.

[0039] The covert relay rate for drones is:

[0040] in Representing the The rate of covert relay of unmanned aerial vehicles in each time slot.

[0041] In the above embodiment, step 2 includes: Based on the device location, jammer location, and guard location, calculate the third channel gain from the device to the guard and the fourth channel gain from each jammer to the guard. Based on the third channel gain, the fourth channel gain, the device transmit power, and the jammer transmit power, the expression for the detection and received signal of the guard against device communication and its average power are obtained; according to the likelihood ratio detection principle, the closed-form expression for the first minimum detection error rate of the guard against device communication with the assistance of multiple jammers is derived.

[0042] The expression for the channel gain from the device to the watcher in step 2 of this embodiment is:

[0043] in Representing the Channel gain from the device to the watcher This represents the power gain of the ground channel per unit distance. Represents the ground road damage index; Representing the The distance from the equipment to the guard Represents the horizontal position of the guard. It follows a complex Gaussian distribution with a mean of 0 and a variance of 1, representing small-scale fading on the ground.

[0044] The expression for the channel gain from the jammer to the watcher in step 2 is:

[0045] in Represents the jammer number. Representing the The channel gain from the jammer to the watcher Representing the The distance between the jammer and the guard.

[0046] In step 2, the expression for the detected received signal of the device communication by the guard is:

[0047] in Represents the signal number. Represents the total number of signals in each time slot; Representing the The time slot guard is targeting the first The first device communication One received signal, Representing the The first time slot The jammer sent the first... One interference signal, Representing the The transmission power of each jammer Representing the The first time slot A background noise signal, Representing the The first time slot The device sent the first One signal; Representing the The assumption that the devices do not communicate. This represents its alternative hypothesis.

[0048] In step 2, the expression for the average power of the received signal detected by the guard for device communication is:

[0049] in Representing the The time slot guard is targeting the first The average power of the received signal detected by the communication device. The representative parameter is an exponentially random distributed variable, The representative parameter is The exponentially random distribution variable.

[0050] Denote auxiliary variables and ,but and The probability density functions are as follows:

[0051] in represent The probability density function, represent The probability density function; , , and Represents the distribution parameters.

[0052] The derivation of the above probability density function is as follows: Because ,therefore The probability density function is Convolution of probability density functions. Then, according to the properties of the Laplace transform, The probability density function Laplace transform is The product of the probability density function and its Laplace transform, i.e.:

[0053] in represent The probability density function Laplace transform, represent The probability density function Laplace transform, This represents the Laplace variable. Then, according to the inverse Laplace transform, we can obtain... Similarly, we can obtain the probability density function. The probability density function.

[0054] In step 2, the expression for the minimum detection error rate of the guard against device communication with the assistance of multiple jammers is:

[0055] in Represents the first under the assistance of multiple jammers The time slot guard is targeting the first The expression for the minimum detection error rate of communication between devices.

[0056] The derivation of the above expression for the minimum detection error rate is as follows: First, calculate... The derivative is:

[0057] in represent The derivative of . Therefore, for It can be represented as:

[0058] because , and It is a log-concave function, therefore The minimum detection error rate expression for device communication by a watchdog with multiple jammers is given by having only one maximum value:

[0059] In the above embodiment, step 3 includes: calculating the fifth channel gain from the drone to the guard based on the drone's location and the guard's location; Based on the fifth channel gain of the UAV's transmit power and the jammer's transmit power, the expression for the guard's detection and reception signal for UAV communication and its average power are obtained; Based on the likelihood ratio detection principle, a closed-form expression for the minimum detection error rate of the guard for UAV communication is derived with the assistance of multiple interfering devices.

[0060] The expression for the channel gain from the UAV to the guard in step 3 of this embodiment is:

[0061] in Representing the Channel gain from the drone to the guard in each time slot; Representing the The distance from the drone to the guard in each time slot.

[0062] In step 3, the expression for the detected and received signal of the drone communication is:

[0063] in Representing the The first time slot monitoring for drone communication One received signal, Representing the The drone sent the first time slot One signal; Representing the The assumption that the devices do not communicate. This represents its alternative hypothesis.

[0064] In step 3, the expression for the average power of the detected received signal for UAV communication is:

[0065] in Representing the Each time slot is monitored to detect the average power of the received signal in the drone communication detection system. It follows a Gamma distribution, and its probability density function is: .

[0066] Describe auxiliary variables and Their probability density functions are as follows:

[0067] in represent The probability density function, This represents an incomplete gamma function.

[0068] The derivation of the above probability density function is as follows: Since ,therefore The probability density function is and The convolution, i.e.:

[0069] The expression for the minimum detection error rate of the guard against UAV communication with the assistance of multiple jammers, as described in step 3, is as follows:

[0070] in Represents the first under the assistance of multiple jammers The expression for the minimum detection error rate of time-slot monitoring for UAV communication.

[0071] The objective of the joint optimization model described in step 4 of this embodiment is:

[0072] in Represents the set of optimization variables. Representing the first The first time slot The time slot division coefficients calculated by the device relay, drone relay, and server for each device. Representing the first The first time slot The computational load of each device locally, the computational load relayed to the drone, and the computational load relayed to the server; Representing the The objective is to maximize the edge computing cost per device by optimizing time slot segmentation, UAV trajectory, device transmit power, and UAV transmit power, while minimizing the edge computing cost per device.

[0073] The joint optimization model includes the following constraints: UAV mobility constraints, covert relay throughput constraints, average device power consumption constraints, maximum transmit power constraints of devices and UAVs, time slot segmentation coefficient constraints, computing power constraints of each computing node, and covert communication performance constraints based on the minimum detection error rate.

[0074] The mobility constraints are:

[0075] in Represents the time slot length. This represents the maximum speed of the drone; the above constraint means that the speed of the drone is less than its maximum speed.

[0076] The constraints for covert relays are:

[0077] in Represents the spectral width; the meanings of the above constraints are respectively the first... The sum of the computational cost of relaying data from one device to the drone and the computational cost of relaying data to the server should be less than that of the first device. The maximum throughput of relaying data from one device to a drone, the first The sum of the computational load of each device relay server should be less than the maximum throughput of the drone relay to the server.

[0078] The average power consumption constraint of the device is:

[0079] in Representing the The first time slot Calculate the power of each device. Representing the Effective capacitance coefficient of each device Representing the The maximum average power consumption of each device; the meaning of the above constraint is the first The average power consumption of each device should be less than the maximum average power consumption.

[0080] The maximum transmit power constraint is:

[0081] in and Representing the first The maximum transmit power of each device and drone; the meaning of the above constraints is the first... The transmission power of each device and drone should be no less than 0 and no greater than its maximum transmission power.

[0082] The time slot division constraint is:

[0083] The above constraint means that the value of the time slot division coefficient is between 0 and 1, and the sum of the time slot division coefficients of all devices in any time slot should not be greater than 1.

[0084] The calculation constraints are:

[0085] in Representing the first The maximum CPU frequency of individual devices, drones, and servers. Representing the first The number of CPU loops required for each device, drone, and server to compute each bit of data; the meaning of the above constraint is the number of times the device, drone, and server need to compute each bit of data; The computational load of each device, drone, and server should be less than or equal to the maximum computational load of the CPU.

[0086] The constraints for covert communication are:

[0087] in This represents the maximum permissible exposure probability; the above constraint means that the minimum detection error rate of the guard against communication between the device and the drone under the assistance of multiple jammers must be greater than a given threshold.

[0088] The joint optimization model for time slot segmentation, UAV trajectory, device transmit power, UAV transmit power, and computational complexity in the multi-jammer assisted UAV covert relay and edge computing network described in step 4 is as follows:

[0089] The algorithm based on the interior point method and continuous convex approximation described in step 5 of this embodiment is as follows: Step 5.1: Initialize time slot segmentation, UAV trajectory, device transmit power, UAV transmit power, and initial values ​​for computational load. And use it as a local point in the iteration; Step 5.2: In each iteration, first use the continuous convex approximation method to iterate local points in terms of time slot segmentation, UAV trajectory, equipment transmit power, UAV transmit power, and computational complexity. Transform the joint optimization problem into a convex problem; Step 5.3: Solve the problem using the interior point method and use the optimized solution as a new iterative local point. ; Step 5.4: If the performance increase in this iteration is less than the given threshold compared to the previous iteration, then stop the iteration, and the local point of this iteration is the optimal solution to the problem; otherwise, it will be used as the local point of the next iteration, and return to step 5.2 to continue the iteration.

[0090] Figure 3 A comparison of the throughput of the proposed solutions is presented in the embodiments of the present invention. The comparison schemes selected are the traditional single jammer, serverless, and relay-only schemes. It can be seen that the proposed scheme is superior to the comparison schemes under different maximum transmission powers of different devices, which verifies the performance advantage of the proposed scheme.

[0091] Example 2 This embodiment provides a multi-jammer assisted UAV covert relay-edge computing system, including: Relay rate calculation module: It is used in UAV covert relay and edge computing networks to derive the covert relay rate from device to UAV and the covert relay rate from UAV to server based on device location, UAV location and server location, respectively. The first detection and analysis module is used to derive the first minimum detection error rate of the guard against the device communication under the assistance of multiple jammers, based on the device location, jammer location and guard location. The second detection and analysis module is used to derive the second minimum detection error rate of the guard against drone communication under the assistance of multiple jammers, based on the drone's location and the guard's location. Joint optimization modeling module: It is used to construct a joint optimization model based on the obtained covert relay rate, first minimum detection error rate and second minimum detection error rate, with the goal of maximizing edge computing power and minimizing device computing power. This model includes time slot segmentation, UAV trajectory, device transmission power, UAV transmission power and computing task allocation. The optimization solution module is used to solve the joint optimization model to obtain the optimized time slot segmentation scheme, UAV flight trajectory, equipment transmission power, UAV transmission power and computing task allocation scheme.

[0092] It should be understood that any parts not described in detail in this specification belong to the prior art.

[0093] It should be understood that the above description of the preferred embodiments is quite detailed, but this should not be construed as limiting the scope of protection of this invention. It is neither necessary nor possible to exhaustively describe all possible implementations. Those skilled in the art, guided by this invention, can make substitutions or modifications without departing from the scope of the claims, all of which fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for UAV covert relay-edge computing assisted by multiple jammers, characterized in that, Includes the following steps: In the covert relay and edge computing network for drones, the covert relay rate from the device to the drone and the covert relay rate from the drone to the server are derived based on the device location, drone location, and server location, respectively. With the assistance of multiple jammers, based on the device location, jammer location, and guard location, the first minimum detection error rate of the guard against device communication with the assistance of multiple jammers is derived. With the assistance of multiple jammers, based on the location of the UAV and the location of the guard, the second minimum detection error rate of the guard against UAV communication with the assistance of multiple jammers is derived. Based on the obtained covert relay rate, first minimum detection error rate and second minimum detection error rate, a joint optimization model is constructed with the goal of maximizing edge computing power and minimizing device computing power. This model includes time slot segmentation, UAV trajectory, device transmission power, UAV transmission power and computing task allocation. Solve the joint optimization model to obtain the optimized time slot segmentation scheme, UAV flight trajectory, equipment transmission power, UAV transmission power, and computational task allocation scheme.

2. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 1, characterized in that, The steps for determining the covert relay rate include: based on the device location, the drone location, and the server location, a first channel gain from the device to the drone and a second channel gain from the drone to the server; Based on the first channel gain and the device transmit power, the signal-to-noise ratio at the UAV is calculated, and the covert relay rate from the device to the UAV is derived. Based on the second channel gain and the UAV's transmit power, the signal-to-noise ratio at the server is calculated, and the covert relay rate from the UAV to the server is derived accordingly.

3. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 2, characterized in that, The derivation yields the first minimum detection error rate for guarding device communication with the assistance of multiple jammers, which includes: Based on the device location, jammer location, and guard location, calculate the third channel gain from the device to the guard and the fourth channel gain from each jammer to the guard. Based on the third channel gain, the fourth channel gain, the device transmit power, and the jammer transmit power, the expression for the detection and received signal of the guard against device communication and its average power are obtained; according to the likelihood ratio detection principle, the closed-form expression for the first minimum detection error rate of the guard against device communication with the assistance of multiple jammers is derived.

4. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 3, characterized in that, The steps for deriving the second minimum detection error rate of the guard against UAV communication with the assistance of multiple jammers include: calculating the fifth channel gain from the UAV to the guard based on the UAV's location and the guard's location; Based on the fifth channel gain of the UAV's transmit power and the jammer's transmit power, the expression for the guard's detection and reception signal for UAV communication and its average power are obtained; Based on the likelihood ratio detection principle, a closed-form expression for the minimum detection error rate of the guard for UAV communication is derived with the assistance of multiple interfering devices.

5. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 4, characterized in that, The first channel gain is calculated based on the Nakagami-m fading channel model, and the third, fourth, and fifth channel gains are calculated based on the path loss model that includes small-scale fading.

6. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 1, characterized in that, The optimization objective of the joint optimization model is to maximize the computational load of the device with the smallest edge computing completion among all devices by optimizing time slot segmentation, UAV trajectory, device transmission power, UAV transmission power, and computational task allocation.

7. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 6, characterized in that, The joint optimization model includes the following constraints: UAV mobility constraints, covert relay throughput constraints, average device power consumption constraints, maximum transmission power constraints of devices and UAVs, time slot segmentation coefficient constraints, computing power constraints of each computing node, and covert communication performance constraints based on the minimum detection error rate.

8. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 7, characterized in that, The joint optimization model expression is: in, Represents the set of optimization variables. Representing the The horizontal position of the drone in each time slot Representing the The transmit power of the UAV in each time slot Representing the The first time slot The transmission power of each device Representing the first The first time slot The time slot division coefficients calculated by the device relay, drone relay, and server for each device. Representing the first The first time slot The computational load of each device locally, the computational load relayed to the drone, and the computational load relayed to the server; Representing the Edge computing power per device; The total number of time slots; Representing the The horizontal position of the drone in each time slot; Represents the time slot length. This represents the maximum speed of the drone; Representing the The first time slot The computational load of relaying data from one device to the drone. Representing the The first time slot The computational load of relaying data from one device to the server; Represents the bandwidth of the spectrum; Representing the The first time slot The hidden relay rate of each device, Representing the The rate of covert relay of unmanned aerial vehicles in each time slot; Representing the The first time slot Calculate the power of each device; Representing the Maximum average power consumption of each device; and Representing the first Maximum transmission power of individual devices and drones; Total number of devices; Representing the first The maximum CPU frequency of individual devices, drones, and servers. Representing the first The number of CPU loops required for each device, drone, and server to calculate each bit of data; Represents the maximum permissible probability of exposure; Represents the first under the assistance of multiple jammers The time slot guard is targeting the first The expression for the first minimum detection error rate of communication between devices; Represents the first under the assistance of multiple jammers The expression for the second minimum detection error rate of time-slot monitoring for UAV communication; This represents the maximum permissible probability of exposure.

9. The UAV covert relay-edge computing method assisted by multiple jammers according to claim 1, characterized in that, Solving the joint optimization model employs an iterative algorithm based on the interior-point method and continuous convex approximation, including: Initialize the time slot segmentation, UAV trajectory, device transmit power, UAV transmit power, and initial values ​​of computational complexity, and use these as local points for iteration; By using the continuous convex approximation method, the joint optimization problem is transformed into a convex problem by iterating local points in terms of time slot segmentation, UAV trajectory, equipment transmit power, UAV transmit power, and computational complexity. The interior point method is used to solve the convex problem, and the optimized solution is used as a new iterative local point. Determine whether the performance increase of the current iteration compared to the previous iteration is less than a preset threshold. If so, stop the iteration and output the current solution as the optimization result; otherwise, continue to the next iteration.

10. A multi-jammer assisted UAV covert relay-edge computing system, characterized in that, include: Relay rate calculation module: It is used in UAV covert relay and edge computing networks to derive the covert relay rate from device to UAV and the covert relay rate from UAV to server based on device location, UAV location and server location, respectively. The first detection and analysis module is used to derive the first minimum detection error rate of the guard against the device communication under the assistance of multiple jammers, based on the device location, jammer location and guard location. The second detection and analysis module is used to derive the second minimum detection error rate of the guard against drone communication under the assistance of multiple jammers, based on the drone's location and the guard's location. Joint optimization modeling module: It is used to construct a joint optimization model based on the obtained covert relay rate, first minimum detection error rate and second minimum detection error rate, with the goal of maximizing edge computing power and minimizing device computing power. This model includes time slot segmentation, UAV trajectory, device transmission power, UAV transmission power and computing task allocation. The optimization and solution module is used to solve the joint optimization model to obtain the optimized time slot segmentation scheme, UAV flight trajectory, equipment transmission power, UAV transmission power and computing task allocation scheme. The multi-jammer-assisted UAV covert relay-edge computing system is used to perform the steps in the multi-jammer-assisted UAV covert relay-edge computing method according to any one of claims 1-9.

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