Unmanned aerial vehicle assisted Internet of Vehicles anti-interference transmission method based on peak information age and Stackelberg game

Abstract

The invention relates to the technical field of unmanned aerial vehicle auxiliary communication and Internet of Vehicles, in particular to an unmanned aerial vehicle auxiliary Internet of Vehicles anti-interference transmission method based on peak information age and Stackelberg game, which comprises the following steps: establishing a system communication model consisting of a legal unmanned aerial vehicle, an intelligent attacker, a vehicle node and a ground base station; based on the system communication model, the average peak information age of the legal unmanned aerial vehicle is analyzed and deduced; utility functions of a legal unmanned aerial vehicle and an intelligent attacker are defined, a Stackelberg game problem with the average peak information age as an optimization target is constructed, the intelligent attacker serves as a leader, and the legal unmanned aerial vehicle serves as a follower; and solving an equilibrium solution of the Stackelberg game of the legal unmanned aerial vehicle and the intelligent attacker. According to the method, the confrontation relationship between the legal unmanned aerial vehicle and the intelligent attacker is described based on the Stackelberg game theory, the data sensing rate and transmission power of the unmanned aerial vehicle under given interference are dynamically optimized through the sub-gradient iterative algorithm, and collaborative improvement of data transmission timeliness and anti-interference performance is achieved.

Description

Technical Field

[0001] This invention relates to the fields of unmanned aerial vehicle (UAV) assisted communication and vehicle networking technologies, specifically to an anti-interference transmission method for UAV assisted vehicle networking based on peak information age and Stackelberg game theory. Background Technology

[0002] With the rapid development of 5G mobile communication technology, the Internet of Vehicles (IoV), as a core component of intelligent transportation systems, significantly improves road safety, traffic efficiency, and user driving experience by enabling efficient interaction between vehicles and everything else. However, the traditional architecture centered on terrestrial base stations has significant limitations in terms of coverage, network resilience, and dynamic load balancing capabilities, making it difficult to meet the needs of highly mobile, low-latency business scenarios. To overcome these challenges, unmanned aerial vehicle (UAV)-assisted communication technology has been introduced, providing continuous and reliable network connectivity for high-speed moving vehicles and effectively compensating for the functional deficiencies of a single terrestrial network in dynamic environments.

[0003] Furthermore, in applications such as collision warning, collaborative perception, and autonomous driving in the Internet of Vehicles, "information freshness" is crucial to the safety and effectiveness of the system. Information age (AoI) is used to measure the overall staleness of information since its generation, while peak information age (PAoI), as the maximum value of AoI within a given time window, can more accurately characterize the information failure risk faced by the system under extreme conditions. Therefore, it has significant research and practical value in this field.

[0004] While UAV-assisted communication enhances network flexibility, its air-to-ground line-of-sight channel is susceptible to malicious interference, leading to communication link blockage or interruption, severely jeopardizing the timeliness and reliability of vehicle-to-everything (V2X) services. Currently, research on optimizing the timeliness and anti-interference transmission of UAV-assisted V2X systems can be mainly divided into two categories: one focuses on improving the overall system timeliness by optimizing the average information age; however, it lacks sufficient protection for the information age threshold under extreme conditions, making it difficult to cope with the risk of peak performance degradation. The other category focuses on employing physical layer security technologies to enhance anti-interference capabilities, but it does not fully consider the information timeliness requirements during the design process, limiting its applicability in real-time critical scenarios.

[0005] Therefore, when the system faces the risk of interference attacks, how to coordinate and optimize the data sensing rate and transmission power of UAVs to achieve a balance between data transmission timeliness and communication robustness is a key issue that urgently needs to be addressed. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a method for anti-interference transmission of UAV-assisted vehicle-to-everything (V2X) networks based on peak information age and Stackelberg game theory, ensuring the timeliness and security of UAV-assisted V2X transmission in interference environments.

[0007] This invention provides a drone-assisted vehicle-to-everything (V2X) anti-interference transmission method based on peak information age and Stackelberg game theory, comprising the following steps:

[0008] Step S1: Establish a system communication model consisting of legitimate drones, intelligent attackers, vehicle nodes, and ground base stations;

[0009] Step S2: Based on the system communication model, analyze and derive the average peak information age of the legitimate UAV;

[0010] Step S3: Define the utility functions of legitimate drones and intelligent attackers, and construct a Stackelberg game problem with average peak information age as the optimization objective, where the intelligent attacker is the leader and the legitimate drone is the follower;

[0011] Step S4: Solve for the equilibrium solution of the Stackelberg game between the legitimate drone and the intelligent attacker.

[0012] Preferably, step S1 specifically includes:

[0013] Define the set of locations for legal drones Data sensing rate set Λ={λ1,λ2,…,λ N} and transmission power set The channel gain α between the legitimate UAV and the ground base station is calculated based on the simplified path loss model. i It can be expressed as:

[0014]

[0015] Where, d i For legal drones U i The distance to the ground base station, the location of the ground base station is (x B ,y B ,0), where δ is the path loss exponent.

[0016] Define the attacker's location as (x J ,y J ,z J ), calculate the channel gain β from the attacker to the ground base station. J :

[0017]

[0018] Where, dJ It is the distance from the attacker to the ground base station.

[0019] According to Shannon's formula, the channel capacity from a legal UAV to a ground base station can be expressed as:

[0020]

[0021] Where B is the available bandwidth of the system, N0 is the ambient noise power, and ∑ m≠i α m ·P m This represents the total co-channel interference power from other drones.

[0022] Preferably, step S2 specifically includes:

[0023] Treat the packet queuing queue at the legitimate drone location as an M / M / 1 queue model, and update the packets at a rate λ. i The Poisson process arrives at the legitimate drone location, and the service rate is equivalent to the channel capacity μ. i Based on the definition of peak information age, the average peak information age PAoI of legitimate UAVs is derived. i It can be expressed as:

[0024]

[0025] Where, λ i <μ i Ensure a finite queue length.

[0026] Preferably, step S3 specifically includes:

[0027] The utility function for legal drones is defined with the objective of minimizing the average peak information age:

[0028]

[0029] To maximize the average peak information age, the utility function of a legal drone is defined as follows:

[0030]

[0031] in, For the transmission power cost of legal drones, C J For the interference power cost of intelligent attackers, C P W represents the cost per unit of peak information age, and W represents the data volume of a single data packet.

[0032] Determine the data sensing rate λ i Transmission power P iUsing the interference power J as the optimization variable, a Stackelberg game problem with the average peak information age as the optimization objective is constructed based on the utility function, where the intelligent attacker acts as the leader and the legitimate drones act as followers. This optimization problem can be expressed as:

[0033]

[0034] Where, λ M P M and J M These are the preset upper limits for data sensing rate, transmission power, and interference power, respectively.

[0035] Preferably, step S4 specifically includes:

[0036] Step S4-1: In the follower subgame, given the interference power strategy of the intelligent attacker, solve for the optimal data sensing rate and optimal transmission power of the legitimate UAV.

[0037] Step S4-2: In the leader subgame, anticipate the optimal response of the legitimate drone and solve for the optimal interference power of the intelligent attacker.

[0038] Step S4-3: Based on the sub-gradient algorithm, iteratively update the data sensing rate, transmission power of the legitimate UAV, and the interference power of the intelligent attacker until the convergence condition is met, and output the final optimized parameters.

[0039] Preferably, step S4-1 specifically includes:

[0040] Given the interference power, the joint optimization problem of legal UAVs is transformed into a convex optimization problem using Lagrange dual optimization theory, and the optimal data sensing rate is derived. The closed expression:

[0041]

[0042] Secondly, the optimal data sensing rate λ i_opt Substituting the utility function of a legal UAV, the optimal transmission power P is derived based on the KKT conditions. i_opt The closed expression:

[0043]

[0044] in, The nonnegative dual variable is introduced.

[0045] Preferably, step S4-2 specifically includes:

[0046] According to backward induction, when the follower's optimal strategy... Once determined, this is substituted into the attacker's utility function. If the attacker's attack power is too high, the legitimate drone will stop transmitting data, which means... Assumption Φ represents the i-th threshold that influences the attacker's decision. i It can be represented by the following formula:

[0047]

[0048] Where, Φ min =Φ1≤Φ2≤…≤Φ max .

[0049] Considering only two legitimate drones, the optimal attack power J can be derived using Lagrange duality optimization theory and KKT conditions. opt The closed expression:

[0050]

[0051] in, ε1=N0+α1·P1, ε2=N0+α2·P2, ζ J The nonnegative dual variable is introduced.

[0052] Preferably, step S4-3 specifically includes:

[0053] Initialize information about legitimate drones, intelligent attackers, and the environment. Set the maximum number of iterations t. M In each iteration, the attacker first selects their strategy and determines the interference power, and then the legitimate drone, acting as a follower, adjusts its data sensing rate and transmission power based on the current interference power. The parameter changes between the current and previous iterations are calculated. If the change is less than the convergence threshold σ, the iteration terminates and the optimization result is output; otherwise, the iteration loop continues.

[0054] Compared with the prior art, the present invention has at least the following beneficial effects:

[0055] This invention focuses on minimizing the peak information age under interference attacks as its core optimization objective. This enables the system to perceive channel conditions and interference levels in real time and dynamically adjust the data sensing rate and transmission power of the UAV accordingly. For the adversarial relationship between legitimate UAVs and attackers, this invention models the relationship based on Stackelberg game theory and employs a sub-gradient iterative algorithm for efficient solution. This algorithm quickly converges to an equilibrium strategy, thereby achieving a synergistic improvement in data transmission timeliness and anti-interference performance in dynamic adversarial environments. Attached Figure Description

[0056] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0057] Figure 1 This is a flowchart of the anti-interference transmission method for UAV-assisted vehicle-to-everything (V2X) networks based on peak information age and Stackelberg game proposed in this invention.

[0058] Figure 2 This invention relates to a flowchart for finding the equilibrium solution in a Stackelberg game involving legitimate drones and intelligent attackers.

[0059] Figure 3 Simulation diagram for convergence to the optimal transmission strategy for legitimate drones and intelligent attackers. Detailed Implementation

[0060] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0061] To illustrate the effectiveness of the method proposed in this invention, the following detailed description of the above technical solution is provided through a specific embodiment, such as... Figure 1 As shown, a method for anti-interference transmission of UAV-assisted vehicle-to-everything (V2X) networks based on peak information age and Stackelberg game is disclosed. The specific implementation steps are as follows:

[0062] Step S1: Establish a system communication model consisting of legitimate drones, intelligent attackers, vehicle nodes, and ground base stations;

[0063] In this step, the set of locations for legal drones is defined. Data sensing rate set Λ={λ1,λ2,…,λ N} and transmission power set The channel gain α between the legitimate UAV and the ground base station is calculated based on the simplified path loss model. i It can be expressed as:

[0064]

[0065] Where, d i For legal drones U i The distance to the ground base station, the location of the ground base station is (x B ,y B ,0), where δ is the path loss exponent.

[0066] Define the attacker's location as (x J ,y J ,z J ), calculate the channel gain β from the attacker to the ground base station. J :

[0067]

[0068] Where, d J It is the distance from the attacker to the ground base station.

[0069] According to Shannon's formula, the channel capacity from a legal UAV to a ground base station can be expressed as:

[0070]

[0071] Where B is the available bandwidth of the system, N0 is the ambient noise power, and ∑ m≠i α m ·P m This represents the total co-channel interference power from other drones.

[0072] In this embodiment, only the case of two legitimate drones and one intelligent attacker is considered, and the relevant parameters are assigned values. The location of legitimate drone 1 is (4km, 4km, 100m), and the initial transmission power P1(0) = 1W; the location of legitimate drone 2 is (5km, 2km, 200m), and the initial transmission power P2(0) = 1.5W; the location of the intelligent attacker is (8km, 8km, 150m), and the initial interference power J(0) = 0W; the location of the base station is (0km, 0km, 0m), the path loss index is δ = 2, the available bandwidth of the system is B = 3MHz, and the environmental noise power is N0 = -174dBm.

[0073] Step S2: Derive the average age of the legitimate recipient and the eavesdropper.

[0074] In this step, the data packet queue at the legitimate drone location is treated as an M / M / 1 queue model, and the data packets are updated at a rate λ. i The Poisson process arrives at the legitimate drone location, and the service rate is equivalent to the channel capacity μ. i Based on the definition of peak information age, the average peak information age PAoI of legitimate UAVs is derived. i It can be expressed as:

[0075]

[0076] Where, λ i <μ i Ensure a finite queue length.

[0077] In this embodiment, relevant parameters are assigned values. The initial data sensing rate of the legitimate UAV 1 is λ1(0) = 0 Mb / s, and the initial data sensing rate of the legitimate UAV 2 is λ2(0) = 2.5 Mb / s.

[0078] Step S3: Define the utility functions of legitimate drones and intelligent attackers, and construct a Stackelberg game problem with average peak information age as the optimization objective, where the intelligent attacker is the leader and the legitimate drone is the follower;

[0079] The utility function for legal drones is defined with the objective of minimizing the average peak information age:

[0080]

[0081] To maximize the average peak information age, the utility function of a legal drone is defined as follows:

[0082]

[0083] in, For the transmission power cost of legal drones, C J For the interference power cost of intelligent attackers, C P W represents the cost per unit of peak information age, and W represents the data volume of a single data packet.

[0084] Determine the data sensing rate λ i Transmission power P i Using the interference power J as the optimization variable, a Stackelberg game problem with the average peak information age as the optimization objective is constructed based on the utility function, where the intelligent attacker acts as the leader and the legitimate drones act as followers. This optimization problem can be expressed as:

[0085]

[0086] Where, λ M P M and J M These are the preset upper limits for data sensing rate, transmission power, and interference power, respectively.

[0087] In this embodiment, relevant parameters are assigned values. W = 1MB. C p =520000, C J =360000, λ M =3Mb / s, P M =10w, J M =15w.

[0088] Step S4: Solve for the equilibrium solution of the Stackelberg game between the legitimate drone and the intelligent attacker. The specific processing steps are as follows:

[0089] 1) In follower subgames, given the interference power strategy of the intelligent attacker, find the optimal data sensing rate and optimal transmission power for the legitimate UAV.

[0090] Given the interference power, the joint optimization problem of legal UAVs is transformed into a convex optimization problem using Lagrange dual optimization theory, and the optimal data sensing rate is derived. The closed expression:

[0091]

[0092] Secondly, the optimal data sensing rate Substituting the utility function of a legal drone, the optimal transmission power is derived based on the KKT conditions. The closed expression:

[0093]

[0094] in, The nonnegative dual variable is introduced.

[0095] 2) In the leader subgame, anticipate the optimal response of the legitimate drone and solve for the optimal interference power of the intelligent attacker.

[0096] According to backward induction, when the follower's optimal strategy... Once determined, this is substituted into the attacker's utility function. If the attacker's attack power is too high, the legitimate drone will stop transmitting data, which means... Assumption Φ represents the i-th threshold that influences the attacker's decision. i It can be represented by the following formula:

[0097]

[0098] Where, Φ min =Φ1≤Φ2≤…≤Φ max .

[0099] Considering only two legitimate drones, the optimal attack power J can be derived using Lagrange duality optimization theory and KKT conditions. opt The closed expression:

[0100]

[0101] in, ε1=N0+α1·P1, ε2=N0+α2·P2, ζ J The nonnegative dual variable is introduced.

[0102] 3) Based on the sub-gradient algorithm, iteratively update the data sensing rate, transmission power of the legitimate UAV and the interference power of the intelligent attacker until the convergence condition is met, and output the final optimized parameters.

[0103] Initialize information about legitimate drones, intelligent attackers, and the environment. Set the maximum number of iterations t. M In each iteration, the attacker first selects their strategy and determines the interference power, and then the legitimate drone, acting as a follower, adjusts its data sensing rate and transmission power based on the current interference power. The parameter changes between the current and previous iterations are calculated. If the change is less than the convergence threshold σ, the iteration terminates and the optimization result is output; otherwise, the iteration loop continues.

[0104] In this embodiment, dual variables and ζ J Choose the iteration step size and σ J To perform an update, the update expression is:

[0105]

[0106] ζ J (t+1)=[ζ J (t)-σ J ·(J M -J opt (t+1))] +

[0107] The relevant parameter is assigned the value t. M =30, ζ J (0) = 5, σ = 1 × 10 -6 .

[0108] The present invention provides pseudocode for the proposed anti-interference transmission algorithm, as shown in Table 1.

[0109] Table 1

[0110]

[0111] This invention is a method for anti-interference transmission of UAV-assisted vehicle-to-everything (V2X) networks based on peak information age and Stackelberg game. It dynamically optimizes the UAV data sensing rate and transmission power under given interference through a sub-gradient iterative algorithm, thereby achieving a synergistic improvement in data transmission timeliness and anti-interference performance. Figure 3This demonstrates that an iterative algorithm based on subgradient updates can quickly achieve convergence of the behaviors of legitimate drones and intelligent attackers.

[0112] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for anti-interference transmission in UAV-assisted vehicle-to-everything (V2X) networks based on peak information age and Stackelberg game theory, characterized in that... Includes the following steps: Step S1: Establish a system communication model consisting of legitimate drones, intelligent attackers, vehicle nodes, and ground base stations; Step S2: Based on the system communication model, analyze and derive the average peak information age of the legitimate UAV; Step S3: Define the utility functions of legitimate drones and intelligent attackers, and construct a Stackelberg game problem with average peak information age as the optimization objective, where the intelligent attacker is the leader and the legitimate drone is the follower; Step S4: Solve for the equilibrium solution of the Stackelberg game between the legitimate drone and the intelligent attacker; Step S4 specifically includes: Step S4-1: In the follower subgame, given the interference power strategy of the intelligent attacker, solve for the optimal data sensing rate and optimal transmission power of the legitimate UAV. Step S4-2: In the leader subgame, anticipate the optimal response of the legitimate drone and solve for the optimal interference power of the intelligent attacker. Step S4-3: Based on the sub-gradient algorithm, iteratively update the data sensing rate, transmission power of the legitimate UAV, and the interference power of the intelligent attacker until the convergence condition is met, and output the final optimized parameters.

2. The UAV-assisted vehicle-to-everything (V2X) anti-interference transmission method based on peak information age and Stackelberg game theory as described in claim 1, characterized in that, Step S1 specifically includes: Define the set of locations for legal drones Data sensing rate set Λ={λ1,λ2,…,λ N } and transmission power set The channel gain α between the legitimate UAV and the ground base station is calculated based on the simplified path loss model. i It can be expressed as: Where, d i For legal drones U i The distance to the ground base station, the location of the ground base station is (x B ,y B ,0), where δ is the path loss exponent. Define the attacker's location as (x J ,y J ,z J ), calculate the channel gain β from the attacker to the ground base station. J : Where, d J It is the distance from the attacker to the ground base station. According to Shannon's formula, the channel capacity from a legal UAV to a ground base station can be expressed as: Where B is the available bandwidth of the system, N0 is the ambient noise power, and ∑ m≠i α m ·P m This represents the total co-channel interference power from other drones.

3. The UAV-assisted vehicle-to-everything (V2X) anti-interference transmission method based on peak information age and Stackelberg game theory as described in claim 1, characterized in that, Step S2 specifically includes: Treat the packet queuing queue at the legitimate drone location as an M / M / 1 queue model, and update the packets at a rate λ. i The Poisson process arrives at the legitimate drone location, and the service rate is equivalent to the channel capacity μ. i Based on the definition of peak information age, the average peak information age PAoI of legitimate UAVs is derived. i It can be expressed as: Where, λ i <μ i Ensure a finite queue length.

4. The UAV-assisted vehicle-to-everything (V2X) anti-interference transmission method based on peak information age and Stackelberg game theory as described in claim 1, characterized in that, Step S3 specifically includes: The utility function for legal drones is defined with the objective of minimizing the average peak information age: To maximize the average peak information age, the utility function of a legal drone is defined as follows: in, For the transmission power cost of legal drones, C J For the interference power cost of intelligent attackers, C P W represents the cost per unit of peak information age, and W represents the data volume of a single data packet. Determine the data sensing rate λ i Transmission power P i Using the interference power J as the optimization variable, a Stackelberg game problem with the average peak information age as the optimization objective is constructed based on the utility function, where the intelligent attacker acts as the leader and the legitimate drones act as followers. This optimization problem can be expressed as: Where, λ M P M and J M These are the preset upper limits for data sensing rate, transmission power, and interference power, respectively.

5. The UAV-assisted vehicle-to-everything (V2X) anti-interference transmission method based on peak information age and Stackelberg game theory according to claim 1, characterized in that, Step S4-1 specifically includes: Given the interference power, the joint optimization problem of legitimate UAVs is transformed into a convex optimization problem using Lagrange dual optimization theory, and the optimal data sensing rate is derived. The closed expression: Secondly, the optimal data sensing rate Substituting the utility function of a legal drone, the optimal transmission power is derived based on the KKT conditions. The closed expression: in, The nonnegative dual variable is introduced.

6. The UAV-assisted vehicle-to-everything (V2X) anti-interference transmission method based on peak information age and Stackelberg game theory according to claim 1, characterized in that, Step S4-2 specifically includes: According to backward induction, when the follower's optimal strategy... Once determined, this is substituted into the attacker's utility function. If the attacker's attack power is too high, the legitimate drone will stop transmitting data, which means... Assumption Φ represents the i-th threshold that influences the attacker's decision. i It can be represented by the following formula: Where, Φ min =Φ1≤Φ2≤…≤Φ max . Considering only two legitimate drones, the optimal attack power J can be derived using Lagrange duality optimization theory and KKT conditions. opt The closed expression: in, ε1=N0+α1·P1, ε2=N0+α2·P2, ζ J The nonnegative dual variable is introduced.

7. The UAV-assisted vehicle-to-everything (V2X) anti-interference transmission method based on peak information age and Stackelberg game theory according to claim 1, characterized in that, Step S4-3 specifically includes: Initialize information about legitimate drones, intelligent attackers, and the environment. Set the maximum number of iterations t. M In each iteration, the attacker first selects their strategy and determines the interference power, and then the legitimate drone, acting as a follower, adjusts its data sensing rate and transmission power based on the current interference power. The parameter changes between the current and previous iterations are calculated. If the change is less than the convergence threshold σ, the iteration terminates and the optimization result is output; otherwise, the iteration loop continues.

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