Communication perception integrated game anti-interference method, device, equipment and medium

Abstract

The embodiment of the invention provides a communication perception integrated game anti-interference method and device, equipment and a medium. The invention relates to the technical field of signal transmission. The method comprises the following steps: acquiring to-be-sent information needing to be sent to a user side by a base station; obtaining the target beam forming of the base station, the target trajectory of the unmanned aerial vehicle and the target beam forming of the unmanned aerial vehicle; sending the information to be sent to the user side based on the base station according to the target beam forming of the base station; and sending the to-be-sent information to the user side based on the unmanned aerial vehicle according to the target trajectory of the unmanned aerial vehicle and the target beam forming of the unmanned aerial vehicle. The method is used for achieving the technical effect of improving the transmission quality of signal transmission.

Description

Technical Field

[0001] This application relates to the field of signal transmission, and in particular to a communication-sensing integrated game anti-interference method, device, equipment and medium. Background Technology

[0002] In wireless communication systems, the direct communication link between base stations and users is often blocked by obstacles such as buildings and terrain, resulting in a non-line-of-sight transmission environment, which leads to severe signal attenuation and a decline in communication quality.

[0003] To address this issue, drones are commonly used as aerial mobile relay nodes. Leveraging their high mobility and flexible deployment capabilities, they establish line-of-sight relay links between base stations and users, effectively overcoming obstacle obstruction and improving communication coverage and quality. Optimizing parameters such as drone trajectory, transmission power, and beamforming enhances the communication performance of the relay link. However, in practical deployments, both drones and users are vulnerable to malicious external interference, resulting in poor signal transmission quality when the base station transmits signals to the user. Summary of the Invention

[0004] This application provides a communication sensing integrated game anti-interference method, apparatus, device and medium to achieve the technical effect of improving the signal transmission quality when the base station transmits signals to the user terminal.

[0005] In a first aspect, embodiments of this application provide a communication-sensing integrated game-playing anti-interference method, including:

[0006] Obtain the information that the base station needs to send to the user terminal;

[0007] The target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV are obtained. Among them, the target beamforming of the base station is used to characterize the transmission phase and amplitude of the base station antenna, the target trajectory of the UAV is used to characterize the trajectory of the UAV transmitting signals to the user terminal, and the target beamforming of the UAV is used to characterize the angle of the UAV transmitting signals to the user terminal.

[0008] Based on the target beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station.

[0009] Based on the target trajectory and target beamforming of the UAV, the information to be sent is transmitted to the user terminal by the UAV.

[0010] In one possible implementation, the target base station beamforming, the UAV target trajectory, and the UAV target beamforming are acquired. The base station, the UAV, and the user terminal are identified as physical entities. The locations where interference signals are generated when the base station and the UAV transmit information to the user terminal are identified as interference points, including:

[0011] Obtain the location information of the interfering end, and determine the initial beamforming of the interfering end based on the location information of the interfering end;

[0012] Based on the short-range algorithm, the initial beamforming at the interference end is optimized to obtain the optimized beamforming;

[0013] Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized to obtain the optimized trajectory;

[0014] Based on the optimized beamforming at the interference end, the initial beamforming of the base station and the initial beamforming of the UAV are optimized to obtain the optimized beamforming of the base station and the optimized beamforming of the UAV.

[0015] Based on the optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station. Based on the optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0016] Based on the information to be transmitted from the base station and the drone, determine the target function value of the interference end and the first target function value of the entity end;

[0017] If the objective function value of the interfering end does not meet the first preset threshold, or the first objective function value of the entity end does not meet the second preset threshold, then repeat the above steps until the objective function value of the interfering end meets the first preset threshold and the first objective function value of the entity end meets the second preset threshold, or the first value of the repeated steps meets the third preset threshold. Then, the optimized trajectory of the UAV is determined as the target trajectory of the UAV, the optimized beamforming of the base station is determined as the target beamforming of the base station, the optimized beamforming of the UAV is determined as the target beamforming of the UAV, and the optimized beamforming of the interfering end is determined as the target beamforming of the interfering end.

[0018] In one possible implementation, the initial trajectory of the UAV is optimized based on the optimized beamforming at the interference end to obtain the optimized trajectory, including:

[0019] Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized using the maximization-minimization algorithm to obtain a new optimized trajectory for the UAV.

[0020] Acquire the initial beamforming of the base station and the initial beamforming of the UAV;

[0021] Based on the initial beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the initial beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0022] The second objective function value of the entity is determined based on the information to be transmitted from the base station and the drone.

[0023] Repeat the above steps until the second objective function value of the entity meets the fourth preset threshold, or the second value of the above steps meets the fifth preset threshold. Then, the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

[0024] In one possible implementation, the initial beamforming of the base station and the initial beamforming of the UAV are optimized based on the optimized beamforming of the interference end, to obtain the optimized beamforming of the base station and the optimized beamforming of the UAV, including:

[0025] Based on the optimized beamforming at the interference end, the initial beamforming of the base station is optimized using a semi-definite relaxation algorithm to obtain a new optimized beamforming for the base station.

[0026] Based on the optimized beamforming at the interference end, the initial beamforming of the UAV is optimized using a successive convex approximation algorithm to obtain a new optimized beamforming for the UAV.

[0027] Based on the new optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the new optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0028] The third objective function value of the entity is determined based on the information to be transmitted from the base station and the drone.

[0029] Repeat the above steps until the third objective function value satisfies the sixth preset threshold, or the third value of the above steps satisfies the seventh preset threshold. Then, the new optimized beamforming of the base station is determined as the first optimized beamforming of the base station, and the new optimized beamforming of the UAV is determined as the first optimized beamforming of the UAV.

[0030] In one possible implementation, the method further includes:

[0031] Based on the alternating optimization method, new optimized beamforming for the base station and new optimized beamforming for the UAV are obtained.

[0032] In one possible implementation, after determining the new optimized beamforming of the base station as the first optimized beamforming of the base station, and after determining the new optimized beamforming of the UAV as the first optimized beamforming of the UAV, the process includes:

[0033] If the value of the third objective function satisfies the eighth preset threshold, then the first optimized beamforming of the base station is determined as the optimized beamforming of the base station, and the first optimized beamforming of the UAV is determined as the optimized beamforming of the UAV.

[0034] If the value of the third objective function does not meet the eighth preset threshold, the optimized trajectory of the UAV will be optimized to obtain a new optimized trajectory of the UAV.

[0035] The first optimized beamforming of the base station is further optimized to obtain the second optimized beamforming of the base station.

[0036] The first optimized beamforming of the UAV is further optimized to obtain the second optimized beamforming of the UAV.

[0037] Based on the second optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the second optimized beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0038] Based on the information to be transmitted from the base station and the drone, determine the new third objective function value of the entity.

[0039] Repeat the above steps until the new third objective function value satisfies the eighth preset threshold, or the fourth number of repetitions of the above steps satisfies the ninth preset threshold. Then, the second optimized beamforming of the base station is determined as the optimized beamforming of the base station, the second optimized beamforming of the UAV is determined as the optimized beamforming of the UAV, and the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

[0040] In one possible implementation, it also includes:

[0041] After obtaining the optimized beamforming of the base station, the optimized beamforming of the UAV, and the optimized trajectory of the UAV, the reflection information of the interference end is obtained based on the sensing information sent by the UAV to the interference end.

[0042] Based on the reflection information from the interfering end, the signal quality value of the reflected information is obtained;

[0043] Determine whether the signal quality value of the reflected information meets the perception threshold. If so, determine the optimized trajectory of the UAV as the target trajectory of the UAV, determine the optimized beamforming of the base station as the target beamforming of the base station, determine the optimized beamforming of the UAV as the target beamforming of the UAV, and determine the optimized beamforming of the interference end as the target beamforming of the interference end.

[0044] If not, continue to optimize the drone's trajectory, base station, and drone beamforming until the signal quality value of the reflected information meets the perception threshold.

[0045] Secondly, embodiments of this application provide a communication-sensing integrated game-playing anti-interference device, comprising:

[0046] The acquisition module is used to acquire the information that the base station needs to send to the user terminal;

[0047] The processing module is used to acquire the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV; wherein, the target beamforming of the base station is used to characterize the transmission phase and amplitude of the base station's antenna, the target trajectory of the UAV is used to characterize the trajectory of the UAV transmitting signals to the user terminal, and the target beamforming of the UAV is used to characterize the angle at which the UAV transmits signals to the user terminal.

[0048] The transmitting module is used to transmit the information to be transmitted to the user terminal based on the target beamforming of the base station;

[0049] The transmission module is used to transmit the information to be transmitted from the UAV to the user terminal based on the UAV's target trajectory and target beamforming.

[0050] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0051] The memory stores computer-executed instructions;

[0052] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0053] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0054] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0055] The communication sensing integrated game-theoretic anti-interference method, apparatus, device, and medium provided in this application first acquire the information to be transmitted from the base station to the user terminal, and simultaneously determine the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV. Based on the target beamforming of the base station, the information to be transmitted is directionally sent to the user terminal through the base station. According to the target trajectory and target beamforming of the UAV, the UAV is used as a dynamic relay node to forward the information to the user terminal. This method, through spatial and beamforming coordination between the base station and the UAV, can significantly improve the anti-interference capability and communication reliability of the system in complex environments. The target beamforming of the base station can concentrate signal energy and reduce the probability of interference; the target trajectory and target beamforming design of the UAV enable the UAV to flexibly avoid obstructions and interference areas, thereby effectively enhancing the signal-to-interference-plus-noise ratio of the received signal at the user terminal, achieving the technical effect of improving the signal transmission quality when the base station transmits signals to the user terminal. Attached Figure Description

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0057] Figure 1 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 1 ;

[0058] Figure 2 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 2 ;

[0059] Figure 3 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 3 ;

[0060] Figure 4 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 4 ;

[0061] Figure 5 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 5 ;

[0062] Figure 6 A schematic diagram of the integrated communication and sensing game anti-interference device provided in this application;

[0063] Figure 7 A hardware schematic diagram of the communication-sensing integrated game anti-interference device provided in this application.

[0064] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0065] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and approaches consistent with some aspects of this application as detailed in the appended claims.

[0066] First, the following names need to be explained:

[0067] Non-orthogonal multiple access (NOMA) is a wireless communication technology that allows a base station to transmit different data to multiple users simultaneously on the same frequency resource at the same time, and distinguishes the signals of different users through power domain differences and continuous interference cancellation techniques of the receiver.

[0068] Self-interference cancellation (SIC) is a core technology in wireless communication, especially in simultaneous full-duplex systems on the same frequency. On the same communication device, through analog and digital signal processing, the interference caused by the device's own transmitted signal to its own receiving link is actively estimated, reconstructed, and subtracted, thereby enabling the device to transmit and receive signals simultaneously on the same frequency.

[0069] The signal-to-interference-plus-noise ratio (SINR) is used to measure the signal quality at the receiver. It is the ratio of the expected received signal power to the sum of all interference power (including interference from other unrelated sources) and background noise power.

[0070] Space division multiple access (SDMA) is a wireless communication technology that utilizes spatial degrees of freedom to allocate different spatially directional communication channels to multiple user devices located in different spatial locations at the same time and on the same frequency band, thereby enabling resource reuse for simultaneous communication by multiple users.

[0071] While drones transform the obstacle problem in physical space into a geometric problem solvable through maneuverability, and establish line-of-sight links through aerial deployment, thus bypassing signal attenuation in traditional non-line-of-sight environments, the success of this architecture entirely depends on a highly resource-constrained node exposed in open airspace. This dependence constitutes its fundamental technological weakness.

[0072] As aerial nodes, drones' communication links are completely exposed to complex environments, lacking the physical shielding and fixed protection typically found in ground-based infrastructure. This makes them highly vulnerable to malicious external interference. Attackers can use relatively inexpensive portable jamming devices to suppress and interfere with the specific frequency bands in which drones operate. Since the links between drones and base stations / users are usually point-to-point line-of-sight, jamming signals can propagate efficiently along unobstructed paths similar to communication signals, causing devastating damage to the link. Such interference not only renders carefully optimized trajectories, power, and beamforming strategies instantly ineffective but also directly causes a precipitous drop in link quality or even a complete outage, making signal transmission quality extremely unstable and unpredictable. Ultimately, all efforts to improve performance are rendered futile in the face of malicious attacks.

[0073] Interference is not random noise, but rather a hostile signal with a clear purpose and targeting. Current technologies lack effective modeling and response mechanisms for this proactive adversarial environment. The limited onboard energy and computing power of UAVs make it difficult for them to simultaneously perform complex interference detection, identification, and avoidance algorithms in real time while carrying out relay communication tasks. Therefore, when countering interference, they often find themselves in a passive position, forced to either increase transmission power (accelerating energy depletion) or attempt maneuvering to evade (potentially deviating from the optimal service location). Either choice comes at the cost of sacrificing the originally intended improved communication quality and coverage, resulting in a dilemma where security and performance are difficult to balance.

[0074] Therefore, existing technologies exhibit an asymmetry in the "attack and defense capabilities" of base stations and drones when transmitting signals to users. In an environment filled with dynamic signal interference, how can we improve the communication and sensing capabilities of drones? On the one hand, this would enhance the quality of the signals that base stations need to transmit to users; on the other hand, it would allow drones to sense the location of interference sources, thereby effectively improving the beamforming of base stations and drones, as well as the drone's trajectory, ultimately improving the signal quality of signals transmitted to users.

[0075] The core of this invention lies in abandoning the traditional passive optimization approach that treats malicious interference as a fixed or random background, and instead creatively introducing an adversarial game framework. By solving the equilibrium state of this game, the trajectory of the UAV, the base station, and the beamforming of the UAV are optimized in a coordinated manner, thereby realizing a forward-looking and resilient active anti-interference communication strategy.

[0076] Traditional methods for dealing with external malicious interference often employ passive response modes such as "sensor-avoidance" or "power countermeasures." The former assumes that the interference is static or predictable, attempting to maintain communication by avoiding the interference space, but this often fails in the face of intelligent interference. The latter attempts to suppress interference by increasing transmission power, which not only consumes a lot of energy but also easily falls into a vicious cycle of ineffective countermeasures or even self-interference when the interference power is unknown or dynamically changing. These methods all treat interference as an external uncertainty factor without internalizing its intelligent countermeasure behavior as part of the optimization model, resulting in insufficient robustness of the designed trajectory and beamforming schemes in dynamic, adversarial real-world environments.

[0077] The breakthrough of this invention lies in treating the interfering end as an agent with a clear adversarial objective (i.e., minimizing our communication performance), and the entities (including base stations, user terminals, and drones) as another adversarial agent, thus constructing a dynamic, non-cooperative game model. Within this framework, the optimized beamforming of the interfering end is not a fixed input, but rather its optimal response based on our strategy. The process of the entities optimizing the drone trajectory, base station, and drone beamforming involves finding the optimal strategy for the entities in the game, based on predicting the interfering party's optimal response. Essentially, this is solving a minimax optimization problem: finding the resource allocation scheme that maximizes the communication performance of the entities under worst-case interference. The significance of using game theory lies in its focus on the adversarial nature of the problem, ensuring that the trajectory and beamforming design of the entities no longer merely pursues the optimal under theoretical channel conditions, but rather seeks "anti-interference optimality." In this state, neither party can gain any additional benefit by unilaterally changing its strategy. This allows the entity to obtain a stable and predictable performance lower bound even in the presence of interference, thus achieving "performance protection in adversarial situations" and significantly improving the system's communication resilience and survivability in complex adversarial environments.

[0078] To address the aforementioned technical issues, this application provides a communication-sensing integrated game-theoretic anti-interference method, apparatus, device, and medium. This method acquires the information to be transmitted from the base station to the user terminal, determines the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV, and then, based on the target beamforming of the base station, transmits the information to the user terminal via the base station. Simultaneously, based on the target trajectory and target beamforming of the UAV, the UAV acts as a dynamic relay node to forward the information to the user terminal. This significantly improves the anti-interference capability and communication reliability of the system in complex environments through the spatial and beamforming synergy between the base station and the UAV. By enhancing the signal-to-interference-plus-noise ratio (SNR) of the received signal at the user terminal, it ultimately improves the signal transmission quality from the base station to the user terminal. This application overcomes the limitations of traditional static anti-interference modes, providing an adaptive solution for high-reliability communication in complex environments through a dynamic collaborative optimization mechanism, thus promoting the practical development of communication-sensing integrated systems in adversarial scenarios.

[0079] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] Figure 1 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0081] S101. Obtain the information to be sent by the base station to the user terminal.

[0082] In this embodiment, the drone acquires the information to be sent from the base station to the user terminal. This information can be the user's voice, text, images, or any data that needs to be transmitted. It is the object of processing in the entire transmission process.

[0083] Optionally, certain constraints must be imposed on the drone, including but not limited to flying from a designated starting position to the destination position, the flight altitude not exceeding the specified range, and the flight speed not exceeding the maximum limit.

[0084] S102, acquire the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV.

[0085] In this embodiment, the initial beamforming of the base station, the initial trajectory of the UAV, and the initial beamforming of the UAV must first be obtained. These initial beamformings are then optimized to obtain the target beamformings of the base station, the target trajectory of the UAV, and the target beamforming of the UAV. Specifically, the target beamforming of the base station characterizes the transmit phase and amplitude of the base station's antenna, the target trajectory of the UAV characterizes the trajectory of the UAV transmitting signals to the user terminal, and the target beamforming of the UAV characterizes the angle at which the UAV transmits signals to the user terminal.

[0086] S103. Based on the target beamforming of the base station, the information to be sent is sent to the user terminal based on the base station.

[0087] In this embodiment, the base station adjusts the phase and amplitude of its antenna array according to the target beamforming parameters determined in step S102, forming a concentrated, high-energy signal beam pointing towards the user end, and transmitting the information to be transmitted through this beam. Through beamforming, the base station can concentrate the transmission power towards the user end, thereby significantly enhancing the signal strength and anti-interference capability of the link, reducing energy waste and interference in other directions.

[0088] S104. Based on the target trajectory and target beamforming of the UAV, the information to be sent is transmitted to the user terminal based on the UAV.

[0089] In this embodiment, during flight, the UAV strictly follows the target trajectory planned in S102 to reach the optimal communication position. Simultaneously, it synchronously adjusts the beamforming parameters of its onboard antenna to ensure its transmitted beam accurately covers the target user terminal on the ground. In this state, the UAV receives information from the base station and forwards it to the user terminal. Through trajectory movement, the UAV provides a direct line-of-sight link to the user, overcoming obstructions. Through its beamforming, it accurately delivers signal energy to the target user and precisely delivers sensing signal energy to the interference point, ultimately achieving the fundamental goals of improving received signal quality, ensuring communication speed, and enhancing sensing echo rate and reliability.

[0090] The communication-sensing integrated game-theoretic anti-interference method provided in this application obtains the information to be sent from the base station to the user terminal, determines the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV, and then, based on the target beamforming of the base station, transmits the information to be sent to the user terminal through the base station. Simultaneously, based on the target trajectory and target beamforming of the UAV, the UAV acts as a dynamic relay node to forward the information to the user terminal. In this process, the target beamforming of the base station can concentrate signal energy to reduce the probability of interference, while the target trajectory and target beamforming of the UAV enable the UAV to flexibly avoid obstructions and... Interference zones are eliminated, allowing the spatial and beam coordination between base stations and drones to be fully utilized, significantly improving the system's anti-interference capability and communication reliability in complex environments. Ultimately, by effectively enhancing the signal-to-interference-plus-noise ratio (SNR) of the received signal at the user end, the technical effect of improving the signal transmission quality from the base station to the user end is achieved. Its essential significance lies in breaking through the limitations of traditional fixed relay mode in dealing with dynamic interference. By introducing intelligent trajectory planning and beam coordination mechanisms of drones, a dynamic defense system with dual anti-interference capabilities in spatial and beam dimensions is built for wireless communication systems in complex adversarial environments, providing key technical support for building the next generation of highly reliable and secure intelligent communication networks.

[0091] Figure 2 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, step S102 of the integrated communication and sensing game anti-interference method is described in detail, which involves obtaining the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV. The base station, the UAV, and the user terminal are identified as physical entities. The locations where interference signals are generated when the base station and the UAV send information to be transmitted to the user terminal are identified as interference points. The method includes:

[0092] S201. Obtain the location information of the interfering end, and determine the initial beamforming of the interfering end based on the location information of the interfering end.

[0093] In this embodiment, the UAV's perception system first relies on its onboard detection equipment, such as directional antenna arrays, signal angle of arrival estimation modules, or composite sensors integrating radio frequency sensing and vision, to detect and locate malicious interference sources in space. It is necessary not only to identify the presence of interference but also to accurately obtain the location information of the interfering party in three-dimensional space. The method for obtaining location information can be proactive, such as calculating the direction of arrival by analyzing the phase difference of the interference signal on different antenna elements and performing triangulation; or it can be passive, combining prior knowledge, such as focusing on scanning areas known to be vulnerable to attack.

[0094] S202. Based on the short-range algorithm, the initial beamforming at the interference end is optimized to obtain the optimized beamforming.

[0095] In this embodiment, the beamforming optimization at the interference end is complex because its objective function (aiming to maximize the interference effect on the communication link) and constraints (such as transmit power limits and beamforming vector magnitude constraints), as well as variables such as beamforming and UAV trajectory at the physical end, exhibit highly nonlinear and mutually coupled relationships. Therefore, this application first relaxes the problem by introducing additional auxiliary variables to decouple the original constraints, which partially simplifies the problem structure and creates conditions for subsequent algorithm processing. Next, by applying methods such as fractional programming and penalized dual averaging, the form of the objective function is deeply reconstructed. Essentially, this maps the original problem with complex fractions and constraints into a more regular mathematical form. In this process, by introducing carefully designed penalty factors and distance functions, it can be ensured that the relaxed or transformed variables and constraints during optimization eventually converge to a physically valid solution. This series of mathematical operations ultimately transforms the originally intractable non-convex problem into a sub-problem framework that can be effectively solved using standard convex optimization techniques (such as quadratic programming).

[0096] S203. Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized to obtain the optimized trajectory.

[0097] In this embodiment, Figure 3 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 3 In this embodiment Figure 2 Based on the embodiments, this paper provides a detailed explanation of step S203 in the integrated communication and sensing game-theoretic anti-interference method, which describes how to obtain the optimized trajectory of the UAV. The method includes:

[0098] S301. Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized using the maximization-minimization algorithm to obtain a new optimized trajectory for the UAV.

[0099] In this embodiment, under the worst-case scenario of optimized beamforming at the jamming end (i.e., the currently calculated optimal jamming strategy of the "enemy"), the UAV trajectory is optimized to ensure the best possible performance for the physical end under the worst jamming conditions. Specifically, the algorithm aims to maximize the lower bound of system performance in the worst jamming scenario, such as the minimum achievable rate or the worst signal-to-interference-plus-noise ratio. In this way, the designed trajectory is inherently robust to jamming because its optimization objective is to defend against the most severe impact that the jamming end can cause.

[0100] In practice, solving for a new UAV trajectory using the MM algorithm (Maximum-Minimum Algorithm) is an iterative approximation process. First, the algorithm formulates the UAV trajectory optimization problem as maximizing an objective function (such as system throughput or outage probability) under optimal beamforming constraints at the interference point. Since this problem is typically non-convex and difficult to solve directly, the MM algorithm constructs a "substitute function" at each iteration. This substitute function is equal to the original objective function at the current location (current trajectory point) and is no greater than the original objective function across the entire domain. This substitute function is usually convex or easier to handle. Then, the algorithm maximizes this substitute function to obtain a new, better set of UAV trajectory coordinates. This process is repeated continuously: starting with the new trajectory, a new substitute function is constructed and solved again until the trajectory converges to a suboptimal solution. For example, suppose that the current optimized beamforming at the jamming end is to concentrate energy to interfere with a specific airspace link between the UAV and the user. The MM algorithm may guide the UAV trajectory to optimize so that the UAV moves to a certain position. At this position, although the interference is strong, by optimizing its own trajectory geometry, the spatial separation (e.g., angle of arrival difference) between the interference signal and the desired signal is maximized. Thus, by using beamforming or spatial filtering, the interference can be suppressed as much as possible at the receiving end (user end), thereby improving the effective signal-to-interference-plus-noise ratio.

[0101] S302, Obtain the initial beamforming of the base station and the initial beamforming of the UAV.

[0102] In this embodiment, during the stage of determining the optimized trajectory of the UAV, neither the beamforming of the base station nor the beamforming of the UAV is optimized; only the initial beamforming parameters are used. This process can accurately achieve targeted optimization of the UAV's trajectory, eliminating interference from the beamforming of the base station and the UAV.

[0103] S303. Based on the initial beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station. Based on the initial beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. Based on the information to be transmitted sent by the base station and the UAV, the second objective function value of the entity terminal is determined.

[0104] In this embodiment, the base station transmits information to the user terminal according to NOMA. Simultaneously, to reduce mutual interference between users and improve communication quality, signal interference between users is eliminated using SIC technology. The signal-to-interference-plus-noise ratio (SIR) of the information to be transmitted by the base station to the k-th user based on initial beamforming is obtained according to the following formula:

[0105]

[0106] in, Let w be the channel coefficient between the base station and the k-th user. b,k [n] represents the beam transmitted by the base station to the k-th user in the nth time slot; v k,l [n] represents the SIC coefficient for the nth time slot, with the goal of ensuring that users can sequentially use SIC technology to eliminate interference between users; w b,l [n] represents the beam transmitted by the base station to the l-th user in the nth time slot; J k [n] represents the sum of the interference signal transmitted by the interference terminal in the nth time slot and the additive white Gaussian noise.

[0107] The UAV transmits the information to be sent to the user using SDMA. The signal-to-interference-plus-noise ratio (SIR) of the information to be sent by the UAV to the k-th user based on the initial beamforming and the new optimized trajectory is obtained according to the following formula:

[0108]

[0109] in, Let w be the channel coefficient between the UAV in the nth time slot and the kth user; t,k [n] represents the communication beam that the UAV sends to the k-th user in the nth time slot; w t,l [n] represents the communication beam that the UAV sends to the l-th user in the nth time slot; w r,i [n] represents the sensing beam emitted by the UAV in the nth time slot. For the kth user, the sensing beam is considered interference; J k [n] represents the sum of the interference signal transmitted by the interference terminal in the nth time slot and the additive white Gaussian noise.

[0110] Furthermore, based on the "Maximum Ratio Combination (MRC)" technology, the user combines the signal directly transmitted from the base station with the signal relayed by the drone. This technology assigns appropriate weights to the two signals (the sum of the weights has a fixed requirement), which can maximize the final received signal quality. Based on the received signal quality of multiple users, the second objective function value proposed in step S303 is obtained:

[0111]

[0112] Where, γ k [n] represents the value of the second objective function between the k-th user, the base station, and the drone; w m,b [n] represents the weight of the information to be sent by the base station to the k-th user; γ b,k→k [n] represents the signal-to-interference-plus-noise ratio (SIR) of the base station transmitting information to the k-th user; w m,u [n] represents the weight of the message to be sent from the UAV station to the k-th user; γu,k [n] represents the signal-to-interference-plus-noise ratio (SIR) of the communication when the UAV sends information to the k-th user.

[0113] In one possible implementation, during the process of the UAV sending information to the user, the reflected information from the interfering end is obtained based on the sensing information sent by the UAV to the interfering end. The signal quality value of the reflected information is then obtained based on this reflected information. It is determined whether the signal quality value of the reflected information meets the sensing threshold. If it does, the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV. If not, the trajectory of the UAV continues to be optimized until the signal quality value of the reflected information meets the sensing threshold. The significance of this step is that when optimizing the UAV's trajectory, it is necessary to constantly monitor the signal quality between the UAV and the interfering end, as the signal quality between them is a crucial factor affecting the subsequent evaluation and determination of the final target trajectory of the UAV.

[0114] S304. Repeat steps S301 to S303 until the second objective function value of the entity meets the fourth preset threshold, or the second value of repeating S301 to S303 meets the fifth preset threshold. Then, the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

[0115] In this embodiment, by repeating the above steps, the trajectory of the UAV can be optimized, thereby obtaining the optimized trajectory of the UAV.

[0116] S204. Based on the optimized beamforming at the interference end, optimize the initial beamforming of the base station and the initial beamforming of the UAV to obtain the optimized beamforming of the base station and the optimized beamforming of the UAV.

[0117] Figure 4 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 3 In this embodiment Figure 2 Based on the embodiments, this paper provides a detailed explanation of step S204 in the integrated communication and sensing game-theoretic anti-interference method, specifically how to obtain the optimized beamforming of the base station and the optimized beamforming of the UAV. The method includes:

[0118] S401. Based on the optimized beamforming at the interference end, the initial beamforming of the base station is optimized using a semi-definite relaxation algorithm to obtain a new optimized beamforming for the base station.

[0119] In this embodiment, given the optimized trajectory of the UAV and its transmitted beamforming, the base station's beamforming optimization still faces several non-convex constraints. A semi-definite relaxation algorithm is used to transform the variables into a semi-definite matrix form. An auxiliary variable is introduced to handle the fractional constraints, further transforming the problem into a semi-definite programming problem, which can then be approximated using Gaussian randomization. A key variable substitution is introduced: the product of the original beamforming vector and its conjugate transpose is defined as a new matrix variable. This new variable naturally satisfies the conditions for a positive semi-definite matrix. Through this substitution, the original non-convex optimization of the beamforming vector is cleverly relaxed into a convex optimization problem concerning this new matrix variable. This convex problem can be efficiently solved using mature algorithms such as the interior-point method.

[0120] S402. Based on the optimized beamforming at the interference end, the initial beamforming of the UAV is optimized using the successive convex approximation algorithm to obtain a new optimized beamforming for the UAV.

[0121] In this embodiment, a complex non-convex global optimization problem is transformed into a series of efficiently solvable convex subproblems through iteration and local approximation. The core process begins with an initial beamforming vector as the starting point. In each iteration, the algorithm performs a convex approximation of the original non-convex objective function or constraints around the current point. This approximation typically uses techniques such as first-order Taylor expansion to replace the original function with a simpler convex function that is locally "tangent" to it, thus constructing a reliable convex proxy problem near the current position. Solving this convex proxy problem yields an improved solution for the current position, which is then used as the starting point for the next iteration, initiating a new local approximation. This process repeats, forming an "approximation-solution-update" loop, allowing the beamforming scheme to iterate gradually along the direction of performance improvement, much like a blind person climbing a mountain, eventually converging to a suboptimal solution to the original non-convex problem.

[0122] This paper optimizes beamforming for UAVs based on a successive convex approximation algorithm. By "convexifying" non-convex functions, it ensures that the subproblems in each iteration are computationally friendly and can be solved quickly using efficient algorithms such as the interior-point method, thus keeping the computational complexity within an acceptable range. This is crucial for UAV platforms with limited computing power and energy, making it possible to perform real-time or near-real-time beamforming optimization on airborne equipment.

[0123] S403. Based on the new optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station. Based on the new optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. Based on the information to be transmitted sent by the base station and the UAV, the third objective function value of the entity is determined.

[0124] In this embodiment, the method for obtaining the third objective function value is the same as in step S303. However, in this step, the base station transmits information based on the new optimized beamforming, and the UAV transmits information to the user terminal based on the new optimized beamforming and the optimized trajectory of the UAV determined in S304. The implementation principle and technical effect are similar, and will not be described in detail here.

[0125] S404. Repeat steps S401 to S403 until the third objective function value satisfies the sixth preset threshold, or the third value of repeating S401 to S403 satisfies the seventh preset threshold. Then, the new optimized beamforming of the base station is determined as the first optimized beamforming of the base station, and the new optimized beamforming of the UAV is determined as the first optimized beamforming of the UAV.

[0126] In this embodiment, by repeating S401 to S403, the beamforming of the UAV and the base station can be optimized, thereby obtaining the first optimized beamforming of the base station and the first optimized beamforming of the UAV.

[0127] In one possible implementation, a new optimized beamforming for the base station and the UAV is obtained based on an alternating optimization method. The alternating optimization method, for example, involves first obtaining the new optimized beamforming for the base station in the first optimization. After optimizing the base station beamforming, the UAV beamforming is then optimized. After optimizing the UAV beamforming, a third objective function value is calculated, and it is determined whether it meets a sixth preset threshold. If it does not meet the sixth preset threshold, the base station beamforming is optimized again, and the UAV beamforming is further optimized. Based on the second optimized base station and UAV beamformings, the third objective function value is calculated again, until the third objective function value meets the sixth preset threshold, or the third value obtained by repeating steps S401 to S403 meets a seventh preset threshold. At this point, the new optimized beamforming for the base station is determined as the first optimized beamforming for the base station, and the new optimized beamforming for the UAV is determined as the first optimized beamforming for the UAV. This method reduces the problem's solution complexity and ensures the feasibility of the solution process. Because there is a strong nonlinear interaction between base station beamforming optimization (which typically involves the weight design of high-dimensional antenna arrays) and UAV beamforming optimization (which is tightly coupled with dynamic position, attitude, and antenna configuration in three-dimensional space), directly solving them together results in extremely high dimensionality. Alternating optimization decouples the original problem, making the structure of individual subproblems more regular at each step after fixing other variables. It may even transform into a convex optimization problem (for example, after fixing other variables, base station beamforming optimization may become a quadratic constrained quadratic programming problem, which can be solved efficiently using semidefinite relaxation methods), or it can be handled using mature numerical methods. This makes it possible to achieve real-time or near-real-time optimization in practical systems with limited computational resources.

[0128] S405. If the value of the third objective function satisfies the eighth preset threshold, then the first optimized beamforming of the base station is determined as the optimized beamforming of the base station, and the first optimized beamforming of the UAV is determined as the optimized beamforming of the UAV.

[0129] In this embodiment, after determining that the new optimized beamforming of the base station is the first optimized beamforming of the base station and the new optimized beamforming of the UAV is the first optimized beamforming of the UAV, a threshold judgment is performed again on the third objective function value. The purpose is to further optimize the obtained beamforming of the base station and the UAV. If the third objective function value meets the eighth preset threshold, then the first optimized beamforming of the base station is determined as the optimized beamforming of the base station and the first optimized beamforming of the UAV is determined as the optimized beamforming of the UAV. The determined optimized beamforming of the base station and the optimized beamforming of the UAV can be applied to the subsequent step S205.

[0130] S406. If the value of the third objective function does not meet the eighth preset threshold, then the optimized trajectory of the UAV is optimized to obtain a new optimized trajectory of the UAV; the first optimized beamforming of the base station is optimized to obtain the second optimized beamforming of the base station; the first optimized beamforming of the UAV is optimized to obtain the second optimized beamforming of the UAV.

[0131] In this embodiment, if the third objective function value does not meet the eighth preset threshold, then the optimized trajectory of the UAV obtained in step S304 needs to be re-optimized. When optimizing the optimized trajectory of the UAV, the contents of steps S301 to S304 need to be executed again. In this case, the optimized trajectory of the UAV is optimized based on the optimized beamforming of the interference end and the maximization-minimization algorithm to obtain a new optimized trajectory of the UAV. When calculating the second objective function value of the entity end in steps S303 to S304, the base station uses the first optimized beamforming of the base station determined in step S404, and the UAV uses the first optimized beamforming of the UAV determined in step S404.

[0132] In one possible implementation, after determining that the value of the second objective function satisfies the fourth preset threshold, or after repeating steps S301 to S303 a second time to satisfy the fifth preset threshold, a new optimized trajectory for the UAV is obtained. Further, steps S401 to S404 are executed. Specifically, based on the optimized beamforming at the interference end, the first optimized beamforming is optimized using a semi-definite relaxation algorithm to obtain the second optimized beamforming for the base station. Based on the optimized beamforming at the interference end, the first optimized beamforming for the UAV is optimized using a successive convex approximation algorithm to obtain the second optimized beamforming for the UAV. The optimization method here is consistent with the algorithms in steps S401 to S404, and its implementation principle and technical effects are similar; therefore, it will not be elaborated upon here. Simultaneously, when optimizing the first optimized beamforming for the base station and the first optimized beamforming for the UAV, an alternating optimization algorithm is used.

[0133] S407. Based on the second optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station. Based on the second optimized beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. Based on the information to be transmitted sent by the base station and the UAV, a new third objective function value is determined for the entity.

[0134] In this embodiment, the method for obtaining the third objective function value is the same as in step S303. However, in this step, the base station transmits information based on the second optimized beamforming, and the UAV transmits information to the user terminal based on the second optimized beamforming and the new optimized trajectory of the UAV determined in S406. The implementation principle and technical effect are similar, and will not be elaborated upon here. Similarly, during the process of the UAV transmitting information to the user terminal, the reflection information of the interference terminal is obtained based on the sensing information transmitted by the UAV to the interference terminal; the signal quality value of the reflected information is obtained based on the reflection information of the interference terminal; it is determined whether the signal quality value of the reflected information meets the sensing threshold. If so, the new optimized trajectory of the UAV meets the requirements. The significance of this step is that when optimizing the UAV trajectory, it is necessary to constantly monitor the signal quality between the UAV and the interference terminal, because the signal quality between the UAV and the interference terminal is a crucial influencing factor in the subsequent evaluation of the final target trajectory of the UAV.

[0135] S408. Repeat S406 to S407 until the new third objective function value satisfies the eighth preset threshold, or repeat S406 to S407 a fourth time until the value satisfies the ninth preset threshold. Then, the second optimized beamforming of the base station is determined as the optimized beamforming of the base station, the second optimized beamforming of the UAV is determined as the optimized beamforming of the UAV, and the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

[0136] In this embodiment, by repeating S406 to S407, the beamforming of the base station can be further optimized, as well as the trajectory and beamforming of the UAV can be further optimized, thereby finally determining the optimized trajectory of the UAV in S203 and the optimized beamforming of the base station and the UAV in step S204.

[0137] S205. Based on the optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station. Based on the optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. Based on the information to be transmitted sent by the base station and the UAV, the objective function value of the interference end and the first objective function value of the entity end are determined.

[0138] In this embodiment, the method for obtaining the first objective function value is the same as in step S303. However, in this step, the base station transmits information based on optimized beamforming, and the UAV transmits information to the user terminal based on optimized beamforming and the UAV's optimized trajectory. The implementation principle and technical effects are similar, and will not be elaborated upon here. Similarly, during the process of the UAV transmitting information to the user terminal, the reflection information of the interference terminal is obtained based on the sensing information transmitted by the UAV to the interference terminal; the signal quality value of the reflected information is obtained based on the reflection information of the interference terminal; it is then determined whether the signal quality value of the reflected information meets the sensing threshold. If so, the optimized trajectory of the UAV meets the requirements. The significance of this step lies in the need to constantly monitor the signal quality between the UAV and the interference terminal when optimizing the UAV's trajectory, as the signal quality between the UAV and the interference terminal is a crucial influencing factor in the subsequent evaluation of the final target trajectory of the UAV.

[0139] S206. If the objective function value of the interfering end does not meet the first preset threshold, or the first objective function value of the entity end does not meet the second preset threshold, then repeat steps S202 to S205 until the objective function value of the interfering end meets the first preset threshold and the first objective function value of the entity end meets the second preset threshold, or the first value of the above steps meets the third preset threshold. Then, the optimized trajectory of the UAV is determined as the target trajectory of the UAV, the optimized beamforming of the base station is determined as the target beamforming of the base station, the optimized beamforming of the UAV is determined as the target beamforming of the UAV, and the optimized beamforming of the interfering end is determined as the target beamforming of the interfering end.

[0140] In this embodiment, by repeating the above steps, optimizing the drone's trajectory and beamforming as well as the base station's beamforming, until the target function value of the interference end meets the first preset threshold and the first target function value of the entity end meets the second preset threshold, or by repeating the first value of steps S202 to S205 to meet the third preset threshold, the target trajectory of the drone, the target beamforming of the base station, and the target beamforming of the drone can be determined.

[0141] Figure 5 Flowchart of the communication-sensing integrated game-theoretic anti-interference method provided in this application Figure 5 ,like Figure 5 As shown, in this embodiment... Figure 1 Based on the examples, the communication-sensing integrated game-theoretic anti-interference method is described in detail. The method includes:

[0142] S501. The base station, drone, and user terminal are identified as physical entities. The location where interference signals are generated when the base station and drone send information to be sent to the user terminal is identified as the interference point.

[0143] S502. Based on the near-range algorithm, the initial beamforming at the interference end is optimized to obtain the optimized beamforming.

[0144] S503. Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized to obtain the optimized trajectory.

[0145] S504. Determine the second objective function value of the entity based on the information to be transmitted by the base station and the drone.

[0146] In this embodiment, it is necessary to obtain the initial beamforming of the base station and the initial beamforming of the UAV; based on the initial beamforming of the base station, the information to be sent is sent to the user terminal based on the base station; based on the initial beamforming of the UAV and the new optimized trajectory of the UAV, the information to be sent is sent to the user terminal based on the UAV.

[0147] S505. Determine whether the value of the second objective function meets the fourth preset threshold, or whether the second value of the repeated steps S503 to S504 meets the fifth preset threshold. If the value of the second objective function meets the fourth preset threshold, or the second value of the repeated steps S503 to S504 meets the fifth preset threshold, then the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV, and step S506 is executed. If not, execute steps S503 to S504 until the value of the second objective function meets the fourth preset threshold, or the second value of the repeated steps S503 to S505 meets the fifth preset threshold.

[0148] In this embodiment, the second objective function is L2; ​​the fourth preset threshold is P4; the second value after repeating steps S503 to S504 is M2; and the fifth preset threshold is P5.

[0149] S506. Based on the optimized beamforming at the interference end, the initial beamforming of the base station is optimized using a semi-definite relaxation algorithm to obtain a new optimized beamforming for the base station.

[0150] S507. Based on the optimized beamforming at the interference end, the initial beamforming of the UAV is optimized using the successive convex approximation algorithm to obtain a new optimized beamforming for the UAV.

[0151] In this embodiment, based on the alternating optimization method, new optimized beamforming for the base station and new optimized beamforming for the UAV are obtained.

[0152] S508. Determine the third objective function value of the entity based on the information to be transmitted sent by the base station and the drone.

[0153] In this embodiment, based on the new optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the new optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0154] S509. Determine whether the third objective function value meets the sixth preset threshold, or whether the third value of repeating steps S506 to S508 meets the seventh preset threshold. If the third objective function value meets the sixth preset threshold, or the third value of repeating steps S506 to S508 meets the seventh preset threshold, then the new optimized beamforming of the base station is determined as the first optimized beamforming of the base station, and the new optimized beamforming of the UAV is determined as the first optimized beamforming of the UAV. Then, execute step S510. If not, execute steps S506 to S508 until the third objective function value meets the sixth preset threshold, or the third value of repeating steps S506 to S508 meets the seventh preset threshold.

[0155] In this embodiment, the third objective function is L3; the sixth preset threshold is P6; the third value of repeating steps S506 to S508 is M3; and the sixth preset threshold is P7.

[0156] S510. If the third objective function value satisfies the eighth preset threshold, then the first optimized beamforming of the base station is determined as the optimized beamforming of the base station, and the first optimized beamforming of the UAV is determined as the optimized beamforming of the UAV. That is, continue to execute S511. If the third objective function value does not satisfy the eighth preset threshold, then execute S503 to S509 again, that is, optimize the optimized trajectory of the UAV to obtain a new optimized trajectory of the UAV. That is, execute steps S503 to S504 until step S505 is satisfied, thereby obtaining a new optimized trajectory of the UAV. Further execute step S506 to optimize the first optimized beamforming of the base station to obtain the second optimized beamforming of the base station. Execute step S507 to optimize the first optimized beamforming of the UAV. The second optimized beamforming of the UAV is obtained; based on the second optimized beamforming of the base station, step S508 is executed to send the information to be sent to the user terminal based on the base station, and based on the second optimized beamforming of the UAV and the new optimized trajectory of the UAV, the information to be sent to the user terminal is sent based on the UAV; step S509 is executed to determine the new third objective function value of the entity based on the information to be sent by the base station and the UAV; until the new third objective function value meets the eighth preset threshold, or the fourth number of repetitions of the above steps meets the ninth preset threshold, then the second optimized beamforming of the base station is determined as the optimized beamforming of the base station, the second optimized beamforming of the UAV is determined as the optimized beamforming of the UAV, and the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

[0157] In this embodiment, the third objective function is L3; the eighth preset threshold is P8; the fourth number of repetitions of steps S503 to S509 is M4; and the sixth preset threshold is P9.

[0158] S511. Based on the information to be transmitted sent by the base station and the UAV, determine the target function value of the interference end and the first target function value of the entity end.

[0159] S512. If the target function value W of the interfering end satisfies the first preset threshold and the first target function value of the entity end satisfies the second preset threshold, then the target beamforming of the UAV, the target beamforming of the base station, and the target trajectory of the UAV are obtained. If the target function value W of the interfering end does not satisfy the first preset threshold, or the first target function value of the entity end does not satisfy the second preset threshold, then S502 to S511 are executed until the target function value of the interfering end satisfies the first preset threshold and the first target function value of the entity end satisfies the second preset threshold, or the first value of S502 to S511 is repeated until the third preset threshold is satisfied. Then the optimized trajectory of the UAV is determined as the target trajectory of the UAV, the optimized beamforming of the base station is determined as the target beamforming of the base station, the optimized beamforming of the UAV is determined as the target beamforming of the UAV, and the optimized beamforming of the interfering end is determined as the target beamforming of the interfering end.

[0160] In this embodiment, the objective function value of the interference end is W, the first preset threshold is P, the first objective function is L1, the second preset threshold is P2, the first value of repeating steps S502 to S511 is M1, and the third preset threshold is P3.

[0161] The communication-sensing integrated game-theoretic anti-interference method provided in this application has the core significance and advantage of achieving a qualitative leap in system performance in an active adversarial environment by deeply coupling the perception, communication, and dynamic anti-interference capabilities of the UAV. Specifically, this method innovatively elevates the UAV from a traditional relay node to a game participant with environmental perception and intelligent decision-making capabilities. The UAV first uses its perception capabilities to locate and model the interference source, quantifying the external threat as an adversarial agent in the game model, and initiating an alternating optimization dynamic game process based on this. In this process, the UAV's trajectory, beamforming, and the base station's beamforming are jointly optimized, and each optimization step fully considers the optimal adversarial strategy that the interfering party may adopt. Finally, the base station target beamforming, UAV target beamforming, and target trajectory obtained through game convergence represent an optimal or near-optimal balance strategy achieved under adversarial conditions. This strategy enables the base station's transmission energy to avoid interference directions through precise beamforming, while the UAV's trajectory and beamforming can dynamically coordinate in three-dimensional space. On the one hand, it actively avoids the airspace with the strongest interference by utilizing high maneuverability; on the other hand, it complements and enhances the base station's beam in space. Thus, even under interference conditions, it can still build a highly reliable, high-capacity enhanced relay link for users. Therefore, this method fundamentally transforms anti-interference from a passive "endurance-compensation" mode to an active "perception-prediction-avoidance-countermeasure" integrated mode, thereby significantly improving the information transmission quality and system robustness of information sent to users in complex malicious interference environments.

[0162] Figure 6 A schematic diagram of the integrated communication and sensing game anti-interference device provided in this application is shown below. Figure 6 As shown, the communication-sensing integrated game-playing anti-interference device 60 provided in this embodiment includes:

[0163] The acquisition module 601 is used to acquire the information to be sent by the base station to the user terminal;

[0164] The processing module 602 is used to acquire the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV; wherein, the target beamforming of the base station is used to characterize the transmission phase and amplitude of the base station's antenna, the target trajectory of the UAV is used to characterize the trajectory of the UAV transmitting signals to the user terminal, and the target beamforming of the UAV is used to characterize the angle at which the UAV transmits signals to the user terminal.

[0165] The transmitting module 603 is used to transmit the information to be transmitted to the user terminal based on the target beamforming of the base station;

[0166] The transmitting module 603 is used to transmit the information to be transmitted to the user terminal based on the target trajectory and target beamforming of the UAV.

[0167] In one possible implementation, the processing module 602 is further configured to:

[0168] Obtain the location information of the interfering end, and determine the initial beamforming of the interfering end based on the location information of the interfering end;

[0169] Based on the short-range algorithm, the initial beamforming at the interference end is optimized to obtain the optimized beamforming;

[0170] Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized to obtain the optimized trajectory;

[0171] Based on the optimized beamforming at the interference end, the initial beamforming of the base station and the initial beamforming of the UAV are optimized to obtain the optimized beamforming of the base station and the optimized beamforming of the UAV.

[0172] Based on the optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station. Based on the optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0173] Based on the information to be transmitted from the base station and the drone, determine the target function value of the interference end and the first target function value of the entity end;

[0174] If the objective function value of the interfering end does not meet the first preset threshold, or the first objective function value of the entity end does not meet the second preset threshold, then repeat the above steps until the objective function value of the interfering end meets the first preset threshold and the first objective function value of the entity end meets the second preset threshold, or the first value of the repeated steps meets the third preset threshold. Then, the optimized trajectory of the UAV is determined as the target trajectory of the UAV, the optimized beamforming of the base station is determined as the target beamforming of the base station, the optimized beamforming of the UAV is determined as the target beamforming of the UAV, and the optimized beamforming of the interfering end is determined as the target beamforming of the interfering end.

[0175] In one possible implementation, the processing module 602 is further configured to:

[0176] Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized using the maximization-minimization algorithm to obtain a new optimized trajectory for the UAV.

[0177] Acquire the initial beamforming of the base station and the initial beamforming of the UAV;

[0178] Based on the initial beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the initial beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0179] The second objective function value of the entity is determined based on the information to be transmitted from the base station and the drone.

[0180] Repeat the above steps until the second objective function value of the entity meets the fourth preset threshold, or the second value of the above steps meets the fifth preset threshold. Then, the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

[0181] In one possible implementation, the processing module 602 is further configured to:

[0182] Based on the optimized beamforming at the interference end, the initial beamforming of the base station is optimized using a semi-definite relaxation algorithm to obtain a new optimized beamforming for the base station.

[0183] Based on the optimized beamforming at the interference end, the initial beamforming of the UAV is optimized using a successive convex approximation algorithm to obtain a new optimized beamforming for the UAV.

[0184] Based on the new optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the new optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0185] The third objective function value of the entity is determined based on the information to be transmitted from the base station and the drone.

[0186] Repeat the above steps until the third objective function value satisfies the sixth preset threshold, or the third value of the above steps satisfies the seventh preset threshold. Then, the new optimized beamforming of the base station is determined as the first optimized beamforming of the base station, and the new optimized beamforming of the UAV is determined as the first optimized beamforming of the UAV.

[0187] In one possible implementation, the processing module 602 is further configured to:

[0188] Based on the alternating optimization method, new optimized beamforming for the base station and new optimized beamforming for the UAV are obtained.

[0189] In one possible implementation, the processing module 602 is further configured to:

[0190] If the value of the third objective function satisfies the eighth preset threshold, then the first optimized beamforming of the base station is determined as the optimized beamforming of the base station, and the first optimized beamforming of the UAV is determined as the optimized beamforming of the UAV.

[0191] If the value of the third objective function does not meet the eighth preset threshold, the optimized trajectory of the UAV will be optimized to obtain a new optimized trajectory of the UAV.

[0192] The first optimized beamforming of the base station is further optimized to obtain the second optimized beamforming of the base station.

[0193] The first optimized beamforming of the UAV is further optimized to obtain the second optimized beamforming of the UAV.

[0194] Based on the second optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the second optimized beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV.

[0195] Based on the information to be transmitted from the base station and the drone, determine the new third objective function value of the entity.

[0196] Repeat the above steps until the new third objective function value satisfies the eighth preset threshold, or the fourth number of repetitions of the above steps satisfies the ninth preset threshold. Then, the second optimized beamforming of the base station is determined as the optimized beamforming of the base station, the second optimized beamforming of the UAV is determined as the optimized beamforming of the UAV, and the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

[0197] In one possible implementation, the processing module 602 is further configured to:

[0198] After obtaining the optimized beamforming of the base station, the optimized beamforming of the UAV, and the optimized trajectory of the UAV, the reflection information of the interference end is obtained based on the sensing information sent by the UAV to the interference end.

[0199] Based on the reflection information from the interfering end, the signal quality value of the reflected information is obtained;

[0200] Determine whether the signal quality value of the reflected information meets the perception threshold. If so, determine the optimized trajectory of the UAV as the target trajectory of the UAV, determine the optimized beamforming of the base station as the target beamforming of the base station, determine the optimized beamforming of the UAV as the target beamforming of the UAV, and determine the optimized beamforming of the interference end as the target beamforming of the interference end.

[0201] If not, continue to optimize the drone's trajectory, base station, and drone beamforming until the signal quality value of the reflected information meets the perception threshold.

[0202] The communication-sensing integrated game anti-interference device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0203] Figure 7 This is a hardware schematic diagram of the integrated communication-sensing game-playing anti-interference device provided in this application. Figure 7 As shown, the electronic device 70 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the device 70 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0204] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 701 to perform the above-described method.

[0205] The specific implementation process of processor 701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0206] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0207] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0208] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0209] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0210] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0211] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0212] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0213] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, methods, or units, and may be electrical, mechanical, or other forms.

[0214] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0215] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0216] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0217] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0218] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A communication-sensing integrated game theory anti-interference method, characterized in that, include: Obtain the information that the base station needs to send to the user terminal; The target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV are obtained; wherein, the target beamforming of the base station is used to characterize the transmission phase and amplitude of the antenna of the base station, the target trajectory of the UAV is used to characterize the trajectory of the UAV transmitting signals to the user terminal, and the target beamforming of the UAV is used to characterize the angle at which the UAV transmits signals to the user terminal. Based on the target beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; Based on the target trajectory and target beamforming of the UAV, the information to be sent is transmitted to the user terminal based on the UAV.

2. The method according to claim 1, characterized in that, The process of acquiring the target base station beamforming, the UAV target trajectory, and the UAV target beamforming, identifying the base station, the UAV, and the user terminal as physical entities, and identifying the locations where interference signals are generated when the base station and the UAV transmit the information to be transmitted to the user terminal as interference points, includes: Obtain the location information of the interfering end, and determine the initial beamforming of the interfering end based on the location information of the interfering end; Based on the near-range algorithm, the initial beamforming of the interference end is optimized to obtain the optimized beamforming; Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized to obtain the optimized trajectory; Based on the optimized beamforming of the interference end, the initial beamforming of the base station and the initial beamforming of the UAV are optimized to obtain the optimized beamforming of the base station and the optimized beamforming of the UAV. Based on the optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. Based on the information to be transmitted sent by the base station and the user terminal, the objective function value of the interference terminal and the first objective function value of the entity terminal are determined; If the objective function value at the interference end does not meet the first preset threshold, or the first objective function value at the entity end does not meet the second preset threshold, then repeat the above steps until the objective function value at the interference end meets the first preset threshold. and If the first objective function value of the entity meets the second preset threshold, or if the first value of the above steps meets the third preset threshold, then the optimized trajectory of the UAV is determined as the target trajectory of the UAV, the optimized beamforming of the base station is determined as the target beamforming of the base station, the optimized beamforming of the UAV is determined as the target beamforming of the UAV, and the optimized beamforming of the interference end is determined as the target beamforming of the interference end.

3. The method according to claim 2, characterized in that, The step of optimizing the initial trajectory of the UAV based on the optimized beamforming at the interference end to obtain the optimized trajectory includes: Based on the optimized beamforming at the interference end, the initial trajectory of the UAV is optimized using a maximization-minimization algorithm to obtain a new optimized trajectory for the UAV. Obtain the initial beamforming of the base station and the initial beamforming of the UAV; Based on the initial beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the initial beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. Based on the information to be transmitted sent by the base station and the drone, determine the second objective function value of the entity terminal; Repeat the above steps until the second objective function value of the entity meets the fourth preset threshold, or the second value of the above steps meets the fifth preset threshold, then the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

4. The method according to claim 2, characterized in that, The step of optimizing the initial beamforming of the base station and the initial beamforming of the UAV based on the optimized beamforming of the interference end, to obtain the optimized beamforming of the base station and the optimized beamforming of the UAV, includes: Based on the optimized beamforming of the interference end, the initial beamforming of the base station is optimized using a semi-definite relaxation algorithm to obtain a new optimized beamforming of the base station. Based on the optimized beamforming of the interference end, the initial beamforming of the UAV is optimized using a successive convex approximation algorithm to obtain a new optimized beamforming of the UAV. Based on the new optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the new optimized beamforming of the UAV and the optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. The third objective function value of the entity is determined based on the information to be transmitted sent by the base station and the user terminal. Repeat the above steps until the third objective function value satisfies the sixth preset threshold, or the third value of the above steps satisfies the seventh preset threshold. Then, the new optimized beamforming of the base station is determined as the first optimized beamforming of the base station, and the new optimized beamforming of the UAV is determined as the first optimized beamforming of the UAV.

5. The method according to claim 4, characterized in that, The method further includes: Based on the alternating optimization method, new optimized beamforming for the base station and new optimized beamforming for the UAV are obtained.

6. The method according to claim 4, characterized in that, After determining the new optimized beamforming of the base station as the first optimized beamforming of the base station, and determining the new optimized beamforming of the UAV as the first optimized beamforming of the UAV, the process includes: If the value of the third objective function satisfies the eighth preset threshold, then the first optimized beamforming of the base station is determined as the optimized beamforming of the base station, and the first optimized beamforming of the UAV is determined as the optimized beamforming of the UAV. If the value of the third objective function does not meet the eighth preset threshold, the optimized trajectory of the UAV is optimized to obtain a new optimized trajectory of the UAV. The first optimized beamforming of the base station is further optimized to obtain the second optimized beamforming of the base station. The first optimized beamforming of the UAV is further optimized to obtain the second optimized beamforming of the UAV. Based on the second optimized beamforming of the base station, the information to be transmitted is sent to the user terminal based on the base station; based on the second optimized beamforming of the UAV and the new optimized trajectory of the UAV, the information to be transmitted is sent to the user terminal based on the UAV. Based on the information to be transmitted sent by the base station and the user terminal, a new third objective function value is determined for the entity terminal; Repeat the above steps until the new third objective function value satisfies the eighth preset threshold, or the fourth number of repetitions of the above steps satisfies the ninth preset threshold. Then, the second optimized beamforming of the base station is determined as the optimized beamforming of the base station, the second optimized beamforming of the UAV is determined as the optimized beamforming of the UAV, and the new optimized trajectory of the UAV is determined as the optimized trajectory of the UAV.

7. The method according to any one of claims 1-6, characterized in that, Also includes: After obtaining the optimized beamforming of the base station, the optimized beamforming of the UAV, and the optimized trajectory of the UAV, the reflection information of the interference terminal is obtained based on the sensing information sent by the UAV to the interference terminal. Based on the reflection information from the interfering end, the signal quality value of the reflection information is obtained; Determine whether the signal quality value of the reflected information meets the perception threshold. If so, determine the optimized trajectory of the UAV as the target trajectory of the UAV, determine the optimized beamforming of the base station as the target beamforming of the base station, determine the optimized beamforming of the UAV as the target beamforming of the UAV, and determine the optimized beamforming of the interference end as the target beamforming of the interference end. If not, continue to optimize the trajectory of the UAV, the base station, and the beamforming of the UAV until the signal quality value of the reflected information meets the perception threshold.

8. A communication-sensing integrated game-playing anti-interference device, characterized in that, include: The acquisition module is used to acquire the information that the base station needs to send to the user terminal; The processing module is used to acquire the target beamforming of the base station, the target trajectory of the UAV, and the target beamforming of the UAV; wherein, the target beamforming of the base station is used to characterize the angle at which the base station transmits signals to the user terminal, the target trajectory of the UAV is used to characterize the trajectory at which the UAV transmits signals to the user terminal, and the target beamforming of the UAV is used to characterize the angle at which the UAV transmits signals to the user terminal. The transmitting module is used to transmit the information to be transmitted to the user terminal based on the target beamforming of the base station; The transmitting module is used to transmit the information to be transmitted to the user terminal based on the target trajectory and target beamforming of the UAV.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

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