Unmanned aerial vehicle cluster communication robust anti-interference method assisted by hybrid intelligent reflecting surface

Abstract

The invention requests to protect a hybrid intelligent reflector-assisted unmanned aerial vehicle cluster communication robust anti-interference method, and designs an alternative optimization algorithm based on block coordinate descent to jointly optimize and assist an unmanned aerial vehicle trajectory, hybrid intelligent reflector beam forming, active / passive unit allocation and a non-orthogonal multiple access communication strategy. Aiming at an auxiliary unmanned aerial vehicle trajectory optimization problem, a continuous convex approximation method is adopted to convert the problem into a convex problem which can be efficiently solved. In order to solve the problems of hybrid intelligent reflector configuration and optimization of non-orthogonal multiple access resource allocation, an algorithm based on semi-definite programming relaxation and an algorithm based on dynamic sorting and continuous convex approximation are respectively designed for solving, and it is theoretically proved that the complexity of the method is polynomial time. Simulation results prove that the method is effective in improving the average transmission rate of the system and guaranteeing low communication energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a robust anti-interference method for unmanned aerial vehicle cluster communication, which utilizes unmanned aerial vehicles carrying hybrid intelligent reflecting surfaces for assistance, and is particularly suitable for safe and efficient communication scenarios in complex electromagnetic environments such as low-altitude economy. BACKGROUND

[0002] In recent years, with the rise of low-altitude economy, unmanned aerial vehicle clusters have shown great potential in complex cooperative tasks, but their inherent broadcast nature makes them extremely vulnerable to malicious interference attacks, seriously threatening communication security. To address this challenge, intelligent reflecting surface technology suppresses interference and enhances signals by dynamically reconfiguring electromagnetic wave paths. However, existing intelligent reflecting surface technology has limitations: passive intelligent reflecting surfaces are energy-efficient but are affected by double fading; active intelligent reflecting surfaces can amplify signals, but also amplify interference and noise, and have high energy consumption. Current research still has many shortcomings, such as ignoring multi-user interference within the cluster, not fully utilizing the potential of hybrid intelligent reflecting surfaces, simplifying the scenario to static deployment or single-user communication, and relying on unrealistic perfect interference channel information assumptions, resulting in poor robustness of algorithms in real scenarios. Therefore, how to optimize the mobility of unmanned aerial vehicles, hybrid intelligent reflecting surface beamforming, and multi-user access strategies under imperfect interference channel information to address the intertwined challenges of mobility management, multi-user interference, resource allocation, and information uncertainty is a technical problem that needs to be solved to achieve robust and efficient communication for unmanned aerial vehicle clusters.

[0003] After searching, the application publication number CN115884093B, energy-efficient intelligent reflecting surface-assisted unmanned aerial vehicle communication anti-interference robust design method, includes the following specific steps: S1, obtaining global channel state information: due to the high altitude and high line-of-sight link characteristics of unmanned aerial vehicles, the channel of unmanned aerial vehicle communication is mainly line-of-sight channel. The present application provides an energy-efficient intelligent reflecting surface-assisted unmanned aerial vehicle communication anti-interference design to solve the problem of high energy consumption and low efficiency in the process of unmanned aerial vehicle communication anti-interference. In this method, the alternating optimization algorithm of continuous convex approximation, fractional programming, and S-procedure is used to continuously iterate and optimize the transmission power allocation, intelligent reflecting surface reflection coefficient, and unmanned aerial vehicle trajectory to continuously improve the receiving energy efficiency of the unmanned aerial vehicle until convergence.

[0004] However, intelligent reflector-assisted UAV anti-jamming technologies, as described above, still face three major challenges: intelligent reflector performance, application scenarios, and information uncertainty. Regarding intelligent reflector performance, while existing passive intelligent reflectors offer high energy efficiency, their signal enhancement capabilities are limited by the double fading effect. Active intelligent reflectors, while amplifying signals, also introduce problems such as amplified interference and noise, as well as higher energy consumption. In terms of application scenarios, most existing research simplifies scenarios to static deployment or single-user communication, neglecting multi-user interference within the swarm and failing to meet the real-world needs of dynamic UAV swarm collaboration. Furthermore, regarding information uncertainty, most algorithms rely on unrealistic assumptions of perfect interference channel information, resulting in poor robustness in real, complex electromagnetic environments and hindering their application.

[0005] To address the aforementioned challenges, this invention proposes a complete solution. First, addressing the performance limitations of intelligent reflectors, this invention innovatively employs a hybrid intelligent reflector combining active and passive elements. By collaboratively optimizing its beamforming and the allocation ratio of active and passive units, it effectively combats signal fading while achieving an intelligent balance between performance and energy consumption. Second, to closely align with real-world applications, this invention targets UAV swarm scenarios and introduces a non-orthogonal multiple access communication strategy. By dynamically optimizing user decoding order and power allocation, it efficiently manages multi-user interference. Crucially, this invention considers the practical constraint of imperfect interference source channel information from the outset. By modeling channel estimation errors, it ensures the robustness and practical value of the algorithm in environments of information uncertainty. Therefore, by jointly optimizing UAV trajectories, hybrid intelligent reflector configuration, and non-orthogonal multiple access resource allocation from multiple dimensions, this invention effectively addresses the intertwined challenges of mobility, multi-user interference, and information uncertainty, thereby achieving efficient, energy-saving, and reliable anti-interference communication. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a robust anti-jamming method for UAV swarm communication assisted by a hybrid intelligent reflector. This method aims to maximize the average transmission rate of the system while minimizing communication energy consumption under the constraint of imperfect interference source channel information by jointly optimizing UAV trajectories, hybrid intelligent reflector beamforming, active / passive cell allocation, and NOMA communication strategies. This provides an efficient, energy-saving, and robust anti-jamming communication framework for UAV swarms in the low-altitude economy. A robust anti-jamming method for UAV swarm communication assisted by a hybrid intelligent reflector is proposed. The technical solution of this invention is as follows:

[0007] A robust anti-jamming method for UAV swarm communication assisted by a hybrid intelligent reflector includes the following steps:

[0008] 1) Construct a model of a UAV swarm anti-jamming communication system assisted by a hybrid intelligent reflector, model the channel, energy consumption and non-orthogonal multiple access protocol, and on this basis, construct a multi-objective joint optimization problem that maximizes the average transmission rate of the system and minimizes the total communication energy consumption;

[0009] 2) Based on the model in step 1), establish a multi-objective optimization problem. To solve this highly coupled non-convex optimization problem, utilize... - Constraint methods transform it into a single-objective optimization problem;

[0010] 3) The transformed problem is decomposed into three coupled sub-problems: H-UAV trajectory optimization, hybrid IRS configuration optimization, and NOMA resource allocation optimization. For the three sub-problems, an alternating optimization iterative algorithm based on block coordinate descent is designed. The algorithm is solved alternately by continuous convex approximation, semidefinite programming relaxation, and dynamic sorting methods until the algorithm converges and obtains an approximate optimal solution.

[0011] Furthermore, the channel modeling in step 1) specifically includes;

[0012] Using a hybrid IRS-assisted unmanned aerial vehicle (UAV) H-UAV as an airborne relay, considering K UAVs and one H-UAV, the UAVs in the swarm use... This indicates that each drone is equipped with only one antenna; the total time is divided into... Each time slot has a length of [number] timeslots. equal, In each time slot, only one drone acts as the transmitter, while the other drones act as receivers to receive signals from the transmitter. The positions of all drones are fixed within each time slot but change between different time slots and can be modeled in a three-dimensional Cartesian coordinate system. At that time, drones The coordinate positions of H-UAV are respectively represented as and , , Assume the intelligent jammer is located at a fixed position on the ground, and its coordinate position is defined as follows: drones In the time slot The speed is defined as , , ;

[0013] Considering the IRS carried by the drone It consists of a uniform planar array, that is ,in , and These represent the IRS reflective elements along... shaft and The number of axes; IRS reflector units are divided into active and passive types; passive IRS reflector units only support phase shift adjustment of the incident signal; active IRS reflector units can not only adjust the phase shift of the incident signal, but also amplify the signal through a reflective amplifier; the number of active reflector units in a hybrid IRS is defined as... It can be used It means that, among them The rest are passive reflective units; in the time slot The reflection vector of the IRS can be expressed as: and , , ;in It is the imaginary unit. and These represent the phase shift and reflection amplitude of the IRS reflector, respectively; when hour, ;when hour, ;

[0014] A. Channel Model

[0015] Communication channels between drones can be divided into: direct channels and reflected channels; time slots From drones To drones The direct channel is modeled as a Ricean fading channel:

[0016]

[0017] in , It is a drone and drones The distance between them; Indicates drone and drones Channel coefficients between Indicates drone Location coordinates Indicates drone The location coordinates. This indicates the path loss at 1 meter. Indicates drone and drones Path loss index between The corresponding Rice factor is represented as; the small-scale fading component is represented as... It follows a complex Gaussian distribution with a mean of 0 and a variance of 1, while It is the phase term of the line-of-sight link;

[0018] The reflection link includes the link from the transmitting UAV to the IRS and the link from the IRS to the receiving UAV; therefore, a line-of-sight link channel model is adopted for the link from the IRS to the UAV; specifically, from the UAV... The channel to the IRS can be represented as:

[0019]

[0020] in , Indicates drone Distance between and IRS; variable The receiver array response representing the IRS can be obtained in the following ways:

[0021]

[0022] in and These are two time slots The expressions related to the azimuth and elevation angles of arrival are shown in the figure. These represent the first reflective unit of the IRS. Axis coordinates and drones of Axis coordinates They represent drones of Axial coordinates and the first reflection unit of the IRS Axis coordinates, symbol and These represent the IRS array elements along... shaft and The spacing between axes; Let the coordinates of the first reflecting unit of the IRS be represented by the following: ;

[0023] Based on channel estimation, from jammers to drones The channel representation of H-UAV is as follows:

[0024]

[0025]

[0026] in , , and They represent time slots respectively Chinese jammers and drones The distance between them and between the jammer and the H-UAV; and These represent jammers and drones, respectively. Path loss index between and between the jammer and the H-UAV, and Indicates the corresponding Rice factor; and It is a small-scale fading that follows a complex Gaussian distribution with a mean of 0 and a variance of 1; and The phase term of a line-of-sight link can be represented as: and ;

[0027] From drones To drones The effective channel gain is expressed as:

[0028]

[0029] Similarly, from jammers to drones Gain The calculation is as follows:

[0030]

[0031] drones From drones The received signal is represented as :

[0032]

[0033] Among them, binary variables , They represent time slots respectively Whether from drone and jammers towards drones Sending signals when the drone In the time slot To drones When sending signals ,otherwise , Similarly. and They represent time slots respectively At that time, from the drone and jammer transmission to drone ; signal power; and These represent the noise amplified by the active IRS and the additive white Gaussian noise corresponding to the transmit-receive pair, respectively. Let represent the variance of the additive white Gaussian noise, respectively.

[0034] Furthermore, the energy consumption modeling in step 1) specifically includes:

[0035] drones The total communication energy consumption is expressed as:

[0036]

[0037] , Representing time slot length and UAV respectively In the time slot To drones The power of the transmitted signal, This indicates the number of communication drones. Hybrid IRSs also consume energy when used as relay nodes; the average power consumption of passive and active IRSs can be expressed as follows: ,

[0038]

[0039] in These represent the power consumption of the switching and control circuits of each reflector, the DC bias power consumption of each IRS reflector, and the amplification efficiency, respectively. Represents the reflection matrix of the IRS. The variance of the active IRS amplified noise, which follows a complex Gaussian distribution, is expressed in the time slot. The total energy consumption of the system is:

[0040] .

[0041] Furthermore, the modeling of the interference source-related channel error model in step 1) specifically includes:

[0042] Assume the channel uses a length of The guidance signal is estimated; jammers and drones Jammers and IRS, and IRS and drones The channel estimation error can be expressed as:

[0043]

[0044]

[0045]

[0046] That is, the channel estimation error follows an independent and identically distributed circularly symmetric complex Gaussian random vector distribution; here, , and They represent time slots respectively At that time, between the jammer and the IRS, and between the jammer and the drone. Between, and between IRS and drones The actual channel between them; the variance of the circularly symmetric complex Gaussian random vector distribution is expressed as follows: , and These correspond to jammer and drone, respectively. Jammers - IRS and IRS-Drone The channel.

[0047] Furthermore, step 1) NOMA transmission model modeling specifically includes;

[0048] Introducing binary variables To represent the decoding order among a group of drones, in time slots In the middle, if drones Simultaneously with drones and Communication, and drones To drones The actual channel gain is better than that of UAVs To drones The channel gain, then ;otherwise, .for The following constraints must be met:

[0049]

[0050]

[0051] To ensure fairness, users with lower effective channel power gain will receive higher transmit power. This is enforced through the following power allocation constraints:

[0052]

[0053] Based on the decoding principle of NOMA, in the time slot In China, drones To drones achievable transmission rate of the transmitted signal It can be represented as:

[0054] .

[0055] Furthermore, in step 2): a multi-objective optimization problem is established based on the model in step 1). To solve this highly coupled non-convex optimization problem, a multi-objective optimization problem is established using... - Constraint methods transform it into a single-objective optimization problem, specifically including:

[0056] To achieve efficient communication between drones, the drone's transmit power allocation was jointly optimized. Drone decoding sequence IRS reflectance The ratio of active to passive reflection units and the location of H-UAV The aim is to maximize the average jamming-resistant achievable transmission rate of the considered system and minimize its communication energy consumption when the jammer's CSI is incomplete. The optimization problem is described as follows:

[0057]

[0058]

[0059] in Indicates drone Minimum acceptable rate required Indicates the maximum transmit power of each drone; symbol The constraint C1 represents the maximum reflection amplitude of the active IRS reflector; constraint C2 and C3 represent the minimum acceptable rate to ensure UAV service quality; constraint C4 represents the UAV transmit power constraint; constraint C5 represents the IRS reflection phase shift constraint; constraint C6 represents the reflection amplitude constraint of the passive IRS reflector; constraint C7 represents the proportion constraint of the active IRS reflector; constraint C8 represents the flight constraint of the UAV swarm; constraint C9 represents the flight position constraint between the H-UAV and other UAVs; constraint C10 represents the amplification power constraint of the active IRS; and C11-C13 represent the decoding order and transmit power constraints.

[0060] pass - The constraint method transforms problems P1 and P2 into single-objective problems:

[0061] .

[0062] Furthermore, step 3) specifically includes:

[0063] Under fixed IRS configuration and NOMA strategy, auxiliary variables are introduced to decouple constraints, and a continuous convex approximation method is used to transform the original non-convex trajectory optimization problem into a series of easily solvable convex optimization problems, thereby iteratively updating the flight trajectory of the H-UAV. Under fixed H-UAV trajectory and NOMA strategy, semidefinite programming relaxation technique is used to transform the complex IRS beamforming problem into a semidefinite programming problem. For the non-convex rank-one constraint introduced by semidefinite programming relaxation, a penalty function-based method is designed to approximate the solution. Finally, the allocation ratio of active and passive units is determined based on the solved reflection unit amplitude. Under fixed H-UAV trajectory and IRS configuration, the decoding order of NOMA users is first dynamically determined based on the current equivalent channel gain, and then an SCA-based algorithm is used to optimize the transmit power allocation of each user's UAV.

[0064] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a robust anti-jamming method for UAV swarm communication assisted by a hybrid intelligent reflector as described in any one of the claims.

[0065] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector as described in any one of the claims.

[0066] The advantages and beneficial effects of this invention are as follows:

[0067] This invention achieves significant benefits by deeply integrating and synergistically optimizing the mobility of H-UAVs, the powerful signal processing capabilities of hybrid IRS, and the high spectral efficiency of NOMA. First, this method demonstrates superior anti-interference performance and system transmission rate; simulation results verify that its average transmission rate is significantly better than traditional schemes under various complex conditions. Second, this method achieves intelligent energy efficiency control, intelligently balancing rate and energy consumption according to environmental changes, thereby maintaining high system energy efficiency. Furthermore, this invention considers the real-world challenge of imperfect interference (CSI) from the outset, making it more robust and practical in real electromagnetic environments, ensuring the stability and reliability of the communication system. This integrated and synergistic design fully demonstrates the superiority of system synergy, and its overall performance far surpasses schemes that optimize individual components in isolation.

[0068] The core innovation of this invention is mainly embodied in the advanced collaborative optimization method constructed in claims 1 to 7. Its ingenuity lies in the fact that it does not simply combine existing technologies, but innovatively places multiple highly coupled variables, including the UAV's flight trajectory, beamforming of the hybrid intelligent reflector and unique active / passive unit allocation, and the power and decoding order of non-orthogonal multiple access, within a unified framework for joint optimization. The solution method first directly addresses the real challenge of imperfect interference source channel information. Unlike conventional schemes that rely on ideal assumptions, this invention fundamentally guarantees the robustness of the algorithm by modeling channel errors. Secondly, the highly non-convex and nonlinear problems arising from simultaneously optimizing highly differentiated variables are difficult to solve using conventional techniques.

[0069] To overcome this challenge, this invention innovatively designs an alternating optimization algorithm based on block coordinate descent. This algorithm decomposes the complex original problem into three manageable subproblems: trajectory, hybrid intelligent reflector configuration, and non-orthogonal multiple access resource allocation. For each subproblem, a series of solution strategies are tailored, such as continuous convex approximation and semidefinite programming relaxation combined with penalty function methods. This series of innovations not only achieves superior anti-interference performance and system transmission rate but also possesses high robustness and practical value in real-world complex electromagnetic environments, and achieves intelligent energy efficiency control. Its overall performance far surpasses traditional schemes that optimize individual components in isolation. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of a preferred embodiment of the anti-interference drone swarm system model provided by the present invention, which is a hybrid IRS-assisted system.

[0071] Figure 2 This is a convergence performance graph of the algorithm proposed in this invention, showing how the average transmission rate of the system changes with the number of algorithm iterations under different flight altitudes and interference power conditions.

[0072] Figure 3 This is a comparison chart of the relationship between the system's average transmission rate and interference power, used to demonstrate the performance of different algorithms when interference power changes.

[0073] Figure 4 This is a comparison chart of the relationship between the system's average transmission rate and the drone's maximum transmission power, used to demonstrate the performance of different algorithms when the drone's transmission power changes.

[0074] Figure 5 This is a comparison chart of the relationship between the system's average transmission rate and the number of IRS units, used to demonstrate the performance of different algorithms when the number of IRS units changes.

[0075] Figure 6This is a comparison chart of the relationship between the system's average transmission rate and the drone's flight altitude, used to demonstrate the performance of different algorithms when the flight altitude changes.

[0076] Figure 7 This is a comparison chart of the relationship between the system's average transmission rate and the path loss index, used to demonstrate the performance of different algorithms when the channel path loss index changes.

[0077] Figure 8 This is a comparison chart of the relationship between the system's average power consumption and the interference power, used to analyze the energy consumption of different algorithms when the interference power changes.

[0078] Figure 9 This is a comparison chart of the relationship between the system's average power consumption and the drone's maximum transmit power, used to analyze the energy consumption of different algorithms when the drone's transmit power changes.

[0079] Figure 10 This is a comparison chart of the relationship between the system's average power consumption and the number of IRS units, used to analyze the energy consumption of different algorithms when the number of IRS units changes.

[0080] Figure 11 This is a comparison chart of the relationship between the system's average power consumption and the drone's flight altitude, used to analyze the energy consumption of different algorithms when the flight altitude changes.

[0081] Figure 12 This is a comparison chart of the relationship between the system's average power consumption and the path loss index, used to analyze the energy consumption of different algorithms when the channel path loss index changes. Detailed Implementation

[0082] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0083] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0084] An example of this invention provides a method comprising:

[0085] Step 1: Construct a model of a UAV swarm anti-jamming communication system assisted by a hybrid intelligent reflector, model the channel, energy consumption and non-orthogonal multiple access protocol, and on this basis, construct a multi-objective joint optimization problem aimed at maximizing the average transmission rate of the system and minimizing the total communication energy consumption.

[0086] Step 2: Based on the model in Step 1), establish a multi-objective optimization problem. To solve this highly coupled non-convex optimization problem, utilize... - Constraint methods transform it into a single-objective optimization problem.

[0087] Step 3: Decompose the transformed problem into three coupled sub-problems: H-UAV trajectory optimization, hybrid IRS configuration optimization, and NOMA resource allocation optimization. For these three sub-problems, an alternating optimization iterative algorithm based on block coordinate descent is designed. This algorithm alternately solves the sub-problems using methods such as continuous convex approximation, semidefinite programming relaxation, and dynamic sorting until the algorithm converges to obtain an approximate optimal solution.

[0088] A. H-UAV trajectory optimization

[0089] The decomposition problem P3 leads to the H-UAV trajectory optimization problem P4:

[0090]

[0091] To handle problem P4, we define the process from the drone launcher. The upper and lower bounds of the distance to H-UAV are and From H-UAV to drone receiver The upper and lower bounds of the distance are and ; and the lower bound of the distance from the H-UAV to the jammer is They satisfy the following inequalities:

[0092]

[0093] Therefore, we can obtain the source of interference and the drone. Upper bound of effective channel gain, UAV With drones The lower bound of the effective channel gain and the upper bound of the reflected signal gain are respectively:

[0094]

[0095]

[0096]

[0097]

[0098] here, , , , , , , , and It is an intermediate variable that is introduced.

[0099] To facilitate the subsequent representation of the lower bound of the receiving rate, we then introduce an auxiliary variable. and This makes the following equation true:

[0100]

[0101]

[0102] Therefore, the lower bound of the expected receiving rate The upper limit of the amplification power of active IRS It can be represented as:

[0103]

[0104]

[0105] Therefore, subproblem P4 can be transformed into:

[0106]

[0107] To address this non-convex problem, a series of auxiliary variables are first introduced to decouple the H-UAV position variables. The relevant expressions are then used. Next, employing a continuous convex approximation method, in the nth iteration, a first-order Taylor expansion is performed on these non-convex constraints using the position points obtained from the previous iteration, approximating them as convex constraints. For example, constraints... It can be approximated as By applying similar treatment to all non-convex constraints, the original trajectory optimization subproblem is transformed into a standard convex optimization problem in each iteration, which can be solved efficiently using standard convex optimization solvers such as CVX.

[0108] B. IRS Configuration

[0109] Optimize beamforming given transmit power and NOMA decoding order. ratio of active to passive reflection units For ease of optimization, we treat the beamforming of the hybrid IRS as a special type of active IRS beamforming optimization, where the amplitude adjustment range of all reflecting elements is... . This is the maximum reflection amplitude, a fixed parameter determined by the limits of the active IRS element. Therefore, problem P3 can be transformed into:

[0110]

[0111] This problem is also non-convex, and we use semidefinite programming relaxation techniques to solve it. First, define... Let IRS reflectance coefficient vector be used, and construct a higher-dimensional matrix. Through this transformation, the expressions for rate and power can be modified to reflect the following: The quadratic term is transformed into a term about The linear trace function. The semidefinite programming relaxation transformation introduces a non-convex rank-one constraint. To handle this constraint, we relax it and add a penalty function term to the objective function. ,in and Let be the nuclear norm and the spectral norm, respectively. The penalty function term is zero only when the rank is one. After transformation and semidefinite programming relaxation, this subproblem is also transformed into a convex problem that can be solved in each iteration. The optimal solution is then obtained. Subsequently, the approximately optimal reflection vector can be recovered through methods such as eigenvalue decomposition. Finally, through statistics The amplitudes of each element are in the range The number of active units can be used to determine the allocation ratio of active units.

[0112] C. NOMA Resource Allocation

[0113] Given the H-UAV trajectories and IRS configurations, the channel gains among all UAVs can now be derived. Therefore, we devise a dynamic NOMA user decoding strategy: first, we calculate and sort the channel gains derived from the H-UAV trajectories and IRS matrices, and then determine the decoding order based on NOMA constraints. Thus, the transmit power allocation subproblem can be written as:

[0114]

[0115] This problem remains nonconvex due to its logarithmic objective function. We employ a method combining difference convex programming and semidefinite programming relaxation to solve it. Specifically, the logarithmic term in the objective function... It is split into the difference of two concave functions. Then, for the second concave function part, at its current iteration point... A first-order Taylor expansion is performed to obtain a linear and easily tractable lower bound. Using this method, the original non-convex power allocation problem is transformed into a standard convex optimization problem in each iteration, which can be solved efficiently. By iterating through the above three subproblems until the variables converge, the H-UAV trajectory, IRS configuration, and NOMA resource allocation strategy that meet the system performance requirements can be obtained.

[0116] The algorithm proposed in this invention has significant effects, which can be explained in detail with reference to the accompanying drawings. Figure 2 Convergence analysis shows that the algorithm of this invention can converge quickly after about 8 iterations under different flight altitudes and interference power, verifying the effectiveness and stability of the algorithm.

[0117] The superiority of this invention has been fully demonstrated in performance comparisons with various benchmark algorithms. Figures 3 to 7 The comparison results of the system's average transmission rate are presented. The results show that, regardless of the interference power enhancement ( Figure 3 ), and the maximum transmission power of drones has been increased ( Figure 4 The number of IRS units has increased. Figure 5 Increased flight altitude of drones Figure 6 ), or path loss worsens ( Figure 7 Under various conditions, including those mentioned above, the BCDB algorithm proposed in this invention consistently outperforms other comparative schemes in terms of average transmission rate. This demonstrates that through multi-dimensional collaborative optimization, this invention can most effectively suppress interference and enhance legitimate signals, thereby achieving the highest communication rate.

[0118] at the same time, Figures 8 to 12 The comparison results of the system's average power consumption are presented. These results demonstrate that the present invention can effectively control communication power consumption while achieving high transmission rates. For example, when facing enhanced interference power ( Figure 8 The BCDB algorithm of this invention intelligently reduces the transmission power to maintain high energy efficiency. Figure 9 The results show that as the maximum available transmission power of the drone increases, the algorithm of this invention can make reasonable use of this power to improve the speed, and its power consumption increases steadily, but it is still better than most solutions. Figure 10 This indicates that increasing the number of IRS units leads to increased power consumption, but the power consumption increase of the algorithm in this invention is the most gradual, demonstrating efficient utilization of hybrid IRS resources. Under various parameter variations, the power consumption performance of the algorithm in this invention is also superior to or close to the comparative scheme, especially as flight altitude increases. Figure 11 The power consumption control is the most outstanding. Overall, the simulation results strongly demonstrate the dual effectiveness of this invention in improving anti-interference communication speed and ensuring low communication power consumption.

[0119] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

[0120] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] The above embodiments should be understood as illustrative only and not as limiting the scope of protection of the present invention. After reading the description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector, characterized in that, Includes the following steps: 1) Construct a model of a UAV swarm anti-jamming communication system assisted by a hybrid intelligent reflector, model the channel, energy consumption and non-orthogonal multiple access protocol, and on this basis, construct a multi-objective joint optimization problem that maximizes the average transmission rate of the system and minimizes the total communication energy consumption; 2) Based on the model in step 1), establish a multi-objective optimization problem. To solve this highly coupled non-convex optimization problem, utilize... - Constraint methods transform it into a single-objective optimization problem; 3) The transformed problem is decomposed into three coupled sub-problems: H-UAV trajectory optimization, hybrid IRS configuration optimization, and NOMA resource allocation optimization. For the three sub-problems, an alternating optimization iterative algorithm based on block coordinate descent is designed. The algorithm is solved alternately by continuous convex approximation, semidefinite programming relaxation, and dynamic sorting methods until the algorithm converges and obtains an approximate optimal solution.

2. The robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector, as described in claim 1, is characterized in that... The channel modeling in step 1) specifically includes: Using a hybrid IRS-assisted unmanned aerial vehicle (UAV) H-UAV as an airborne relay, considering K UAVs and one H-UAV, the UAVs in the swarm use... This indicates that each drone is equipped with only one antenna; the total time is divided into... Each time slot has a length of [number] timeslots. equal, In each time slot, only one drone acts as the transmitter, while the other drones act as receivers to receive signals from the transmitter. The positions of all drones are fixed within each time slot but change between different time slots and can be modeled in a three-dimensional Cartesian coordinate system. At that time, drones The coordinate positions of H-UAV are respectively represented as and , , Assume the intelligent jammer is located at a fixed position on the ground, and its coordinate position is defined as follows: ; drones In the time slot The speed is defined as , , ; Considering the IRS carried by the drone It consists of a uniform planar array, that is ,in , and These represent the IRS reflective elements along... shaft and The number of axes; IRS reflector units are divided into active and passive types; passive IRS reflector units only support phase shift adjustment of the incident signal; active IRS reflector units can not only adjust the phase shift of the incident signal, but also amplify the signal through a reflective amplifier; the number of active reflector units in a hybrid IRS is defined as... It can be used It means that, among them The rest are passive reflective units; in the time slot The reflection vector of the IRS can be expressed as: and , , ;in It is the imaginary unit. and These represent the phase shift and reflection amplitude of the IRS reflector, respectively; when hour, ;when hour, ; A. Channel Model Communication channels between drones can be divided into: direct channels and reflected channels; time slots From drones To drones The direct channel is modeled as a Ricean fading channel: in , It is a drone and drones The distance between them; Indicates drone and drones Channel coefficients between Indicates drone Location coordinates Indicates drone Position coordinates; This indicates the path loss at 1 meter. Indicates drone and drones Path loss index between The corresponding Rice factor is represented as; the small-scale fading component is represented as... It follows a complex Gaussian distribution with a mean of 0 and a variance of 1, while It is the phase term of the line-of-sight link; The reflection link includes the link from the drone to the IRS and the link from the IRS to the drone. Therefore, a line-of-sight link channel model is adopted for the link from the IRS to the UAV; specifically, from the UAV... The channel to the IRS can be represented as: in , Indicates drone Distance between and IRS; variable The receiver array response representing the IRS can be obtained in the following ways: in and These are two time slots The expressions related to the azimuth and elevation angles of arrival are shown in the figure. These represent the first reflective unit of the IRS. Axis coordinates and drones of Axis coordinates They represent drones of Axial coordinates and the first reflection unit of the IRS Axis coordinates, symbol and These represent the IRS array elements along... shaft and The spacing between axes; Let the coordinates of the first reflecting unit of the IRS be represented by the following: ; Based on channel estimation, from jammers to drones The channel representation of H-UAV is as follows: in , , and They represent time slots respectively Chinese jammers and drones The distance between them and between the jammer and the H-UAV; and These represent jammers and drones, respectively. Path loss index between and between the jammer and the H-UAV, and Indicates the corresponding Rice factor; and It is a small-scale fading that follows a complex Gaussian distribution with a mean of 0 and a variance of 1; and The phase term of a line-of-sight link can be represented as: and ; From drones To drones The effective channel gain is expressed as: Similarly, from jammers to drones Gain The calculation is as follows: drones From drones The received signal is represented as : Among them, binary variables , They represent time slots respectively Whether from drone and jammers towards drones Sending signals when the drone In the time slot To drones When sending signals ,otherwise , Similarly; and They represent time slots respectively At that time, from the drone and jammer transmission to drone ; signal power; and These represent the noise amplified by the active IRS and the additive white Gaussian noise corresponding to the transmit-receive pair, respectively. Let represent the variance of the additive white Gaussian noise, respectively.

3. The robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector, as described in claim 2, is characterized in that... The energy consumption modeling in step 1) specifically includes: drones The total communication energy consumption is expressed as: , Representing time slot length and UAV respectively In the time slot To drones The power of the transmitted signal, This indicates the number of communication drones; hybrid IRS also consumes energy when used as a relay node, and the average power consumption of passive and active IRS can be expressed as follows: , in These represent the power consumption of the switching and control circuits of each reflector, the DC bias power consumption of each IRS reflector, and the amplification efficiency, respectively. Represents the reflection matrix of the IRS. The variance of the active IRS amplified noise, which follows a complex Gaussian distribution, is expressed in the time slot. The total energy consumption of the system is: 。 4. The robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector, as described in claim 3, is characterized in that... The modeling of the interference source-related channel error model in step 1) specifically includes: Assume the channel uses a length of The guidance signal is estimated; jammers and drones Jammers and IRS, and IRS and drones The channel estimation error can be expressed as: That is, the channel estimation error follows an independent and identically distributed circularly symmetric complex Gaussian random vector distribution; here, , and They represent time slots respectively At that time, between the jammer and the IRS, and between the jammer and the drone. Between, and between IRS and drones The actual channel between them; the variance of the circularly symmetric complex Gaussian random vector distribution is expressed as follows: , and These correspond to jammer and drone, respectively. Jammers - IRS and IRS-Drone The channel.

5. A robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector, as described in claim 4, is characterized in that... Step 1) NOMA transmission model modeling specifically includes: Introducing binary variables To represent the decoding order among a group of drones, in time slots In the middle, if drones Simultaneously with drones and Communication, and drones To drones The actual channel gain is better than that of UAVs To drones The channel gain, then ; otherwise, ;for The following constraints must be met: To ensure fairness, users with lower effective channel power gain will receive higher transmit power; this is enforced through the following power allocation constraints: Based on the decoding principle of NOMA, in the time slot In China, drones To drones achievable transmission rate of the transmitted signal It can be represented as: 。 6. A robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector, as described in claim 5, is characterized in that... Step 2): Based on the model in Step 1), a multi-objective optimization problem is established. To solve this highly coupled non-convex optimization problem, the following steps are taken: - Constraint methods transform it into a single-objective optimization problem, specifically including: To achieve efficient communication between drones, the drone's transmit power allocation was jointly optimized. Drone decoding sequence IRS reflectance The ratio of active to passive reflection units and the location of H-UAV The aim is to maximize the average jamming-resistant achievable transmission rate of the considered system and minimize its communication energy consumption when the jammer's CSI is incomplete. The optimization problem is described as follows: in Indicates drone Minimum acceptable rate required Indicates the maximum transmit power of each drone; symbol The constraint C1 represents the maximum reflection amplitude of the active IRS reflector; constraint C2 and C3 represent the minimum acceptable rate to ensure UAV service quality; constraint C4 represents the UAV transmit power constraint; constraint C5 represents the IRS reflection phase shift constraint; constraint C6 represents the reflection amplitude constraint of the passive IRS reflector; constraint C7 represents the proportion constraint of the active IRS reflector; constraint C8 represents the flight constraint of the UAV swarm; constraint C9 represents the flight position constraint between the H-UAV and other UAVs; constraint C10 represents the amplification power constraint of the active IRS; and C11-C13 represent the decoding order and transmit power constraints. pass - The constraint method transforms problems P1 and P2 into single-objective problems: 。 7. A robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector, as described in claim 6, is characterized in that... Step 3) specifically includes: Under fixed IRS configuration and NOMA strategy, auxiliary variables are introduced to decouple constraints, and a continuous convex approximation method is used to transform the original non-convex trajectory optimization problem into a series of easily solvable convex optimization problems, thereby iteratively updating the flight trajectory of the H-UAV. Under fixed H-UAV trajectory and NOMA strategy, semidefinite programming relaxation technique is used to transform the complex IRS beamforming problem into a semidefinite programming problem. For the non-convex rank-one constraint introduced by semidefinite programming relaxation, a penalty function-based method is designed to approximate the solution. Finally, the allocation ratio of active and passive units is determined based on the solved reflection unit amplitude. Under fixed H-UAV trajectory and IRS configuration, the decoding order of NOMA users is first dynamically determined based on the current equivalent channel gain, and then an SCA-based algorithm is used to optimize the transmit power allocation of each user's UAV.

8. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the robust anti-interference method for UAV swarm communication assisted by a hybrid intelligent reflector as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the robust anti-interference method for UAV swarm communication assisted by the hybrid intelligent reflector as described in any one of claims 1 to 7.

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