Robust design method of air reconfigurable intelligent surface communication system, electronic equipment and storage medium

Abstract

The invention discloses a robust design method of an air reconfigurable intelligent surface communication system, electronic equipment and a storage medium. Comprising the steps of system modeling, auxiliary variable calculation, reference model initialization, optimization loop judgment, power optimization, passive beam forming optimization, unmanned aerial vehicle position optimization, auxiliary variable updating, optimization loop judgment returning and optimization result outputting. On the premise that a platform hardware damage model and channel dynamic change are jointly considered, reliable guarantee of communication link performance in a non-ideal hardware condition and a complex and changeable environment is achieved through end-to-end compression estimation and a robust optimization framework; the problem that an existing Aerial RIS auxiliary communication system is difficult to consider hardware non-ideality and multi-target performance constraint in a high dynamic scene can be solved, reliable guarantee of communication link performance in non-ideal hardware conditions and complex and changeable environments is achieved, and the method is suitable for complex propagation environments facing ports and channels.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of communication engineering, and particularly relates to a robust design method of an aerial reconfigurable intelligent surface communication system, an electronic device and a storage medium. BACKGROUND

[0002] With the wide deployment of the sixth generation (6G) wireless communication, the reconfigurable intelligent surface (RIS) has become an important means to improve the link quality and expand the coverage.

[0003] The prior art mainly deploys the RIS at the ground fixed station, which can improve the channel condition to a certain extent, but in the face of complex propagation conditions such as ports and waterways, it is difficult to balance flexibility and performance stability due to the fixed position and blind area coverage of the ground station.

[0004] To solve the above limitations, in recent years, researchers have proposed an "aerial reconfigurable intelligent surface" (Aerial RIS) scheme by loading the RIS on a platform such as an unmanned aerial vehicle (UAV) or a high-altitude balloon, to realize full-coverage channel enhancement for key services by taking advantage of its mobile deployment. However, existing work focuses on flight trajectory planning and phase optimization, and there is still a lack of systematic robust design for hardware non-idealities, especially the phase noise of the platform-mounted RIS, the non-linear distortion of the radio frequency amplifier, and the I / Q imbalance. SUMMARY

[0005] The purpose of the present application is to provide a robust design method of an aerial reconfigurable intelligent surface communication system, an electronic device and a storage medium, to overcome the problem that the existing Aerial RIS assisted communication system is difficult to balance hardware non-idealities and multi-target performance constraints in high dynamic scenarios, and to realize reliable protection of communication link performance in non-ideal hardware conditions and complex and variable environments, which is suitable for complex propagation environments such as ports and waterways.

[0006] In a first aspect, the present application provides a robust design method of an aerial reconfigurable intelligent surface communication system, comprising the following steps:

[0007] S1, system modeling: establishing a system model including a UAV, a reconfigurable intelligent surface, a terminal device and a control center, and modeling and analyzing hardware non-idealities in the system model;

[0008] S2, auxiliary variable calculation: based on the current system parameters, calculating auxiliary variables related to the optimization target, the auxiliary variables being used to convert the optimization problem containing coupling variables into at least two sub-problems which are easy to solve step by step;

[0009] S3, initialize the reference model: determine the initial parameter configuration, including power allocation, reconfigurable intelligent surface phase configuration and UAV position, to provide the starting solution for the optimization process;

[0010] S4, optimization cycle determination: determine whether the current system configuration meets the predetermined performance convergence condition, if it meets, output the optimization result, if it does not meet, enter the next step;

[0011] S5, power optimization: based on the current auxiliary variable and the reference model, the transmit power of the terminal device is optimized;

[0012] S6, passive beamforming optimization: for the phase matrix of the reconfigurable intelligent surface, passive beamforming optimization is performed to improve channel gain;

[0013] S7, UAV position optimization: according to the optimization target, the spatial position of the UAV is adjusted to achieve better system performance;

[0014] S8, auxiliary variable update: according to the optimized parameter result, the auxiliary variable is updated to provide new input for the next optimization cycle;

[0015] S9, return to step S4, determine whether the updated system meets the performance convergence condition, if it does not converge, continue to execute the optimization process of steps S5-S8;

[0016] S10: output optimization result: when the system performance reaches the predetermined standard, output the final optimization configuration, including transmit power, reconfigurable intelligent surface phase and UAV position parameters.

[0017] Preferably, in step S1, when modeling the system, it includes:

[0018] S11, install a programmable phase reconfigurable intelligent surface reflection unit on the UAV platform, and configure a hardware damage monitoring module between the reconfigurable intelligent surface driver and the UAV flight control system;

[0019] S12, deploy a hardware quality factor acquisition unit in the control center and the Internet of Things terminal, which is used to acquire hardware distortion indicators such as phase noise, I / Q imbalance and radio frequency amplifier nonlinearity in real time.

[0020] Preferably, in step S2, the hardware non-ideal factors are actual hardware defects and limitations, including but not limited to hardware loss of transceiver, nonlinear characteristics of power amplifier, phase noise, quantization error and limited resolution effect;

[0021] The auxiliary variables include auxiliary variables for decoupling SINR expression, distance auxiliary variables in UAV position optimization, and Lagrange multipliers for dual transformation;

[0022] In the auxiliary variable calculation, the base station and the UAV-reconfigurable intelligent surface cooperatively transmit a pilot sequence during system operation, the Internet of Things terminal transmits a weighted data frame upward, and the control center feeds back received channel information.

[0023] Preferably, in step S3, when initializing the reference model, the central processing server uses compressed sensing and least squares method to jointly estimate the multiple-input multiple-output channel of the cascaded Internet of Things-reconfigurable intelligent surface-control center and the hardware damage model parameters in combination with hardware monitoring data.

[0024] Preferably, in step S4, when optimizing the loop decision, the server constructs an optimization problem of link performance and hardware non-ideality with the goal of maximizing the weighted sum and spectral efficiency.

[0025] Preferably, in steps S5-S7, when optimizing power, passive beamforming, and UAV position, the following steps are included:

[0026] S51, the optimization problem is divided into several sub-problems by a fractional programming method, and the Internet of Things transmission power in S5, the reconfigurable intelligent surface phase configuration in S6, and the UAV position / attitude in S7 are iteratively solved under an alternating optimization framework; the reconfigurable intelligent surface phase optimization adopts a manifold optimization algorithm, and the UAV position optimization adopts a continuous convex approximation method.

[0027] Preferably, in step S8, when updating the auxiliary variables, after optimization is completed, the server synchronously issues update instructions for the reconfigurable intelligent surface phase vector, the UAV flight attitude, and the Internet of Things transmission power through a wireless control link, realizing joint optimization control of reconfigurable intelligent surface reflection enhancement, UAV platform position adjustment, and Internet of Things terminal power distribution.

[0028] Preferably, in step S9, the following steps are included:

[0029] S91, in the system operation phase, hardware monitoring, channel estimation, and optimization control form a closed-loop feedback;

[0030] S92, when hardware damage mutation or performance degradation is detected, an online re-estimation and re-optimization process is automatically triggered, and algorithm hyperparameters and damage model weights are continuously updated through a deep reinforcement learning engine to adapt to dynamic environments.

[0031] In a second aspect, the present application provides an electronic device including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform the steps of the robust design method of the air-reconfigurable intelligent surface communication system according to the first aspect of the present application.

[0032] In a third aspect, the present application provides a storage medium having stored thereon a computer program, which, when executed by a processor, implements the robust design method of the aerial reconfigurable intelligent surface communication system according to the first aspect of the present application.

[0033] The robust design method of the aerial reconfigurable intelligent surface communication system, the electronic device and the storage medium provided by the present application have the following beneficial effects:

[0034] The robust design method of the aerial reconfigurable intelligent surface communication system provided by the present application relates to the design of space-air-ground integrated communication systems in 6G wireless communication networks and adaptive optimization methods based on aerial reconfigurable intelligent surfaces. It focuses on improving system link performance and robustness in the presence of non-ideal hardware loss and other factors. As a hardware fault-tolerant aerial reconfigurable intelligent surface adaptive optimization method, it considers the platform hardware damage model and channel dynamic changes, and realizes reliable protection of communication link performance in non-ideal hardware conditions and complex and variable environments through end-to-end compression estimation and robust optimization framework. This method effectively balances hardware non-idealness, multi-objective performance constraints and algorithm realizability, providing key technical support for the next generation of space-air-ground integrated communication systems. It can overcome the problem that existing Aerial RIS assisted communication systems cannot balance hardware non-idealness and multi-objective performance constraints in high dynamic scenarios, and realize reliable protection of communication link performance in non-ideal hardware conditions and complex and variable environments, and is suitable for complex propagation environments facing ports and waterways.

[0035] The robust design method of the aerial reconfigurable intelligent surface communication system provided by the present application has the following beneficial advantages:

[0036] 1. High reliability guarantee: By introducing a hardware damage monitoring module and model joint estimation mechanism, it can detect and compensate phase noise, radio frequency nonlinearity, I / Q (in-phase / quadrature phase) imbalance and other hardware non-ideal factors in real time, significantly improving the link stability and reliability of the system in complex environments and hardware damage conditions.

[0037] 2. End-to-end delay optimization: By constructing a multi-objective optimization framework, combining fractional programming (FP) and alternating optimization (AO) algorithms, the end-to-end transmission delay is effectively controlled while ensuring link performance, improving the response speed and quality of service of the system.

[0038] 3. Flexible deployment and efficient computation: RIS is mounted on the UAV platform, with the ability of rapid mobile deployment and dynamic coverage. At the same time, low-complexity algorithms such as manifold optimization and successive convex approximation (SCA) are used to reduce the computational burden and deployment cost, enhancing the practicality and scalability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 This is a flowchart of a robust design method for an airborne reconfigurable smart surface communication system provided in an embodiment of the present invention.

[0040] Figure 2 This is a flowchart of a robust design method for an airborne reconfigurable smart surface communication system provided in another embodiment of the present invention.

[0041] Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present invention.

[0042] Figure 3 In this context, 100 represents electronic devices, 200 represents processors, and 300 represents storage media. Detailed Implementation

[0043] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Example 1

[0045] like Figure 1 As shown, this embodiment provides a robust design method for an airborne reconfigurable smart surface communication system, including the following steps:

[0046] S1. System Modeling: Establish a system model including UAV, reconfigurable smart surface, terminal equipment and control center, and perform modeling and analysis of hardware non-ideal factors in the system model;

[0047] S2. Calculation of auxiliary variables: Based on the current system parameters, calculate the auxiliary variables related to the optimization objective. The auxiliary variables are used to transform the optimization problem with coupled variables into at least two sub-problems that are easy to solve step by step.

[0048] S3. Initialize the reference model: Determine the initial parameter configuration, including power allocation, reconfigurable smart surface phase configuration, and UAV position, to provide an initial solution for the optimization process;

[0049] S4. Optimization Loop Determination: Determine whether the current system configuration meets the predetermined performance convergence conditions. If it does, output the optimization result; otherwise, proceed to the next step.

[0050] S5. Power Optimization: Optimize the transmit power of the terminal equipment based on the current auxiliary variables and reference model;

[0051] S6. Passive beamforming optimization: Passive beamforming optimization is performed on the phase matrix of the reconstructed smart surface to improve channel gain;

[0052] S7, UAV position optimization: adjust the spatial position of the UAV according to the optimization target, to achieve better system performance;

[0053] S8, auxiliary variable update: update the auxiliary variable according to the optimized parameter result, and provide new input for the next optimization cycle;

[0054] S9, return to step S4, judge whether the updated system meets the performance convergence condition, if not, continue to execute the optimization process of steps S5-S8;

[0055] S10: output optimization result: when the system performance reaches the predetermined standard, output the final optimization configuration, including the transmission power, reconfigurable intelligent surface phase and UAV position parameters.

[0056] As shown in Figure 2 , specifically, in step S1, when modeling the system, including:

[0057] S11, install programmable phase RIS reflection unit on the UAV platform, and configure hardware damage monitoring module between RIS driver and UAV flight control system;

[0058] S12, at the same time, deploy hardware quality factor acquisition unit at the control center (CC) and Internet of Things (IoT) terminal, for real-time acquisition of hardware distortion indicators such as phase noise, I / Q imbalance and radio frequency amplifier nonlinearity.

[0059] Specifically, in step S2, the hardware non-ideal factors are actual hardware defects and limitations, including but not limited to hardware loss of transceiver, nonlinear characteristics of power amplifier, phase noise, quantization error and limited resolution effect;

[0060] The auxiliary variables include auxiliary variables for decoupling SINR expression, distance auxiliary variables in UAV position optimization, and Lagrange multipliers for dual transformation;

[0061] When calculating the auxiliary variables, the base station and the UAV-reconfigurable intelligent surface cooperatively send pilot sequences during system operation, the Internet of Things terminal transmits weighted data frames uplink, and the control center feeds back the received channel information; that is, the base station and the UAV-RIS cooperatively send pilot sequences, the IoT terminal transmits weighted data frames uplink, and the CC feeds back the received channel information; the optimization problem containing coupled variables is converted into at least two sub-problems which are easy to solve step by step;

[0062] Specifically, in step S3, when initializing the reference model, the central processing server uses compressed sensing and least squares method to jointly estimate the cascaded IoT-RIS-CC MIMO (Multiple Input Multiple Output) channel and hardware damage model parameters combined with hardware monitoring data.

[0063] Specifically, in step S4, when the optimization cycle is determined, the server constructs an optimization problem of link performance and hardware imperfection with the goal of maximizing the weighted sum spectral efficiency (WSSE), that is, an optimization problem considering link performance and hardware imperfection comprehensively.

[0064] Specifically, in steps S5-S7, when the power optimization, passive beamforming optimization, and UAV position optimization are performed, the following steps are included:

[0065] S51, the optimization problem is divided into several sub-problems by a fractional programming method, and the IoT transmission power in S5, the RIS phase configuration in S6, and the UAV position / attitude in S7 are iteratively solved under an alternating optimization framework; the RIS phase optimization adopts a manifold optimization algorithm, and the UAV position optimization adopts an SCA method to ensure efficient convergence of the solving process.

[0066] Specifically, in step S8, when the auxiliary variable is updated, after the optimization is completed, the server synchronously issues update instructions for the RIS phase vector, UAV flight attitude, and IoT transmission power through a wireless control link, realizing joint optimization and control of RIS reflection enhancement, UAV platform position adjustment, and IoT terminal power distribution.

[0067] Specifically, in step S9, the following steps are included:

[0068] S91, in the system running phase, hardware monitoring, channel estimation, and optimization control form a closed-loop feedback;

[0069] S92, when hardware damage mutation or performance degradation is detected, an online re-estimation and re-optimization process is automatically triggered, and algorithm hyperparameters and damage model weights are continuously updated through a deep reinforcement learning engine to adapt to dynamic environments, improving the adaptive ability and link robustness of the system.

[0070] In one specific embodiment, the robust design method of the air-reconfigurable intelligent surface communication system of the present embodiment includes the following stages:

[0071] The first stage is the system model construction stage, including step S1.

[0072] The second stage is the construction of the optimization problem, including step S2.

[0073] The third stage is the solving method of the optimization problem, including steps S3-S7.

[0074] The fourth stage is the performance simulation and actual verification, including steps S8-S10.

[0075] More specifically, in step S1, when modeling the system, the system model installs a programmable phase RIS on the UAV platform to assist the uplink data transmission between the ground IoT device and the multi-antenna CC.

[0076] In step S2, when calculating the auxiliary variable, in the channel model, the hardware quality factor is introduced, and hardware non-ideal factors such as phase noise, radio frequency amplifier nonlinear distortion, and quantization error are modeled as additional complex Gaussian distortion noise, thereby constructing a cascaded IoT-RIS-CC channel containing hardware damage.

[0077] On this basis, in step S3, when initializing the reference model, taking the weighted sum WSSE maximization as the overall optimization objective, the terminal transmit power, the passive phase configuration of the RIS, and the UAV position are jointly optimized to achieve robust improvement of link performance under the conditions of meeting multiple quality of service (QoS) and physical constraints such as decoding bit error rate, power budget, RIS phase constraint, and UAV flight range.

[0078] Since the proposed optimization problem has highly non-convex and multi-variable coupling characteristics, in step S4, when determining the optimization cycle, the FP decoupling technology is further adopted to transform the original problem into a series of sub-problems, and the AO framework is used to iteratively solve the transmit power, RIS phase, and UAV position. Among them, the RIS phase configuration uses the Manifold optimization algorithm to achieve fast convergence, and the UAV position optimization uses the SCA method to improve the solving efficiency; the robust optimization algorithm can take into account hardware non-ideality, complex channel conditions, and multi-objective performance requirements, and provides a feasible and efficient link adaptive optimization method for space-ground-integrated communication systems.

[0079] In step S5, when optimizing the power, according to formulas (1) and (2):

[0080]

[0081] where y CC is the received signal at the central controller (i.e., control center, CC), ξ CC is the hardware quality factor at the central controller, is the received signal before distortion, h k represents the kth cascaded IoT-UAV-CC channel, x k is the kth transmitted signal, n0 is an additive white Gaussian noise (AWGN), and is modeled as a CSCG (complex symmetric Gaussian) random vector with zero mean and covariance 2 I N , that is: K is the total number of channels or subcarriers in the system, z cis the noise due to non-ideal hardware distortion, which can be modeled as a CSCG random vector with zero mean and covariance Ω cc CC is obtained by equation (3):

[0082]

[0083] where Ω cc is the distortion noise covariance matrix of the control center (CC) due to hardware impairments, E(·) denotes the mathematical expectation of a random variable, denotes the conjugate transpose of P k is the transmit power of the kth Internet of Things (IoT) device, denotes the conjugate transpose of h k , is the power of additive white Gaussian noise, I N is an N x N identity matrix.

[0084] The kth cascaded IoT-UAV-CC channel is specifically represented by equation (4):

[0085]

[0086] where h k is the cascaded channel of the kth IoT device to the central controller, h d,k denotes the direct channel of the kth IoT device to the CC, denotes the set of all Internet of Things (IoT) devices in the system, G denotes the channel from the UAV to the CC, G H denotes the conjugate transpose of G; f k denotes the channel of the kth IoT device to the UAV; Φ is the reflection matrix of the RIS, Φ = diag(θ), where θ = [φ1, …, φ M ] T is the reflection coefficient vector on the RIS, θ m ∈ [0, 2π) represents the phase shift of the mth reflection unit, m ∈ 1, 2, …, M; is a standard mathematical expression, representing a mathematical representation that holds for any k.

[0087] G is shown in equation (5):

[0088]

[0089] where N t is the number of CC transmit antennas, M is the number of RIS elements, P is the number of Saleh-Valenzuela paths, is the complex fading coefficient of the ith path,​ an array response vector transmitted by the RIS, an array response vector transmitted by the RIS and received by the CC; an azimuth angle when a signal is received from the RIS unit in the ith path; an elevation angle when a signal is received from the RIS unit in the ith path; an angle of arrival when a signal is reflected from the RIS unit to the CC in the ith path.

[0090] f k as shown in equation (6):

[0091]

[0092] where N r is the number of IoT device receive antennas, L is the number of paths, is a complex fading coefficient of the ith path, an array response vector transmitted by the IoT device, an array response vector received by the RIS; denotes the conjugate transpose of

[0093] denotes an estimate of the symbol transmitted by the kth IoT device, as shown in equation (7):

[0094]

[0095] where w k is a beamforming vector at the CC for decoding the symbol transmitted by the kth IoT device; denotes the conjugate transpose of w k ; y C is a received signal vector at the CC;

[0096] R k denotes the maximum achievable spectral efficiency (SE) of the kth IoT device, as shown in equation (8):

[0097] R k = log2(1 + γ k ) (8)

[0098] where γ k denotes the instantaneous signal-to-interference-and-noise ratio (SINR) of the device, as shown in equation (9):

[0099]

[0100] where ξ IoT is a hardware quality factor of the IoT device, and ξ CC ​P is the hardware quality factor of the central controller, P k P is the transmit power of the kth user, P k h is the beamforming vector, h k is the cascade channel from the kth IoT device to the central controller, as in (4); I is the noise power; I 1,k , I 2,k , and I 3,k denote different types of interference terms, which are specifically expressed as (10)-(12):

[0101]

[0102] By jointly optimizing W (the set of beamforming vectors), P (the set of powers), Φ (the reflection matrix of the RIS), and U (the position of the UAV), the weighted sum of the spectral efficiency of the IoT devices is maximized, where ω k is the weight of the kth IoT device, and the constructed optimization problem is as in (13)-(17):

[0103]

[0104] Subject to

[0105]

[0106] |U-A0|≤d max (C5) (17)

[0107] where C2 denotes the normalization of the beamforming vectors; C3 denotes that the transmit power of each device does not exceed the maximum power P max ; C4 denotes that the modulus of the phase offset coefficient of the RIS reflection unit is equal to 1; C5 denotes that the position of the UAV U does not exceed the maximum distance d max from the initial position A0; under the given transmit power P, passive beamforming matrix Φ, and UAV position U, the optimization problem is simplified to the problem (P2') as in (18)-(19), which is an optimization problem about the beamforming vector W, and the objective function is the sum of the weighted spectral efficiency;

[0108]

[0109] Subject to

[0110] Since the beamforming vectors w k of each user are independent of each other, the problem (P2') is decomposed into K independent sub-problems; the constraint condition (C2) ensures the normalization of the beamforming vectors, and by maximizing the Rayleigh quotient, (20) is obtained:

[0111]

[0112] By the above simplification, the optimization problem (P1) is simplified as (21)-(22):

[0113]

[0114] Subject to C3-C5(22)

[0115] Problem (P3) cannot be solved directly due to the non-convexity of the objective function which is in the form of sum of ratio functions. To improve the solvability of problem (P3), Lagrangian dual transformation and fractional programming are used to extract the ratio from the logarithmic function. Optimization problem (P3) is equivalent to (23)-(25):

[0116]

[0117] Subject to C3-C5(24)

[0118]

[0119] where f FP is the reconstructed expression of the objective function derived by fractional programming and auxiliary variable transformation, is the auxiliary variable introduced to replace the non-convex ratio term in the original SINR expression; v k is the auxiliary variable introduced further to handle the fractional structure of each term in the ratio form objective function; A k is the signal power related term, B k is the sum of noise and interference power.

[0120] Based on (26)-(29):

[0121]

[0122] B k = I 1,k + I 2,k + I 3,k + σ 2 (28)

[0123]

[0124] Since each variable in optimization problem (P4) is independent of each other, an alternating optimization algorithm is used to solve it. For a given passive beamforming matrix Φ and UAV position U, problem (P4) is further simplified as problem (P5) where the transmit power P is the only optimization variable and the objective function is fP , to obtain formulas (30)-(33):

[0125]

[0126] Subject to C3(31)

[0127]

[0128] wherein, is an auxiliary variable v k is the conjugate complex of

[0129] Since problem (P5) has a concave objective function and affine constraints, the optimal solution can be obtained by solving ; the final transmit power optimal solution is , i.e., the minimum value between the calculated P max and P k ';

[0130]

[0131] wherein, P k ' is represented as formula (34):

[0132]

[0133] In step S6, in the passive beamforming optimization, under the condition of given transmit power P and UAV position U, the optimization problem of passive beamforming matrix Φ (corresponding to reflection phase vector θ) is formulated as problem (P6) of formulas (35)-(36), the objective being to minimize the weighted quadratic form plus linear term of θ:

[0134]

[0135] Subject to C4(36)

[0136] wherein, Ξ is represented as formula (37), is represented as formula (38):

[0137]

[0138]

[0139] wherein, f i is the channel vector of the ith IoT device to the aerial intelligent reflecting surface.

[0140] An optimal value of θ is obtained using a manifold optimization algorithm;

[0141] In step S7, during UAV position optimization, given the passive beamforming matrix Φ and transmit power P, the UAV position U is optimized by solving problem (P8) of equations (39)-(40); the objective function f of equation (41) is... U The sum of negative weighted spectral efficiencies reflects the coupling relationship between the overall system performance and the UAV's spatial location. Constraint (C5) ensures that the UAV's horizontal movement range does not exceed the maximum distance limit, thus balancing communication performance and flight safety.

[0142]

[0143] Subject to C5 (40)

[0144]

[0145] Among them, f U Let β be the sub-objective function for optimizing the UAV's position, and let β be the reference path gain constant in the path loss model. For the non-line-of-sight (NLoS) component of the channel vector from the IoT device to the RIS, φ k as auxiliary variables The name is changed to R, which is the mathematical operator for taking the real part.

[0146] By using a successive convex approximation algorithm, the non-convex problem (P8) can be transformed into a convex problem (P9) of equations (42)-(45):

[0147]

[0148]

[0149] (a lower ) 2 ≤|U (n) -O| 2 +2(U (n) -O) T (UU (n) ), (44)

[0150]

[0151] Where, 'a' is an auxiliary variable used to approximate the distance-related non-convex constraint as a convex form, 'b' is an auxiliary variable used to approximate the distance-related non-convex constraint as a convex form, 'A0' is the initial position of the UAV, and 'L' is the initial position of the UAV. k These are the location coordinates of the kth IoT device.

[0152] The optimization problem (P9) is a convex optimization problem, which can be solved using existing convex optimization algorithms (such as the interior point method).

[0153] Embodiment Two

[0154] As shown in Figure 3 The embodiment provides an electronic device 100, including a processor 200 and a storage medium 300; the storage medium 300 is configured to store instructions; the processor 200 is configured to operate according to the instructions to perform the steps of the robust design method of the aerial reconfigurable intelligent surface communication system described in Embodiment One.

[0155] Embodiment Three

[0156] The embodiment provides a storage medium 300, which stores a computer program, and the computer program is executed by the processor 200 to implement the robust design method of the aerial reconfigurable intelligent surface communication system described in Embodiment One.

[0157] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0159] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0160] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented processes, and the instructions executed on the computer or other programmable data processing devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or a plurality of flows and / or the functions specified in the block Figure 1 one block or a plurality of blocks.

[0161] The above description is only the preferred embodiments of the present application, and it should be pointed out that for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can be made, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. A robust design method for an airborne reconfigurable intelligent surface communication system, characterized in that, Includes the following steps: S1. System Modeling: Establish a system model including UAV, reconfigurable smart surface, terminal equipment and control center, and perform modeling and analysis of hardware non-ideal factors in the system model; S2. Calculation of auxiliary variables: Based on the current system parameters, calculate the auxiliary variables related to the optimization objective. The auxiliary variables are used to transform the optimization problem with coupled variables into at least two sub-problems that are easy to solve step by step. S3. Initialize the reference model: Determine the initial parameter configuration, including power allocation, reconfigurable smart surface phase configuration, and UAV position, to provide an initial solution for the optimization process; S4. Optimization Loop Determination: Determine whether the current system configuration meets the predetermined performance convergence conditions. If it does, output the optimization result; otherwise, proceed to the next step. S5. Power Optimization: Optimize the transmit power of the terminal equipment based on the current auxiliary variables and reference model; S6. Passive beamforming optimization: Passive beamforming optimization is performed on the phase matrix of the reconstructed smart surface to improve channel gain; S7. Drone Position Optimization: Adjust the spatial position of the drone according to the optimization target to achieve better system performance; S8. Auxiliary variable update: Update the auxiliary variables based on the optimized parameter results to provide new input for the next optimization loop; S9. Return to step S4 and determine whether the updated system meets the performance convergence condition. If it does not converge, continue to execute the optimization process of steps S5 to S8. S10: Output optimization results: When the system performance reaches the predetermined standard, the final optimized configuration is output, including the transmit power, reconfigurable smart surface phase and UAV position parameters.

2. The robust design method for an airborne reconfigurable intelligent surface communication system according to claim 1, characterized in that, In step S1, system modeling includes: S11. Install a programmable phase-reconfigurable intelligent surface reflection unit on the UAV platform, and configure a hardware damage monitoring module between the reconfigurable intelligent surface driver and the UAV flight control system. S12. Deploy hardware quality factor acquisition units in the control center and IoT terminals to collect hardware distortion indicators such as phase noise, I / Q imbalance, and RF amplifier nonlinearity in real time.

3. The robust design method for an airborne reconfigurable intelligent surface communication system according to claim 2, characterized in that, In step S2, the hardware non-ideal factors are actual hardware defects and limitations, including but not limited to the hardware losses of the transceiver, the nonlinear characteristics of the power amplifier, phase noise, quantization error and finite resolution effect. The auxiliary variables include auxiliary variables used to decouple the SINR expression, distance auxiliary variables in UAV position optimization, and Lagrange multipliers used for dual transformation; During auxiliary variable calculation, the base station and the UAV-reconfigurable smart surface collaboratively transmit pilot sequences during system operation, the IoT terminal transmits weighted data frames uplink, and the control center feeds back the received channel information.

4. The robust design method for the airborne reconfigurable intelligent surface communication system according to claim 3, characterized in that, In step S3, when initializing the reference model, the central processing server uses compressed sensing and least squares methods, combined with hardware monitoring data, to jointly estimate the parameters of the cascaded IoT-reconfigurable smart surface-control center's multiple input multiple output channels and hardware damage model.

5. The robust design method for an airborne reconfigurable intelligent surface communication system according to claim 4, characterized in that, In step S4, during the optimization of the loop determination, the server aims to maximize the weighted sum and spectral efficiency, and constructs an optimization problem of link performance and hardware non-ideals.

6. The robust design method for an airborne reconfigurable intelligent surface communication system according to claim 5, characterized in that, In steps S5-S7, during power optimization, passive beamforming optimization, and UAV position optimization, the following are included: S51. The optimization problem is decomposed into several subproblems using fractional programming. Under the alternating optimization framework, the IoT transmission power in S5, the reconfigurable smart surface phase configuration in S6, and the UAV position / attitude are solved iteratively. The reconfigurable smart surface phase optimization adopts the manifold optimization algorithm, and the UAV position optimization adopts the continuous convex approximation method.

7. The robust design method for an airborne reconfigurable intelligent surface communication system according to claim 6, characterized in that, In step S8, when the auxiliary variables are updated, after the optimization is completed, the server synchronously sends update instructions for the reconfigurable smart surface phase vector, UAV flight attitude and IoT transmission power through the wireless control link, so as to realize the joint optimization control of reconfigurable smart surface reflection enhancement, UAV platform position adjustment and IoT terminal power allocation.

8. The robust design method for an airborne reconfigurable smart surface communication system according to any one of claims 1-7, characterized in that, Step S9 includes: S91. During the system operation phase, hardware monitoring, channel estimation, and optimization control form a closed-loop feedback. S92. When a hardware damage mutation or performance degradation is detected, an online re-evaluation and re-optimization process is automatically triggered, and the algorithm hyperparameters and damage model weights are continuously updated through a deep reinforcement learning engine to adapt to the dynamic environment.

9. An electronic device, characterized in that, It includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform the steps of the robust design method for an airborne reconfigurable smart surface communication system according to any one of claims 1-8.

10. A storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a robust design method for an airborne reconfigurable smart surface communication system according to any one of claims 1-8.

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