Precoding, phase shifting and deployment joint optimization method and system of air-ground Internet of Vehicles

Abstract

The invention discloses a pre-coding, phase shift and deployment joint optimization method and system for an air-ground Internet of Vehicles, and the method comprises the steps: constructing an air-ground Internet of Vehicles system model based on an MIMO technology and an IRS, and the air-ground Internet of Vehicles system model comprises a network model and a communication model; based on the constructed air-ground vehicle networking system model, modeling a weighting and rate maximization problem as a joint optimization problem; respectively solving a joint optimization problem until convergence by adopting an iterative optimization algorithm through a Lagrange multiplier method, a multi-dimensional complex quadratic transformation technology and a projection finite memory quasi-Newton method, and obtaining an optimized deployment strategy to realize weighting and rate maximization; through joint optimization of precoding, phase shift and unmanned aerial vehicle deployment and fusion of MIMO and IRS technologies, the limitation of separation and optimization of the traditional Internet of Vehicles is solved, the weighting sum rate is remarkably improved, the dynamic environment adaptability is enhanced, the performance and engineering feasibility are considered, and efficient communication support is provided for intelligent traffic.

Description

Technical Field

[0001] This invention belongs to the field of vehicle networking technology, specifically relating to a method and system for joint optimization of precoding, phase shifting and deployment in air-to-ground vehicle networking. Background Technology

[0002] As the core of Intelligent Transportation Systems (ITS), Vehicle-to-Everything (V2X) technology improves traffic efficiency, road safety, and user experience, serving as a key support for applications such as autonomous driving and vehicle-to-infrastructure (V2I) communication. However, traditional V2X relies heavily on ground-based base stations (BS), facing significant challenges in complex environments, including signal congestion, coverage blind spots, and multipath interference in densely populated urban areas with high-rise buildings, mountainous valleys, and remote rural areas. Especially under high vehicle density or extreme weather conditions, communication links are prone to interruption, making it difficult to meet the low latency (<10 milliseconds) and high reliability (>99.999%) requirements of V2X services. Against this backdrop, utilizing Unmanned Aerial Vehicles (UAVs) to build air-to-ground V2X has become a breakthrough solution. UAVs, with their flexible hovering and autonomous trajectory planning capabilities, can quickly fill coverage blind spots left by ground-based base stations.

[0003] While drone-assisted communication can extend coverage, the performance bottleneck of air-to-ground vehicle-to-everything (V2X) networks remains significant due to dynamic fluctuations in channel quality and insufficient spectral efficiency. To address this challenge, academia has begun exploring drone technology equipped with Intelligent Reflecting Surfaces (IRS). IRS intelligently redirects signals by adjusting electromagnetic wave propagation characteristics and using phase shifting, thereby optimizing wireless signal transmission quality and suppressing multipath effects and interference. Simultaneously, to address the issue of limited spectrum resources, Multiple Input Multiple Output (MIMO) technology can be used to achieve spatial signal multiplexing and improve channel capacity. Therefore, integrating MIMO technology with drones equipped with intelligent reflectors for application in air-to-ground V2X networks can further enhance network performance.

[0004] Prior art 1 proposes an optimization algorithm for maximizing the weighted total rate of multiple users assisted by a smart reflector. First, a mathematical model of the smart reflector-assisted communication scenario is established, considering the discrete characteristics of the smart reflector phase shift parameters. A joint optimization problem of base station transmission precoding and smart reflector phase shift parameters is established with the objective of maximizing the weighted total rate of users. Then, a block coordinate descent algorithm is used to decouple the problem variables. Finally, the weighted minimum mean square error and genetic algorithm are used to iteratively solve for the base station precoding and smart reflector phase shift parameters respectively. Simulation results show that the proposed algorithm can improve the overall communication rate of the communication system.

[0005] Existing technology 2 deploys reconfigurable smart surfaces on drones. Leveraging the flexible movement trajectories and on-demand deployment capabilities of drones, it effectively addresses the problem of reduced information transmission efficiency caused by obstacles such as trees and buildings. Specifically, for vehicle-to-vehicle (V2V) communication scenarios assisted by aerial reconfigurable smart surfaces, this paper proposes a geometry-based 3D channel model. This model comprehensively considers the rotation and arbitrary trajectory movement of the drone in three degrees of freedom, as well as the impact of drone attitude changes on the channel model, and introduces a time-varying spatial phase. Furthermore, it considers the real-time motion speed and direction of the transmitter, receiver, and drone, provides an expression for the complex channel impulse response, and conducts a detailed analysis of key channel statistical characteristics such as the spatial cross-correlation function, the time-domain autocorrelation function, and the channel capacity.

[0006] Existing technology 3 combines UAVs with spectrum sharing technology to establish high-quality communication links and improve spectrum resource utilization efficiency. Specifically, a UAV cognitive relay communication network assisted by an intelligent reflector is designed. By jointly optimizing the UAV's location deployment, the beamforming of the secondary base station, and the phase shift matrix of the intelligent reflector, the transmission rate of secondary users in the spectrum sharing network is maximized. To solve the established non-convex problem, it is decoupled into three sub-problems, and then an alternating optimization algorithm is proposed to iteratively optimize the variables. The UAV's location is optimized using a continuous convex approximation method; the beamforming of the secondary base station is optimized using a direct fractional programming method; and the phase shift matrix of the intelligent reflector is optimized using a combination of direct fractional programming and alternating direction multiplier method.

[0007] However, the problem with existing technologies is:

[0008] First, existing technology 1 is typically based on static channel assumptions and fails to fully reflect the time-varying characteristics of cascaded channels in air-to-ground vehicle-to-everything (V2X) networks. When a drone equipped with a smart reflector provides services to ground vehicles, the signal needs to travel through a multi-hop transmission path: base station → drone equipped with smart reflector → ground vehicle. This process significantly increases the dimensionality of the equivalent channel. Therefore, existing technology 1 struggles to accurately capture the real-time coupling relationship between cascaded channel vectors, resulting in limited performance in dynamic multi-hop scenarios.

[0009] Secondly, existing technology 2 neglects the impact of MIMO and smart reflector beamforming on the transmission performance of air-to-ground vehicle networks. For example, the flight altitude of a drone changes the line-of-sight probability of the air-to-ground link, thus affecting the reflection efficiency of the smart reflector. Furthermore, to achieve communication fairness among vehicles with different data rates, differentiated weights need to be assigned based on their service requirements. However, these key engineering factors have not received sufficient attention and systematic analysis in existing research (such as existing technologies 2 and 3), and further in-depth exploration and modeling are urgently needed.

[0010] Finally, since the number of intelligent reflector units carried by UAVs can reach hundreds, the optimization problem of their phase offset is a high-dimensional non-convex optimization problem. Although the Block Coordinate Descent (BCD) algorithm can reduce the optimization complexity through variable decoupling, the update of the phase offset still depends on the location of the fixed infrastructure. When the intelligent reflector is deployed on the UAV, its spatial position and attitude directly affect the incident angle and reflection path of the signal. However, existing studies (such as prior art 1 and 3) generally neglect the joint influence of phase offset and UAV three-dimensional coordinates, resulting in the reflection gain not being fully exploited.

[0011] The difficulty in solving the above technical problems:

[0012] First, in dynamic multi-node systems composed of base stations, UAVs, and vehicles, signals need to be reflected by intelligent reflectors and transmitted via multiple hops. Therefore, designing an optimal precoding strategy that maximizes channel capacity is crucial for improving transmission efficiency. However, existing solutions struggle to adapt to the high-dimensional time-varying characteristics of cascaded channels. Second, while intelligent reflector phase offset optimization can control signal reflection direction and improve quality, this problem is inherently a high-dimensional non-convex optimization. Its solution is highly dependent on UAV deployment strategies and real-time channel conditions, and current methods do not fully consider the dynamic impact of UAV spatial pose on the reflection path. Finally, UAV deployment strategies have a significant impact on system performance. However, existing optimization schemes often focus on single objectives (such as energy consumption or coverage), neglecting their correlation with MIMO channel matrices and intelligent reflector beamforming. Furthermore, constrained by dynamic topology changes and time-varying multi-dimensional resource constraints, it is necessary to coordinate the optimization of practical engineering factors such as altitude control (affecting the line-of-sight probability of air-to-ground links) and differentiated vehicle weight allocation (ensuring communication fairness). These challenges collectively restrict the overall improvement of air-to-ground vehicle-to-everything (V2X) performance. Summary of the Invention

[0013] The technical problem to be solved by the present invention is to provide a joint optimization method and system for precoding, phase shifting and deployment of air-to-ground vehicle networking, which addresses the shortcomings of the prior art and solves the technical problems of low spectrum efficiency, poor adaptability to dynamic environment and infeasible computational complexity caused by the separate optimization of precoding, IRS phase shifting and UAV deployment.

[0014] The present invention adopts the following technical solution:

[0015] A joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicle-to-everything (V2X) communication includes the following steps:

[0016] Construct an air-to-ground vehicle-to-everything (V2X) system model based on MIMO technology and IRS, including a network model and a communication model;

[0017] Based on the constructed air-to-ground vehicle-to-everything (V2X) system model, the weighted sum rate maximization problem is modeled as a joint optimization problem;

[0018] An iterative optimization algorithm is adopted, using the Lagrange multiplier method, multidimensional complex quadratic transformation technique and projective finite memory quasi-Newton method to solve the joint optimization problem until convergence, and obtain the optimized deployment strategy to maximize the weighted sum rate.

[0019] Preferably, the network model is as follows:

[0020] Assuming all base stations are synchronized, define... d s,v Indicates the s-th Send to the vth one on the spectrum The vehicle's data symbol, in the bth... At the base station, data symbol d s,v Through the precoded vector w b,s,v Perform precoding, definition W = [W1,...,W b ,...W B ], precoded symbol g b,s Represented as For the v-th vehicle, the frequency domain channel of the base station-vehicle link on the s-th spectrum is defined as follows:

[0021] The frequency domain channel of the base station-smart reflector-vehicle link on the s-th spectrum. for:

[0022]

[0023] in, For the frequency domain channel from the u-th smart reflector to the v-th vehicle on the s-th spectrum, Θ b,u,s For the frequency domain channel from the b-th base station to the u-th smart reflector on the s-th spectrum, F u Let be the phase shift matrix of the u-th intelligent reflector;

[0024] Define the feasible set of reflection coefficients for smart reflective surfaces Frequency domain channel at the v-th vehicle

[0025] Preferably, the communication model is as follows:

[0026] The signal y received by the v-th vehicle from the b-th base station b,s,v for:

[0027]

[0028] The signal y of the vth vehicle on the sth frequency spectrum s,v for:

[0029]

[0030] Among them, o s,v The additive white Gaussian noise at the receiving end. And j≠v, For the base station-vehicle link, the frequency domain channel is located on the s-th spectrum. for h s,v The conjugate transpose of , For the frequency domain channel from the u-th smart reflector to the v-th vehicle on the s-th spectrum, C represents the number of antennas for each vehicle, and Y represents the number of reflective elements on the smart reflective surface installed on each drone. For F u The conjugate transpose of F u Let Θ be the phase shift matrix of the u-th intelligent reflector. b,u,s For the frequency domain channel from the b-th base station to the u-th smart reflector on the s-th spectrum, w b,s,v For the precoded vector, w s,j for V represents the number of vehicles, B represents the number of base stations, and d represents the number of base stations. s,j For the data symbols transmitted to the j-th vehicle on the s-th spectrum;

[0031] Data symbol d on the s-th spectrum s,v The signal-to-interference-plus-noise ratio γ at the v-th vehicle s,v Represented as:

[0032]

[0033] Where, σ 2 Noise power; I C It is the identity matrix. For w s,v The conjugate transpose of , w b,s,v h is the precoding vector. s,v for For the frequency domain channel at the v-th vehicle, for h s,v The conjugate transpose of w s,j for

[0034] The data rate R of the v-th vehicle on the s-th spectrum s,v for:

[0035]

[0036] Weighted sum rate R tot for:

[0037]

[0038] Where, ω v Let be the speed weight of the v-th vehicle.

[0039] Preferably, the air-to-ground vehicle-to-everything (V2X) system model based on MIMO technology and IRS includes B base stations, V vehicles, and U drones, with the sets of base stations, vehicles, and drones respectively defined as... and

[0040] Each base station is equipped with A antennas, and each vehicle is equipped with C antennas;

[0041] Each drone is equipped with a smart reflective surface with Y reflective elements, defined as follows:

[0042] The air-ground integrated vehicle-to-everything (V2X) network has S spectrums, defined as follows: Using the Cartesian coordinate system, defined at the u-th... The three-dimensional coordinates of the drone equipped with a smart reflector are L u =(x u ,y y ,z u ), L={L1,...,L U}

[0043] Preferably, the joint optimization problem includes precoding design, IRS phase modulation, and UAV deployment strategy. First, a weighted sum rate maximization problem is modeled. Then, an auxiliary variable Q is introduced, transforming the modeled optimization problem P1 into P2. The optimal solution of the auxiliary variable Q is then determined. Substitute the objective function of P2 to complete the problem transformation.

[0044] Preferably, the weighted sum rate maximization problem is modeled as follows:

[0045]

[0046] in, Let [x] be the maximum transmit power of the b-th base station; min ,x max ]、[y min ,y max ] and [z min ,z max To define the range of horizontal, longitude, and vertical positions of the UAV equipped with the intelligent reflector, constraint C1 limits the transmission power of each base station, constraint C2 is the phase amplitude constraint of the intelligent reflector, and constraint C3 is the position constraint of the UAV equipped with the intelligent reflector.

[0047] Preferably, the objective function for optimization problem P2 is:

[0048]

[0049] Among them, R tot For weighted sum rate, W, F, and L represent the precoding design strategy, phase shift optimization strategy, and UAV deployment strategy, respectively. ω v Let be the speed weight of the v-th vehicle. Let Q be the optimal solution for the auxiliary variable, V be the number of vehicles, and S be the number of spectrums. For the frequency domain channel at the v-th vehicle, for h s,v The conjugate transpose of σ 2 For noise power, I C It is an identity matrix.

[0050] Preferably, an iterative optimization algorithm is used, employing the Lagrange multiplier method, multidimensional complex quadratic transformation technique, and projective finite-memory quasi-Newton method to solve the joint optimization problem until convergence, specifically as follows:

[0051] Given a UAV deployment L, the precoding design strategy W and phase shift optimization strategy F are iteratively solved using the Lagrange multiplier method. Based on the obtained precoding design strategy W and phase shift optimization strategy F, the UAV deployment strategy L is obtained using the projective finite-memory quasi-Newton method. The above process is repeated until convergence, and the UAV deployment strategy that yields the maximum weighted sum rate is selected as the optimal UAV deployment strategy (L). * .

[0052] Preferably, the optimal drone deployment strategy (L) * for:

[0053]

[0054] Among them, R tot For weighted sum rate;

[0055] Iteration stops when any of the following conditions are met:

[0056] 1) Where τ1 is the gradient threshold;

[0057] 2) Where τ2 is the iteration threshold;

[0058] 3) Reaching the maximum number of iterations κ max .

[0059] Secondly, embodiments of the present invention provide a joint optimization system for precoding, phase shifting, and deployment in air-to-ground vehicle networking, comprising:

[0060] The module constructs a model of an air-to-ground vehicle-to-everything (V2X) system based on MIMO technology and IRS, including a network model and a communication model.

[0061] The joint module, based on the constructed air-to-ground vehicle-to-everything (V2X) system model, models the weighted sum rate maximization problem as a joint optimization problem;

[0062] The optimization module employs an iterative optimization algorithm, using the Lagrange multiplier method, multidimensional complex quadratic transformation technique, and projective finite-memory quasi-Newton method to solve the joint optimization problem until convergence, thereby obtaining an optimized deployment strategy to maximize the weighted sum and rate.

[0063] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking.

[0064] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking.

[0065] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking.

[0066] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, wherein when the computer program is executed by the electronic device, it implements the steps of the above-described joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking.

[0067] Compared with the prior art, the present invention has at least the following beneficial effects:

[0068] This invention presents a joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicular networks. Addressing the problems of high relay node deployment costs, susceptibility to communication link obstruction, and severe multipath fading in traditional terrestrial vehicular networks, this invention designs an air-to-ground vehicular network system architecture integrating Multiple-Input Multiple-Output (MIMO) technology and an Intelligent Reflector (IRS). Based on this architecture, a joint optimization method for precoding, phase shifting, and deployment is designed. By integrating IRS components into the UAV platform, this invention significantly improves the flexibility and dynamic adaptability of the network architecture. It also effectively utilizes the programmable nature of the IRS to intelligently control the signal propagation environment, thereby improving communication performance without increasing transmission power. Furthermore, the introduction of the joint optimization algorithm enables efficient allocation and collaborative optimization of network resources in complex scenarios with multiple users, multiple nodes, and multidimensional constraints, ultimately achieving a significant optimization effect in weighted sum rate. Compared to existing optimization methods that only perform precoding or node deployment, this invention, through deep integration of system modeling and algorithm design, exhibits superior performance in weighted sum rate, providing technical support and application prospects for next-generation intelligent vehicular networks.

[0069] Furthermore, by quantizing the channel parameters of multi-hop transmission, the attenuation and coupling relationship of the signal in the air-to-ground link is accurately captured. This provides a high-fidelity channel model foundation for subsequent optimization, avoids optimization deviations caused by model simplification, and makes precoding and phase shift adjustment more closely match the actual propagation characteristics.

[0070] Furthermore, by decomposing the desired signal and interference terms, the impact of noise and multi-user interference on communication quality is precisely quantified, and a weighted sum rate index is defined based on this. This provides a scientific quantitative basis for the optimization objective, ensuring that the weighted sum rate maximization objective is measurable and optimizable, while also guaranteeing fairness in vehicle communication through weights.

[0071] Furthermore, the three-dimensional position of the UAV is defined using a Cartesian coordinate system, standardizing hardware parameters and providing a structured framework for system modeling. This enhances the operability of the method, facilitating parameter adjustment based on actual scenarios (such as vehicle density and coverage area), and providing clear variable boundaries for subsequent optimization.

[0072] Furthermore, by utilizing auxiliary variables to decouple the strong coupling between precoding, phase shifting, and deployment, the non-convex objective function is transformed into a more manageable form. This reduces the solution complexity of high-dimensional non-convex problems, making iterative solutions feasible for joint optimization problems that were originally difficult to solve directly, thus providing an algorithmic foundation for engineering implementation.

[0073] Furthermore, by constraining C1, C2, and C3, it is ensured that the optimization results comply with hardware physical limitations and safety specifications. A balance is struck between performance enhancement and resource constraints to avoid power overload or drone overreach caused by unrestricted optimization, ensuring the results have practical deployment value.

[0074] Furthermore, the original objective function is rewritten, removing complex coupling terms to better suit the step-by-step solution of the iterative algorithm. This improves the solvability of the objective function, reduces computational redundancy during the iteration process, and simulations show that it can accelerate the convergence speed, with the number of iterations controlled within 9-11.

[0075] Furthermore, by using a loop of fixed deployment to optimize signal parameters and then optimizing deployment based on signal parameters, the optimal solution is gradually approached. This approach balances the accuracy and efficiency of solving high-dimensional problems, overcomes the computational bottleneck of joint optimization, reduces complexity compared to a single algorithm, and improves optimization accuracy through the fusion of multiple methods.

[0076] Furthermore, projection operations ensure the location is within the feasible region, a multi-starting-point strategy avoids local optima, and gradient thresholds control iteration costs. This achieves precise optimization of the UAV's 3D position, improves coverage uniformity and channel gain, while avoiding ineffective iterations and balancing performance and computational cost.

[0077] Furthermore, this invention establishes an air-to-ground vehicle-to-everything (V2X) communication model that supports three-dimensional deployment, achieving wide-area coverage and dynamic adaptation in communication scenarios. Specifically, in terms of system modeling, this invention constructs an air-to-ground V2X system model based on MIMO and IRS technologies, comprehensively considering network topology and physical communication processes, covering both network and communication model levels. Unlike traditional models that only rely on communication between ground vehicles and base stations, this invention introduces UAVs equipped with IRS into the airborne communication relay system, significantly expanding the system's deployment dimension in vertical space. This allows vehicles to maintain high-reliability, high-capacity communication connections even in complex terrain, communication blind spots, or emergency scenarios. In addition, the system model considers the multi-antenna characteristics of MIMO technology and the reflection path control capability of IRS, enabling effective modeling of channel characteristics and cooperative gain in multi-user, multi-path propagation environments. The proposed model provides precise physical support and mathematical foundation for subsequent joint optimization algorithms, making the optimization results practically feasible and providing engineering guidance, effectively promoting the development of air-to-ground converged communication systems towards dynamic adaptation, high frequency, and high efficiency.

[0078] Furthermore, this invention jointly optimizes precoding, intelligent reflector phase shifting, and UAV deployment strategies, enhancing the collaborative control capabilities of the communication link. Specifically, for the constructed air-to-ground vehicle-to-everything (V2X) communication model, this invention further formalizes the network capacity maximization problem into a joint optimization problem, encompassing three core sub-problems: precoding design, IRS phase modulation, and UAV 3D deployment. The original complex non-convex problem is reasonably transformed by introducing auxiliary variables. This transformation reduces the difficulty of solving the problem, allowing optimization objectives from different physical layers to be processed collaboratively within a unified mathematical framework. Moreover, through modeling the weighted sum rate and designing the objective function, this invention can differentiate resource allocation and performance trade-offs based on vehicle communication quality, location, and bandwidth usage requirements, solving the problems of parameter separation optimization, slow response, and local optima in existing technologies.

[0079] Furthermore, this invention introduces an efficient iterative algorithm to reduce the difficulty of solving complex optimization problems and improve computational efficiency. Specifically, in terms of algorithm design, based on the aforementioned joint optimization problem, this invention proposes an efficient iterative solution algorithm with polynomial time complexity. This algorithm decomposes the overall optimization problem into three sub-problems and solves each sub-problem efficiently using appropriate methods. Compared with traditional exhaustive or hierarchical optimization methods, the algorithm proposed in this invention significantly reduces computational complexity and hardware implementation costs while ensuring optimization accuracy. This provides theoretical and technical support for real-time communication scheduling and dynamic deployment in resource-constrained vehicle-to-everything (V2X) environments, demonstrating superior practicality and scalability.

[0080] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0081] In summary, this invention, through a closed-loop design of dynamic modeling, theoretical derivation, engineering optimization, and experimental verification, combines theoretical rigor with engineering practicality. It not only fills the theoretical gap in weighted and rate optimization of air-to-ground vehicle networks, but also provides quantifiable and adjustable key parameter optimization basis for system deployment, promoting the evolution of intelligent transportation systems towards high reliability and low latency.

[0082] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0083] Figure 1 This is a flowchart of the method of the present invention;

[0084] Figure 2 This is a schematic diagram of an air-to-ground vehicle network based on multiple-input multiple-output (MIMO) technology and intelligent reflective surface (IRS) provided in an embodiment of the present invention;

[0085] Figure 3 This is a schematic diagram of the channels in each frequency domain for downlink transmission;

[0086] Figure 4 This is a schematic diagram for the iterative optimization used to solve P1;

[0087] Figure 5 To compare the performance of the embodiments of the present invention with comparative schemes 1, 2, and 3 in terms of weighted sum rate under different vehicle numbers;

[0088] Figure 6 To compare the performance of the embodiments of the present invention with comparative schemes 1, 2, and 3 in terms of weighted sum rate under different maximum transmission power of base stations;

[0089] Figure 7 The effect of the number of reflective elements on the weighted sum rate of embodiments of the present invention;

[0090] Figure 8 The convergence of the iterative optimization algorithm in this embodiment of the invention;

[0091] Figure 9 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0092] Figure 10 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0093] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0094] This invention provides a joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicular networks (V2V). It constructs an V2V architecture based on Multiple-Input Multiple-Output (MIMO) technology and Intelligent Reflector Surfaces (IRS). By utilizing energy-efficient and easily deployable UAVs equipped with IRS, it partially replaces ground relay nodes in traditional V2V networks, significantly improving communication capacity. Furthermore, to fully exploit the performance potential of this architecture, the weighted sum rate maximization problem is modeled as a joint optimization problem involving precoding design, IRS phase modulation, and UAV 3D deployment. For the constructed non-convex optimization model, an iterative solution algorithm with polynomial time complexity is proposed to progressively approximate a feasible solution to the weighted sum rate maximization problem. Specifically, the Lagrange multiplier method combined with the Multi-Dimensional Complex Quadratic Transform (MCQT) technique is used to optimize the precoding matrix and the IRS phase shift strategy, respectively. At the same time, the Limited-Memory Broyden Fletcher Goldfarb Shanno (L-BFGS) method is introduced to efficiently solve the UAV 3D deployment problem constrained by dynamic topology and obtain the optimized deployment strategy.

[0095] Please see Figure 1 This invention presents a joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicle-to-everything (V2X) networks. First, based on the least squares (LS) channel estimation method, a joint channel model is constructed, incorporating the three-dimensional motion trajectory of the UAV, vehicle dynamic distribution, co-channel interference, self-interference, and Ricean fading factor. Then, using generalized integrals and infinite series summation operations, an accurate analytical expression for the total channel capacity is derived. Building upon this, to address the high computational complexity of the accurate expression, a truncated series approximation method is designed. Through theoretical analysis, a closed-form mapping relationship between the number of truncated terms and the approximation error is established. Finally, combined with Monte Carlo simulation verification, the error between the accurate expression and the approximation method is presented, and the total capacity difference between FD-NOMA and FD-OMA schemes is compared and analyzed, quantifying the impact of key parameters (such as the Ricean factor) on the total channel capacity. The specific steps are as follows:

[0096] S1. Construct an air-to-ground vehicle network system model based on multiple-input multiple-output (MIMO) technology and intelligent reflector (IRS), including a network model and a communication model.

[0097] 1.1 Network Model

[0098] Please see Figure 2 , Figure 2 This is a schematic diagram of an air-to-ground vehicle network based on Multiple-Input Multiple-Output (MIMO) technology and Intelligent Reflector Surface (IRS), including B base stations, V vehicles, and U drones. The sets of base stations, vehicles, and drones are defined as follows: and Each base station is equipped with A antennas (defined) Each vehicle is equipped with C antennas (defined). Each drone is equipped with a smart reflective surface with Y reflective elements, defined as follows: In addition, the air-ground integrated vehicle-to-everything (V2X) network has S spectrums, defined as follows: Furthermore, using the Cartesian coordinate system, the u-th coordinate system is defined... The three-dimensional coordinates of the drone equipped with a smart reflector are L u =(x u ,y y ,z u Define L = {L1, ..., L} U}

[0099] Assume all base stations are synchronized. This invention defines... Where d s,v Indicates the s-th Send to the vth one on the spectrum The vehicle's data symbols, and normalized power. In the b-th... At the base station, data symbol d s,v It needs to be precoded using vector w b,s,v Perform precoding, definition

[0100] W = [W1,...,W b ,...W B Therefore, the precoded symbol g b,s Represented as For the v-th vehicle, the frequency domain channel of the base station-vehicle link on the s-th spectrum is defined as follows:

[0101] Please see Figure 3 , Figure 3This is a schematic diagram of the frequency domain channels for downlink transmission. Specifically, the frequency domain channel of the base station-smart reflector-vehicle link on the s-th spectrum is defined as follows: It is represented as:

[0102]

[0103] in, For the frequency domain channel from the u-th smart reflector to the v-th vehicle on the s-th spectrum, Θ b,u,s For the frequency domain channel from the b-th base station to the u-th smart reflector on the s-th spectrum, F u Let be the phase shift matrix of the u-th intelligent reflector. And F u Represented as This invention defines the feasible set of reflection coefficients of intelligent reflective surfaces as follows: This invention assumes that the amplitude and phase associated with the intelligent reflector elements are controlled independently and continuously. Furthermore, it considers the frequency domain channel at the v-th vehicle. Composed of two parts, we get:

[0104]

[0105] 1.2 Communication model

[0106] The signal y received from the b-th base station at the v-th vehicle b,s,v Represented as:

[0107]

[0108] Since V vehicles can be served by B base stations simultaneously, the signal received at the v-th vehicle should be an overlapping signal. In this case, the signal of the v-th vehicle in the s-th spectrum is defined as y. s,v , Represented as:

[0109]

[0110] Among them, o s,v The additive white Gaussian noise (AWGN) at the receiving end (i.e., the v-th vehicle); according to equation (3), y s,v It was further rewritten as:

[0111]

[0112] in, And j ≠ v. It is worth noting that ys,v From the expected signal and interference signals It consists of two parts. achievable 1 :

[0113] and

[0114]

[0115] For ease of processing, define F = diag(F1,...,F U ), Equation (5) is further rewritten as:

[0116]

[0117] By using equation (2), in equation (8) At the same time, define Equation (8) is further rewritten as:

[0118]

[0119] Based on the above analysis, for the data symbol d on the s-th spectrum s,v In terms of the signal-to-interference-plus-noise ratio (SINR) γ at the v-th vehicle... s,v Represented as:

[0120]

[0121] Where, σ 2 Noise power; I C It is the identity matrix.

[0122] Therefore, the data rate R of the v-th vehicle on the s-th spectrum is s,v for:

[0123]

[0124] In air-to-ground integrated vehicle networks that utilize intelligent reflective surfaces and MIMO (Multi-In, Multiple-Out) technology, the weighted sum rate R... tot It can be calculated as:

[0125]

[0126] Where, ω v Let be the speed weight of the v-th vehicle.

[0127] S2. Based on the obtained air-to-ground vehicle network system model, construct a weighted sum rate maximization problem, and transform the problem by introducing auxiliary variables;

[0128] S201, Modeling the Weighted Sum Rate Maximization Problem

[0129] By optimizing the precoding design W, phase shift F, and UAV deployment L, this invention aims to maximize the weighted sum rate R. tot The weighted sum rate maximization problem is modeled as follows:

[0130]

[0131] in, Let [x] be the maximum transmit power of the b-th base station; min ,x max ]、[y min ,y max ] and [z min ,z max [P1] represents the range of horizontal, longitude, and vertical positions for the UAV equipped with the intelligent reflector. In P1, constraint C1 limits the transmit power of each base station. Constraint C2 is the phase amplitude constraint for the intelligent reflector. Constraint C3 is the position constraint for the UAV equipped with the intelligent reflector.

[0132] S202, Problem Transformation

[0133] Introduce auxiliary variable Q = [q 1,1 ,...,q 1,V ,q 2,1 ,...,q S,V ] T , The objective function of P1 is rewritten as:

[0134]

[0135] Among them, R s,v (W,F,L) is represented as:

[0136]

[0137] Therefore, by using the auxiliary variable Q, the modeled optimization problem P1 can be transformed into:

[0138]

[0139] Given the optimization variables (W, F, L), and based on this, let The optimal solution for the auxiliary variable Q can be obtained. Then, the present invention will Substituting the objective function into P2, it can be rewritten as:

[0140]

[0141] in,

[0142] S3. Based on the obtained joint optimization problem, an iterative optimization algorithm is adopted. The optimal precoding design, intelligent reflector phase shift optimization and UAV deployment strategy are obtained by iteratively solving the problem through the Lagrange multiplier method, multidimensional complex quadratic transformation technique and projective finite memory quasi-Newton method. The complexity of the proposed iterative optimization algorithm is also given.

[0143] Since the objective function of P2 remains non-convex and complex, the joint optimization of the precoding design W, phase shift F, and UAV deployment L is very challenging. In this case, an iterative optimization algorithm is proposed to solve P1, such as... Figure 4 As shown:

[0144] First, given the UAV deployment L, the precoding design strategy W and the phase shift optimization strategy F are iteratively solved using the Lagrange multiplier method.

[0145] Then, based on the obtained precoding design strategy W and phase shift optimization strategy F, the UAV deployment strategy L is obtained by using the projective finite memory quasi-Newton method (L-BFGS).

[0146] Finally, repeat the above process until convergence.

[0147] S301, Precoding Design

[0148] In equation (17), it can be seen that the optimization variables (W, F, L) only affect R. s,v At this stage, given (Q, F, L), the modeled weighted sum rate maximization problem is simplified to the following precoding design problem:

[0149]

[0150] Among them, R Precoding (W) can be represented as:

[0151]

[0152] Although the optimization variables (Q, F, L) are given in advance, P3.1 is a high-dimensional sum-of-fractions problem, making it difficult to solve directly. Therefore, this invention introduces an auxiliary variable U = [u...] in P3.1. 1,1 ,...,u 1,V ,u 2,1 ,...,u S,V ], Then, P3.1 can be rewritten as:

[0153]

[0154] Among them, R Precoding (W,U) is represented as:

[0155]

[0156] Where Θ1(U) and Θ2(U) are:

[0157]

[0158] and

[0159]

[0160] in, Indicates the real part.

[0161] This invention further iteratively solves P3.2, given the optimization variable (W), let Obtain the optimal solution for auxiliary variable U.

[0162]

[0163] Based on this, Substitute into P3.2. Objective function R Precoding (W) is rewritten in the following form:

[0164]

[0165] in, For Kronecker's product;

[0166] Then, the present invention will impose constraints. Transformed into:

[0167]

[0168] in, and By using equations (25) and (26), P3.2 is further rewritten as:

[0169]

[0170] Due to the matrix and Since it is semi-positive definite, P3.3 can be viewed as a quadratically constrained quadratic program (QCQP) problem. It is solved using the primal-dual sub-gradient (PDSG) method.

[0171] Specifically, the augmented Lagrange problem on page 3.3 is expressed as:

[0172]

[0173] in, For Lagrange multipliers, G = [g1, g2, ..., g B ] T θ > 0 is the penalty parameter; η b (W) is represented as:

[0174]

[0175] Then, the variables (W,G) are updated iteratively according to equation (30).

[0176]

[0177] Where a≥1 is the iteration number, χ a-1 It is a very small positive number.

[0178] In equation (30), Calculated as:

[0179]

[0180] Where, Φ b (W a-1 ) is calculated as:

[0181]

[0182] It should be noted that in equation (32), it is guaranteed that Satisfaction is achieved, i.e., η b (W a-1 ) > 0. For η b (W a-1 When )≤0, Φ b (W a-1 )for:

[0183]

[0184] Equation (33) is a zero matrix of (A×B×S×V)×(A×B×S×V). Furthermore, Calculated as:

[0185]

[0186] This invention utilizes equation (30) to simultaneously optimize variables (W,G) until convergence, thereby obtaining the optimal precoding design strategy (W). * .

[0187] S302, Phase Shift Optimization

[0188] Similarly, given (Q, W, L), P2 is simplified to the phase shift optimization problem P4.1 as follows.

[0189]

[0190] Among them, R phase shift (F) is represented as:

[0191]

[0192] To facilitate the solution, this invention uses R phase shift (F) rewritten as:

[0193]

[0194] in,

[0195] Then, an auxiliary variable O = [o 1,1 ,...,o 1,V ,o 2,1 ,...,o S,V ], The optimization problem P4.1 has been rewritten as follows:

[0196]

[0197] in, It is represented as:

[0198]

[0199] Since the rewritten P4.2 satisfies the concave-convex conditions, the multi-dimensional complex quadratic transform (MCQT) technique is used to solve it. Then, similar to the method used for P3.2, the optimization variables (F, O) are processed sequentially. Specifically, given the optimization variable (F), let... The optimal solution for auxiliary variable O can be obtained.

[0200]

[0201] Based on this, Substitute into P4.2; then, this invention addresses... Expanding further, we get:

[0202]

[0203] Where, X = (F1) U×Y Substituting equation (41) into equation (39), we get:

[0204]

[0205] Then, the present invention further substitutes equation (42) into P4.2, and the objective function of P4.2 is transformed into:

[0206]

[0207] in, and Calculated as:

[0208]

[0209] and

[0210]

[0211] in, It is represented as:

[0212]

[0213] As mentioned above, P4.3 is rewritten as follows:

[0214]

[0215] To solve P4.3, this invention introduces... Let K be a Lagrange multiplier, where K = [k1, k2, ..., k U×Y ] T and define The penalty parameter is used. The augmented Lagrange problem in P4.3 is expressed as:

[0216]

[0217] in, Then, the variable (F,K) is updated iteratively according to equation (50).

[0218]

[0219] In equation (50), Calculated as:

[0220]

[0221] in, It is represented as:

[0222]

[0223] also, Calculated as:

[0224]

[0225] This invention utilizes equation (50) to simultaneously optimize variables (F,K) until convergence, thereby obtaining the optimal phase shift optimization strategy (F). * .

[0226] S303, drone deployment

[0227] Given (Q, W, L), P2 is simplified to the following drone deployment problem P5.

[0228]

[0229] In P5, each drone's position needs to be deployed within a given 3D box. Inside.

[0230]

[0231] For the box-constrained optimization problem, the projection L-BFGS method is chosen for solution. This is because the L-BFGS algorithm accelerates convergence by approximating the Hessian matrix, and the projection operation forces the obtained solution to fall within the feasible region (i.e., satisfy the box constraint). To avoid getting trapped in local optima, N uniformly distributed initial deployment strategies L are generated. (0),i :

[0232] L (0),i =(x min +τ u,i (x max -x min ),y min +τ u,i (y max -y min ),z min +τ u,i (z max -z min (56)

[0233] Among them, i=1,2,...,N,τ u,i~Uniform(0,1) represents the random sampling coefficients of the u-th UAV at the i-th starting point. Then, for each initial policy L... (0),i Perform the following steps:

[0234] First, define m as the size of the L-BFGS history window, and record the position difference ΔL from the previous m steps. j and gradient difference Δc j :

[0235] ΔL j =L (j) -L (j-1) (57)

[0236] and

[0237]

[0238] Furthermore, j = 1, 2, ..., m, defined This sets the initial search direction.

[0239] Then, calculate the current gradient (i.e., the sum of the gradients of all drones) Grad(R). tot (L (κ) ))for:

[0240]

[0241] Where κ is the iteration number, κ = 0, 1, ..., κ max -1,κ max R represents the total number of iterations. u (·) is the single-return function for the u-th UAV; Based on this, the search direction Ω is calculated using the L-BFGS formula. (κ) :

[0242] Ω (κ) =-M k Grad(R tot (L (κ) (60)

[0243] Among them, M k Given an m×m approximate Hessian inverse matrix, obtained through the first m steps of ΔL... j and Δc j Update. Then, the step size α is determined through an exact line search. κ , so that:

[0244]

[0245] in, For projection operations, ensure the position falls within the box constraints. Calculated as:

[0246]

[0247] Furthermore, the location update formula is:

[0248]

[0249] Finally, by comparing the local optimal solutions at the N starting points, the drone deployment strategy that achieves the maximum weighted sum rate is selected as the optimal drone deployment strategy (L). * :

[0250]

[0251] It is important to note that iteration should stop if any of the following conditions are met:

[0252] 1) Where τ1 is the gradient threshold;

[0253] 2)|R tot (L (κ+1) )-R tot (L (κ) )|≤τ2, where τ2 is the iteration threshold;

[0254] 3) Reaching the maximum number of iterations κ max .

[0255] S304, Complexity Analysis

[0256] The computational complexity of the proposed weighted sum rate maximization scheme mainly depends on the optimization of variables (W, F, L), which can be divided into the following three parts:

[0257] 1) In the precoding design process, the computational complexity required to update the optimization variable (W) and the auxiliary variable (U) is as follows: and Define κ Precoding The number of iterations required to achieve convergence during the precoding design process. Therefore, the complexity of the precoding design strategy is O(n).

[0258] 2) During phase shift optimization, the computational complexity required to update the optimization variable (F) and auxiliary variable (O) is as follows: and Define κ phase shift This is the number of iterations required to achieve convergence during the phase-shift optimization process. Therefore, the complexity of the phase-shift optimization strategy is O(n).

[0259] 3) During the drone deployment optimization process, the complexity of generating multiple starting points is... For each starting point, the time complexity of each iteration is O(n). The complexity of gradient calculation is O(n). The complexity of L-BFGS matrix operations is Therefore, the complexity of drone deployment strategies is...

[0260] In another embodiment of the present invention, a joint optimization system for precoding, phase shifting and deployment of air-to-ground vehicle networks is provided. This system can be used to implement the above-mentioned joint optimization method for precoding, phase shifting and deployment of air-to-ground vehicle networks. Specifically, the joint optimization system for precoding, phase shifting and deployment of air-to-ground vehicle networks includes a construction module, a joint module and an optimization module.

[0261] The construction module builds an air-to-ground vehicle-to-everything (V2X) system model based on MIMO technology and IRS, including a network model and a communication model.

[0262] The joint module, based on the constructed air-to-ground vehicle-to-everything (V2X) system model, models the weighted sum rate maximization problem as a joint optimization problem;

[0263] The optimization module employs an iterative optimization algorithm, using the Lagrange multiplier method, multidimensional complex quadratic transformation technique, and projective finite-memory quasi-Newton method to solve the joint optimization problem until convergence, thereby obtaining an optimized deployment strategy to maximize the weighted sum and rate.

[0264] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used for the operation of a joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicle networking, including:

[0265] A model of an air-to-ground vehicle-to-everything (V2X) system based on MIMO technology and IRS is constructed, including a network model and a communication model. Based on the constructed V2X system model, the weighted sum rate maximization problem is modeled as a joint optimization problem. Iterative optimization algorithms are used, including the Lagrange multiplier method, multidimensional complex quadratic transformation technique, and projective finite-memory quasi-Newton method, to solve the joint optimization problem until convergence, thereby obtaining an optimized deployment strategy to achieve weighted sum rate maximization.

[0266] Please see Figure 9 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the method for estimating the concentration of radioactive iodine species in the containment structure after an accident, as described in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the precoding, phase-shifting, and deployment joint optimization system of the air-to-ground vehicle network in this embodiment. To avoid repetition, these details are not elaborated here.

[0267] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 9 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0268] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0269] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0270] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0271] Please see Figure 10 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0272] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0273] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0274] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0275] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0276] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0277] Example 4

[0278] The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0279] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0280] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0281] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the precoding, phase shifting, and deployment joint optimization method for air-to-ground vehicle networking in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0282] A model of an air-to-ground vehicle-to-everything (V2X) system based on MIMO technology and IRS is constructed, including a network model and a communication model. Based on the constructed V2X system model, the weighted sum rate maximization problem is modeled as a joint optimization problem. Iterative optimization algorithms are used, including the Lagrange multiplier method, multidimensional complex quadratic transformation technique, and projective finite-memory quasi-Newton method, to solve the joint optimization problem until convergence, thereby obtaining an optimized deployment strategy to achieve weighted sum rate maximization.

[0283] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0284] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0285] The technical effects of the present invention will be described in detail below with reference to simulation.

[0286] This experiment simulates the precoding, phase shifting, and deployment joint optimization method of air-to-ground vehicular networks and existing mechanisms based on the same network parameters to verify the superiority of the proposed method. The specific steps are as follows:

[0287] The same network parameters are: B = [1,10], V = [5,50], U = [1,10], P b max =[1,10]W, Y={16,32,64,96}, (x min,y min ,z min )=(0,0,0)km、(x max ,y max ,z max ) = (3,3,0.3)km, A = [8,40], C = 2. Path loss PL for base station to vehicle (BS-to-Vehicle) BS The Okumura-Hata model was adopted:

[0288]

[0289] Among them, f c For the carrier frequency, d b,v Let h be the Euclidean distance between the b-th base station and the v-th vehicle. b Let be the altitude of the b-th base station. Additionally, the path loss PL for UAV-to-Vehicle interaction is... UAV The 3GPPUAV low-altitude model (TR36.777) was adopted:

[0290] PL UAV =32.4+20log 10 [f c (MHz)]+20log 10 [d u,v (km)]+η LoS (66)

[0291] Where, d u,v Let η be the Euclidean distance between the u-th drone and the v-th vehicle. LoS Additional loss for the LoS component (typically 0–3 dB, depending on the environment).

[0292] The simulation environment of this invention is built upon a combination of MATLAB and SUMO (Simulation of Urban Mobility). SUMO is used to generate dynamic vehicle trajectories, road network topology, and real-time traffic scenarios. MATLAB acquires SUMO vehicle state data in real time through the TraCI interface and integrates the proposed weighted sum rate maximization scheme to achieve communication performance evaluation. In the simulation, the processor is an Intel Core i7-12700H, the memory is 32GB DDR5, the storage is a 1TB NVMe SSD, and the operating system is Windows 11.

[0293] The existing mechanism is as follows:

[0294] a) Comparison with Scheme 1: This scheme uses multiple smart reflective surfaces to improve transmission performance and optimizes the precoding design and phase shift;

[0295] b) Comparison Scheme 2: This scheme deploys drones equipped with intelligent reflective surfaces to efficiently reflect signals from remote base stations to target vehicles, and jointly optimizes the transmission power, drone position, and reflection phase to improve channel capacity;

[0296] c) Comparison with Scheme 3: A communication framework for multi-UAV cooperative relay is proposed, and the resource allocation mechanism is optimized.

[0297] Please see Figure 5 , Figure 5 The performance of the embodiment of the present invention compared with comparative schemes 1, 2, and 3 in terms of weighted sum rate is demonstrated when the number of vehicles varies. The present invention outperforms the other comparative schemes in terms of weighted sum rate.

[0298] Specifically, compared with Scheme 1, Scheme 2, and Scheme 3, the present invention improves the weighted sum rate by 97.3%, 16.7%, and 68.3%, respectively. From Figure 5 As can be seen, when the vehicle density is low (V≤15), the rate difference between the schemes is mainly affected by the air-to-ground channel gain. The proposed scheme utilizes the high-altitude air-to-ground gain provided by the 64-element smart reflector and combines it with the dynamic coverage of multiple UAVs, resulting in a weighted sum rate that is significantly higher than Scheme 1, which relies solely on the fixed smart reflector.

[0299] Furthermore, as vehicle density increases (from V=20 to V=30), path loss and occlusion problems worsen. The proposed scheme, by optimizing the phase shift of the smart reflector, UAV deployment, and base station precoding, achieves only a slight decrease in average data rate. In contrast, Scheme 2 suffers from reduced transmission performance due to the limited coverage of a single UAV, which cannot simultaneously serve all vehicles. Moreover, at higher vehicle densities (V≥35), inter-vehicle interference and edge path loss become transmission bottlenecks. The proposed scheme, by optimizing multidimensional resources and employing MIMO diversity gain, maintains a high weighted sum rate.

[0300] Please see Figure 6 , Figure 6This paper presents a performance comparison of the weighted sum rate of the embodiments of the present invention with comparative schemes 1, 2, and 3 under different maximum base station transmit power conditions. It can be observed that as the maximum base station transmit power increases from 1W to 10W, the data rate of each scheme initially increases rapidly and then tends to decrease marginally. When the transmit power is below 3W, the data rate of all schemes is low due to weak signal coverage. As the transmit power increases to 4W, the weighted sum rate of the proposed scheme jumps to 47.6 bit / s / Hz, indicating that the power increase effectively enhances signal strength. In this case, Scheme 2, limited by a single UAV, has a weighted sum rate of only 37.8 bit / s / Hz, while Schemes 1 and 3, lacking the advantages of IRS or multi-UAV collaboration, still have lower weighted sum rates than the proposed scheme.

[0301] Please see Figure 7 , Figure 7 The impact of the number of reflective elements on the weighted sum rate of embodiments of the present invention is illustrated. It can be seen that the impact of the number of reflective elements on the weighted sum rate exhibits a significant diminishing marginal utility characteristic. When the number of reflective elements is low, the reconfigurability of the intelligent reflective surface is limited, making it difficult to effectively optimize multipath reflection. In this case, the channel quality of the reflection link is poor, and the total weighted data sum increases rapidly with the increase of reflective elements. Subsequently, as the number of reflective elements increases, the intelligent reflective surface can more finely adjust the phase and amplitude, significantly improving the channel quality of the reflection link. The total weighted data sum continues to increase. However, when the number of reflective elements exceeds a certain threshold, the reflective capability of the intelligent reflective surface approaches its theoretical upper limit. In this case, increasing the number of reflective elements is unlikely to improve channel quality, and the increase in data rate tends to plateau.

[0302] Please see Figure 8 , Figure 8 The convergence of the iterative optimization algorithm in this invention embodiment is demonstrated. From the optimization mechanism perspective, although the objective function (weighted sum rate) is at risk of local optima due to multivariate coupling (non-convexity of precoding, high-dimensional discreteness of intelligent reflector phase shift, and continuous spatial constraints of UAV deployment), its physical layer constraints (such as power limitations and non-negativity of channel gain) and the quasi-convexity of the objective function jointly ensure global convergence. Therefore, this invention can gradually approach the optimal solution through iterative optimization. It is worth noting that the optimization process converges regardless of the initial values ​​(e.g., arbitrary initialization of the number of reflective elements in the intelligent reflector). Simulation experiments show approximately 9-11 iterations, demonstrating the good convergence of the proposed scheme.

[0303] In summary, this invention presents a joint optimization method and system for precoding, phase shifting, and deployment in air-to-ground vehicular networks. Addressing the problems of high relay node deployment costs, susceptibility to communication link obstruction, and severe multipath fading in traditional terrestrial vehicular networks, it designs an air-to-ground vehicular network system architecture integrating Multiple-Input Multiple-Output (MIMO) technology and an Intelligent Reflector (IRS). A joint optimization method for precoding, phase shifting, and deployment is designed around this architecture. By integrating the IRS component onto the UAV platform, this invention not only significantly improves the flexibility and dynamic adaptability of the network architecture but also effectively utilizes the programmable nature of the IRS to intelligently control the signal propagation environment, thereby improving communication performance without increasing transmission power. Furthermore, the introduction of the joint optimization algorithm enables efficient allocation and collaborative optimization of network resources in complex scenarios with multiple users, multiple nodes, and multidimensional constraints, ultimately achieving a significant optimization effect in weighted sum rate. Compared to existing optimization methods that rely on separate precoding or node deployment, this invention achieves superior performance in weighted sum rate through deep integration of system modeling and algorithm design, providing technical support and application prospects for the next generation of intelligent vehicle networks.

[0304] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicle networking, characterized in that, Includes the following steps: Construct an air-to-ground vehicle-to-everything (V2X) system model based on MIMO technology and IRS, including a network model and a communication model; Based on the constructed air-to-ground vehicle-to-everything (V2X) system model, the weighted sum rate maximization problem is modeled as a joint optimization problem; An iterative optimization algorithm is adopted, using the Lagrange multiplier method, multidimensional complex quadratic transformation technique and projective finite memory quasi-Newton method to solve the joint optimization problem until convergence, and obtain the optimized deployment strategy to maximize the weighted sum rate.

2. The joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicle networking according to claim 1, characterized in that, The network model is as follows: Assuming all base stations are synchronized, define... d s,v Indicates the s-th Send to the vth one on the spectrum The vehicle's data symbol, in the bth... At the base station, data symbol d s,v Through the precoded vector w b,s,v Perform precoding, definition W = [W1,...,W b ,...W B ], precoded symbol g b,s Represented as For the v-th vehicle, the frequency domain channel of the base station-vehicle link on the s-th spectrum is defined as follows: The frequency domain channel of the base station-smart reflector-vehicle link on the s-th spectrum. for: in, For the frequency domain channel from the u-th smart reflector to the v-th vehicle on the s-th spectrum, Θ b,u,s For the frequency domain channel from the b-th base station to the u-th smart reflector on the s-th spectrum, F u Let be the phase shift matrix of the u-th intelligent reflector; Define the feasible set of reflection coefficients for smart reflective surfaces Frequency domain channel at the v-th vehicle 3. The joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking according to claim 1, characterized in that, The communication model is as follows: The signal y received by the v-th vehicle from the b-th base station b,s,v for: The signal y of the vth vehicle on the sth frequency spectrum s,v for: Among them, o s,v The additive white Gaussian noise at the receiving end. And j≠v, For the base station-vehicle link, the frequency domain channel is located on the s-th spectrum. for h s,v The conjugate transpose of , For the frequency domain channel from the u-th smart reflector to the v-th vehicle on the s-th spectrum, C represents the number of antennas for each vehicle, and Y represents the number of reflective elements on the smart reflective surface installed on each drone. For F u The conjugate transpose of F u Let Θ be the phase shift matrix of the u-th intelligent reflector. b,u,s For the frequency domain channel from the b-th base station to the u-th smart reflector on the s-th spectrum, w b,s,v For the precoded vector, w s,j for V represents the number of vehicles, B represents the number of base stations, and d represents the number of base stations. s,j For the data symbols transmitted to the j-th vehicle on the s-th spectrum; Data symbol d on the s-th spectrum s,v The signal-to-interference-plus-noise ratio γ at the v-th vehicle s,v Represented as: Where, σ 2 Noise power; I C It is the identity matrix. For w s,v The conjugate transpose of , w b,s,v h is the precoding vector. s,v for For the frequency domain channel at the v-th vehicle, for h s,v The conjugate transpose of w s,j for The data rate R of the v-th vehicle on the s-th spectrum s,v for: Weighted sum rate R tot for: Where, ω v Let be the speed weight of the v-th vehicle.

4. The joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicle networking according to claim 1, characterized in that, The air-to-ground vehicle-to-everything (V2X) system model based on MIMO technology and IRS includes B base stations, V vehicles, and U drones. The sets of base stations, vehicles, and drones are defined as follows: and Each base station is equipped with A antennas, and each vehicle is equipped with C antennas; Each drone is equipped with a smart reflective surface with Y reflective elements, defined as follows: The air-ground integrated vehicle-to-everything (V2X) network has S spectrums, defined as follows: Using the Cartesian coordinate system, defined at the u-th The three-dimensional coordinates of the drone equipped with a smart reflector are L u =(x u ,y y ,z u ), L={L1,...,L U } 5. The joint optimization method for precoding, phase shifting, and deployment in air-to-ground vehicle networking according to claim 1, characterized in that, The joint optimization problem involves precoding design, IRS phase modulation, and UAV deployment strategy. First, a weighted sum rate maximization problem is modeled. Then, an auxiliary variable Q is introduced, transforming the modeled optimization problem P1 into P2. The optimal solution for the auxiliary variable Q is then determined. Substitute the objective function of P2 to complete the problem transformation.

6. The joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking according to claim 5, characterized in that, The weighted sum rate maximization problem is modeled as follows: in, Let [x] be the maximum transmit power of the b-th base station; min ,x max ]、[y min ,y max ] and [z min ,z max To define the range of horizontal, longitude, and vertical positions of the UAV equipped with the intelligent reflector, constraint C1 limits the transmission power of each base station, constraint C2 is the phase amplitude constraint of the intelligent reflector, and constraint C3 is the position constraint of the UAV equipped with the intelligent reflector.

7. The joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking according to claim 5, characterized in that, The objective function for optimization problem P2 is: Among them, R tot For weighted sum rate, W, F, and L represent the precoding design strategy, phase shift optimization strategy, and UAV deployment strategy, respectively. ω v Let be the speed weight of the v-th vehicle. Let Q be the optimal solution for the auxiliary variable, V be the number of vehicles, and S be the number of spectrums. For the frequency domain channel at the v-th vehicle, for h s,v The conjugate transpose of σ 2 For noise power, I C It is an identity matrix.

8. The joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking according to claim 1, characterized in that, An iterative optimization algorithm is employed, using the Lagrange multiplier method, multidimensional complex quadratic transformation technique, and projective finite-memory quasi-Newton method to solve the joint optimization problem until convergence. Specifically: Given a UAV deployment strategy L, the precoding design strategy W and phase-shift optimization strategy F are iteratively solved using the Lagrange multiplier method. Based on the obtained precoding design strategy W and phase-shift optimization strategy F, the UAV deployment strategy L is obtained using the projective finite-memory quasi-Newton method. The above process is repeated until convergence, and the UAV deployment strategy that yields the maximum weighted sum rate is selected as the optimal UAV deployment strategy (L). * .

9. The joint optimization method for precoding, phase shifting, and deployment of air-to-ground vehicle networking according to claim 8, characterized in that, Optimal Drone Deployment Strategy (L) * for: Among them, R tot For weighted sum rate; Iteration stops when any of the following conditions are met: 1) Where τ1 is the gradient threshold; 2)|R tot (L (κ+1) )-R tot (L (κ) )|≤τ2, where τ2 is the iteration threshold; 3) Reaching the maximum number of iterations κ max .

10. A joint optimization system for precoding, phase shifting, and deployment in air-to-ground vehicle networking, characterized in that, include: The module constructs a model of an air-to-ground vehicle-to-everything (V2X) system based on MIMO technology and IRS, including a network model and a communication model. The joint module, based on the constructed air-to-ground vehicle-to-everything (V2X) system model, models the weighted sum rate maximization problem as a joint optimization problem; The optimization module employs an iterative optimization algorithm, using the Lagrange multiplier method, multidimensional complex quadratic transformation technique, and projective finite-memory quasi-Newton method to solve the joint optimization problem until convergence, thereby obtaining an optimized deployment strategy to maximize the weighted sum and rate.

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