An adjustable antenna-assisted dynamic beamforming optimization method for internet of vehicles

Abstract

This invention discloses an adjustable antenna-assisted dynamic beamforming optimization method for vehicular networks, belonging to the field of vehicular network and mobile communication technology. By establishing a comprehensive near-field multipath channel model incorporating scattering cluster distribution and geometric phase shift characteristics, this invention utilizes the position reconfigurability of a fluidic antenna system to achieve dynamic port switching within the limited space of the vehicular network terminal. This transforms multipath interference in near-field rich scattering environments into spatial diversity gain, solving the scientific problem that fixed ports cannot adapt to complex channel characteristics. Simultaneously, this invention proposes a low-complexity joint optimization algorithm based on the maximum transmission ratio and maximum signal-to-noise ratio criteria. Through closed-form solutions, it rapidly achieves the joint design of base station beamforming and terminal ports, significantly improving channel capacity without requiring only a single radio frequency link or increasing hardware costs. This effectively overcomes the stringent economic constraints of large-scale commercialization on hardware costs and computational latency.

Description

Technical Field

[0001] This invention relates to the fields of vehicle networking and mobile communication technology, specifically to a dynamic beamforming optimization method for vehicle networking assisted by an adjustable antenna. Background Technology

[0002] In recent years, with the rapid development of wireless communication systems, higher demands have been placed on the efficiency of radio spectrum resource utilization. Especially in vehicle-to-everything (V2X) communication scenarios, multi-antenna systems have become a key solution for improving communication performance through spatial diversity. Traditional base station antenna arrays are typically configured with fixed positions, while emerging fluid antenna systems allow the use of reconfigurable ports that can dynamically adjust their positions based on real-time channel conditions, thereby significantly improving channel capacity through spatial diversity.

[0003] Faced with the complex and ever-changing communication environment of the Internet of Vehicles, current technological development presents a dual demand:

[0004] (1) Scientific observation dimension: With the increase in communication frequency band and antenna aperture, vehicle-to-everything (V2X) communication is gradually entering the "near field" region. In this dimension, it is urgent to accurately capture and utilize the multipath propagation characteristics and geometric phase shift features in near field communication scenarios. In order to maximize the utilization of spatial degrees of freedom, it is necessary to deeply observe and model the nonlinear relationship between scatterer distribution, V2X terminal movement trajectory and dynamic channel to achieve accurate channel state perception;

[0005] (2) Economic Application Dimension: Vehicle-to-everything (V2X) applications have extremely high data throughput requirements. From an economic perspective, it is necessary to improve spectrum efficiency and system capacity through dynamic port switching technology without significantly increasing the number of radio frequency links and hardware costs. At the same time, the algorithm is required to have low complexity to adapt to the low power consumption and real-time requirements of V2X terminals.

[0006] However, existing vehicle-to-everything (V2X) antenna optimization technologies face a dual challenge:

[0007] (1) Scientific level: In near-field communication scenarios, the traditional plane wave assumption is no longer applicable, and multipath propagation and geometric phase shift become extremely significant, making channel modeling and analysis exceptionally complex. Existing research focuses on the optimization of fixed-port systems, failing to fully explore the spatial diversity potential of dynamic position ports in scattering-rich environments, and making it difficult to achieve real-time joint optimization of base station beamforming and terminal port selection in fast-moving vehicle environments;

[0008] (2) Economic aspects: Traditional fixed-port baseline systems have limited channel capacity improvement when port spacing increases, which cannot effectively cope with the increasing demand for communication traffic, resulting in low return on spectrum investment. In addition, if complex global search algorithms are used for port selection and beam optimization, it will bring huge computational overhead and processing latency, which is unacceptable in the large-scale commercial deployment of vehicle-to-everything (V2X) networks where economic costs are sensitive and latency requirements are stringent.

[0009] The above problems urgently need to be solved. To address this, a dynamic beamforming optimization method for vehicle-to-everything (V2X) networks with adjustable antenna assistance is proposed. Summary of the Invention

[0010] The technical problem to be solved by this invention is: how to solve the inherent limitation of fixed-port antennas in the prior art that are difficult to adapt to dynamic and complex channel characteristics at the physical level, and to overcome the contradiction between high hardware costs and severe computational delays in large-scale commercial use, and to provide an adjustable antenna-assisted dynamic beamforming optimization method for vehicle networking.

[0011] The present invention solves the above-mentioned technical problems through the following technical solution, and the present invention includes the following steps:

[0012] S1: Construct a near-field multipath channel model for vehicle-to-everything (V2X) networks that incorporates scattering cluster distribution characteristics and geometric phase shift properties;

[0013] S2: Combining the distribution characteristics of scattering clusters and geometric phase shift characteristics, and considering the maximum transmit power constraint of the base station, a joint optimization objective of the base station transmit beamforming vector and the port selection of the vehicular network terminal fluid antenna system is constructed with the goal of maximizing instantaneous channel capacity, and the signal-to-noise ratio function of the vehicular network terminal port is defined.

[0014] S3: Solve the joint optimization objective under power constraints, calculate the optimal transmit beamforming vector for each port based on the maximum transmission ratio criterion, and traverse and filter the optimal ports according to the maximum signal-to-noise ratio criterion to obtain the optimal transmit beamforming vector and optimal port index that are suitable for the current near-field scattering environment.

[0015] S4: Based on the optimal transmit beamforming vector and the optimal port index, the base station configures the actual transmit beamforming vector as the optimal transmit beamforming vector and sends the signal. At the same time, the vehicle-to-everything (V2X) terminal activates the port corresponding to the optimal port index to receive the signal, thereby achieving the maximum transmission rate under the current channel environment and completing the dynamic beamforming optimization of the V2X.

[0016] Furthermore, in step S1, the specific processing procedure is as follows:

[0017] S11: Construct a three-dimensional spatial transmission distance function expression between the base station transmitting antenna, the scatterer, and the terminal user's fluid antenna system port;

[0018] S12: Construct the complex impulse response function expression for the signal propagating through the scattering cluster to each port, including the path phase, the geometric phase of the transmitting array, and the geometric phase of the receiving port.

[0019] Furthermore, in step S11, the construction of the three-dimensional spatial transmission distance function system is based on a system geometric topology model, which is defined as follows:

[0020] The communication scenario is set up to include fixed base stations and mobile vehicle-to-everything (V2X) terminals. On the base station side, a configuration including... A uniform linear array of transmitting antennas, i.e., ULA, in a global three-dimensional Cartesian coordinate system, is the... The location of the root transmitting antenna is defined as ,in The range of values ​​is to Each transmitting antenna is spaced at a fixed interval. Arrangement; On the user side, the vehicle-to-everything (V2X) terminal is equipped with a fluid antenna system, namely FAS, which includes... A set of preset, fixed-position discrete candidate ports, the first... The location coordinates of each port Through formula Calculate, where, For time The changing attitude rotation matrix The center location of FAS For the first The inherent position offset vector of each port relative to the geometric center of FAS The value range is 0 to -1, in subscripts and superscripts Indicates the first An index identifier for each vehicle-to-everything (V2X) terminal user is used to distinguish different V2X terminal users.

[0021] Furthermore, in step S11, the specific processing procedure is as follows:

[0022] S111: For direct coupling, base station antenna With port The instantaneous distance between them is:

[0023] ;

[0024] Assume it exists Distinguished rays, grouped into clusters, using Cluster The Path, time setting The relevant scattering or reflecting point is Then from the base station antenna The distance to the scatterer is:

[0025] ;

[0026] From scatterer to port The distance is:

[0027] ;

[0028] in, Let represent the three-dimensional spatial coordinates of the scatterer corresponding to the i-th propagation path in the l-th scattering cluster at time t;

[0029] S112: The total path length of the multipath components is calculated as follows:

[0030] ;

[0031] S113: The expression for the three-dimensional spatial transmission distance function is then obtained as follows:

[0032] ;

[0033] in, Let t represent the three-dimensional spatial coordinates of the equivalent scattering center of the l-th scattering cluster at time t.

[0034] Furthermore, in step S12, the complex impulse response function is expressed as follows:

[0035] ;

[0036] in, A set representing a cluster; It is a random phase shift that reflects the randomness of scattering; and These are the segmented distances from the transmitter to the scatterer and from the scatterer to the receiver, respectively. Indicates the first The distance of each transmitting antenna element from the center point of the ULA; Used to define the direction of ULA, namely the azimuth and elevation angles; and These are the base station's departure azimuth and departure elevation angles, which change over time. It is the FAS center to the first The distance between ports, Indicates the port spacing; Used to define the direction of FAS; and These are the azimuth and elevation angles at the receiver.

[0037] Furthermore, in step S2, the specific processing procedure is as follows:

[0038] S21: The base station uses single-stream beamforming for downlink transmission. The base station transmits within one symbol period... The dimensional signal vector is:

[0039] ;

[0040] in, To shape the transmitted beam vector, The information symbol represents the unit power normalization condition and satisfies the power constraint. , That is the maximum transmission power;

[0041] S22: When FAS selects the... When receiving data through a port, the distance from the base station to that port... The channel vector is denoted as , No. The complex baseband received signal at each port is represented as follows:

[0042] ;

[0043] Among them, superscript This indicates the conjugate transpose. It is the equivalent complex channel gain. It is additive noise;

[0044] S23: Definition of the The instantaneous received signal-to-noise ratio function for each port is:

[0045] ;

[0046] in, Indicates noise power.

[0047] Furthermore, step S2 also includes the following steps:

[0048] S24: The original form of the joint optimization objective is defined as follows:

[0049] ;

[0050] in, It is the first The channel capacity of each port; since the logarithmic function is monotonically increasing, the above joint optimization objective is equivalent to maximizing the received signal-to-noise ratio. The updated joint optimization objective is as follows:

[0051] .

[0052] Furthermore, in step S3, based on the maximum ratio transmission criterion, given the first... The closed-form formula for calculating the optimal beamforming vector for each port is:

[0053] ;

[0054] in, This represents the maximum transmit power constraint of the base station. Indicates port The relevant real base station-to-port channel vectors, Let be the Euclidean norm of the channel vector;

[0055] Will Substituting into the signal-to-noise ratio function, we get the first... Maximum closed-loop signal-to-noise ratio of each port for:

[0056] .

[0057] Furthermore, in step S3, the selection rule for the optimal receiving port is: traverse all... The maximum instantaneous signal-to-noise ratio calculated for each port. Select the port index that provides the highest signal-to-noise ratio as the optimal port. :

[0058] .

[0059] Furthermore, in step S4, the optimal port of the base station is determined. The optimal beamforming vector configuration is as follows Based on the optimal port and optimal beamforming vector The maximum signal-to-noise ratio and maximum instantaneous channel capacity under the optimal configuration are calculated as follows:

[0060] ;

[0061] in, For maximum signal-to-noise ratio, Maximum instantaneous channel capacity.

[0062] Compared with existing technologies, this invention has the following advantages: The adjustable antenna-assisted dynamic beamforming optimization method for vehicular networks, by constructing a comprehensive near-field multipath channel model that includes scattering cluster distribution and geometric phase shift characteristics, adapts to the complex and variable near-field rich scattering environment of vehicular networks, achieving joint dynamic optimization of base station beamforming and vehicular network terminal port selection; it can effectively utilize the position reconstruction characteristics of fluid antennas to transform significant multipath fading in near-field communication into spatial diversity gain, revealing the positive correlation between normalized port spacing, scattering richness, and system channel capacity; it possesses low-complexity real-time processing capabilities, avoiding the complex iterative solutions of traditional high-dimensional non-convex optimization problems through a closed-form solution algorithm based on MRT (maximum transmission ratio criterion) and SNR (maximum signal-to-noise ratio); it achieves significant performance improvements with only a single radio frequency link, reducing hardware costs and the data processing burden at the receiver, and meeting the actual needs of vehicular network applications for high throughput, low latency, and low energy consumption. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the adjustable antenna-assisted dynamic beamforming model for vehicle-to-everything (V2X) proposed in this embodiment of the invention.

[0064] Figure 2 This is a schematic diagram comparing the channel capacity changes of FAS with port selection and fixed port baseline in an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram comparing the capacity of FAS and fixed port baseline under different scattering environments in an embodiment of the present invention;

[0066] Figure 4 This is a schematic diagram illustrating the variation of modeling error with antenna spacing under different sub-port configurations in this embodiment of the invention;

[0067] Figure 5 This is a schematic diagram of the channel capacity of different FAS configurations and traditional ULA under different signal-to-noise ratios in the embodiments of the present invention. Detailed Implementation

[0068] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0069] Example 1

[0070] This embodiment provides a technical solution: a tunable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks, comprising the following steps:

[0071] In a pre-built near-field multipath channel model for vehicle-to-everything (V2X) networks, the distribution characteristics of scattering clusters, path propagation phase shift, and geometric phase shift characteristics caused by the geometric positions of the transmitter and receiver are analyzed in the near-field environment.

[0072] In the pre-constructed near-field multipath channel model of the Internet of Vehicles, the distribution characteristics of the scattering clusters and the geometric phase shift characteristics are combined, and the maximum transmit power constraint of the base station is considered. A joint optimization objective of the base station beamforming vector and the selection of the vehicle terminal flow antenna port is constructed with the goal of maximizing the instantaneous channel capacity. The signal-to-noise ratio function of the vehicle terminal port is defined.

[0073] Under the power constraint, the joint optimization objective is solved, the optimal beamforming vector corresponding to each candidate port is calculated based on the maximum transmission ratio criterion, and the optimal receiving port is screened by traversing according to the maximum signal-to-noise ratio criterion to obtain the optimal beamforming vector and optimal port index parameters that are suitable for the current near-field rich scattering environment.

[0074] The method for constructing the near-field multipath channel model of the vehicle-to-everything (V2X) network includes: considering that the on-board fluid antenna port is dynamically reconfigurable, constructing a three-dimensional spatial transmission distance function expression between the base station transmitting antenna, the scatterer and the on-board fluid antenna receiving port; and constructing a channel complex impulse response function expression that includes the path phase, the geometric phase of the transmitting array and the geometric phase of the receiving port as the signal propagates through the scattering cluster to each receiving port.

[0075] More specifically, the base station's first The location of each transmitting antenna can be precisely described. This system architecture embodies the organic combination of a static base station antenna array and a dynamic user-end flowing antenna: the base station utilizes a ULA to achieve spatial signal processing at the transmitting end, while the user introduces flexible spatial selection freedom at the receiving end through a FAS. The two work together to address the complex multipath, congestion, and time-varying characteristics of the wireless channel, providing a new system paradigm for improving near-field and far-field communication performance. Under this configuration, the base station's... The transmitting antennas are located at:

[0076] ;

[0077] And users at time At that time, its fluid antenna system FAS was the first The port locations are:

[0078] ;

[0079] in, The center location of FAS Let its direction matrix be... For port Offset vector relative to the center. For direct coupling, antenna With port The instantaneous distance between them is:

[0080] ;

[0081] To model multipath propagation via scattering or reflection, it is assumed that there exists These are distinguishable rays, which may group into clusters. (Using...) Cluster The Path, time setting The relevant scattering or reflecting point is So, from the base station antenna... The distance to the scatterer is:

[0082] ;

[0083] From the scatterer to the FAS receiving port The distance is:

[0084] ;

[0085] Therefore, the total path length of this multipath component is:

[0086] ;

[0087] Corresponding to the previous formula:

[0088] ;

[0089] Finally, base station antenna With FAS port The complex baseband channel impulse response can be written as:

[0090]

[0091] in, Represents a set of clusters. It is a random phase shift that reflects the randomness of scattering. and These are the segmented distances from the transmitter to the scatterer and from the scatterer to the receiver, respectively. Indicates the first The distance of each transmit antenna element from the midpoint of the ULA, where The distance between array elements. The direction of ULA is defined, namely the azimuth and elevation angles. and These are the base station's departure azimuth and departure elevation angles, which change over time. It is the FAS center to the first The distance between the ports, of which Indicates the port spacing. The direction of FAS is defined. and These are the azimuth and elevation angles at the receiver. The subsequent exponential factors explain the propagation phase over the path length, the phase shift caused by the transmitter array geometry, and the phase shift caused by the receiver port geometry, thus forming a comprehensive multipath channel model.

[0092] More specifically, consider a base station using single-stream beamforming for downlink transmission. Assume the base station is equipped with... There are 1 transmitting antenna, and the transmitting beamforming vector is represented as follows: Information symbols are Satisfying the normalization condition for unity power And the total transmit power of the base station is constrained to be ,in That is the maximum transmit power. Therefore, the base station transmits [power] within one symbol period. A 3D signal vector can be written as:

[0093] ;

[0094] When FAS selects the first When receiving data through a port, the distance from the base station to that port... The channel vector is denoted as Because the receiver has only one RF link that linearly combines signals from all antennas, the first... The complex baseband received signal at each port can be represented as:

[0095] ;

[0096] Among them, superscript This indicates the conjugate transpose. It is the equivalent complex channel gain. It is additive noise.

[0097] The noise is typically assumed to be zero-mean complex white Gaussian noise. ,in It is noise power. Therefore, the first... The instantaneous received signal-to-noise ratio of each port is:

[0098] ;

[0099] Subsequent port selection and performance analysis will be based on the above received signal model.

[0100] We consider a single-user downlink scenario in the near-field area, where equipped with A base station with one antenna serves a device equipped with... The data receiver for the FAS on the candidate port. For each FA port, the channel vector from the base station to the port is represented as follows:

[0101] ;

[0102] in, We collect all such channel vectors into a matrix:

[0103] ;

[0104] Base stations utilize single-stream beamforming. Assume... To shape the transmitted beam vector, It is an information symbol and satisfies Under the total transmit power constraint, we have:

[0105] ;

[0106] port The channel capacity is given by the following formula:

[0107] ;

[0108] in, It is the first The instantaneous SNR of each port. Let... This is the index of the selected port. Our goal is to jointly design the beamforming vector. and select port index To maximize the instantaneous channel capacity, the optimization problem is therefore formalized as:

[0109] ;

[0110] because exist Since time is strictly monotonically increasing, this problem is equivalent to:

[0111] ;

[0112] In addition to communication performance, we introduce weighted normalized absolute error. It is a key technical metric used to measure the accuracy of a model, especially when simplifying complex channel models. It is used to quantify the deviation between the simplified model and the real channel, and is therefore defined as:

[0113] ;

[0114] in, Indicates base station antenna With FA port The true channel coefficients between, and These represent the simplification or approximation coefficients used in the design.

[0115] Then we define a composite utility:

[0116] ;

[0117] Or equivalently, consider in Maximizing channel capacity under constraints:

[0118] ;

[0119] because It is about Monotonically increasing, maximizing effective channel capacity This is equivalent to maximizing the received SNR. For the... For each FA port, the received SNR can be expressed as:

[0120] ;

[0121] in Indicates port The relevant real base station to port channel vector.

[0122] For a given port index The beamforming design problem can be simplified to:

[0123] ;

[0124] According to the Cauchy-Schwarz inequality:

[0125] ;

[0126] If and only if and The equality holds when the lines are collinear. Therefore, the ports... The optimal beamformer is:

[0127] ;

[0128] This corresponds to MRT. (Will) Substitute return Port available The corresponding maximum SNR:

[0129] ;

[0130] Due to each port Generate a maximum SNR The optimal port selection rule is to activate the port that provides the highest SNR, or equivalently, the port with the highest channel norm:

[0131] ;

[0132] Therefore, the corresponding optimal beamformer is:

[0133] ;

[0134] Therefore, the maximum SNR and channel capacity obtained are:

[0135] .

[0136] Therefore, under feasibility conditions The problem under consideration achieves the above closed-form optimal solution.

[0137] Example 2

[0138] This embodiment provides a technical solution: a tunable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks, comprising the following steps:

[0139] Step 1: First, a system geometric topology model suitable for the near-field communication environment of vehicle-to-everything (V2X) needs to be constructed. This model defines in detail the spatial physical relationship between the transmitter and receiver in the downlink, providing basic coordinate data for subsequent channel characteristic analysis. The communication scenario is set to consist of a fixed base station (BS) and a mobile V2X terminal user.

[0140] The specific steps are as follows:

[0141] Step 101: In order to achieve directional signal transmission and obtain spatial multiplexing gain, a set of... A uniform linear array (ULA) of transmitting antennas. We establish a global three-dimensional Cartesian coordinate system, in which the nth antenna in the array... The location of the root transmitting antenna is precisely defined ,in The range of values ​​is to These antenna elements are physically spaced at a fixed interval. The spacing parameter is crucial for calculating the near-field geometric phase shift at the transmitter.

[0142] Step 102: The vehicle-to-everything (V2X) terminal is equipped with an advanced fluid antenna system. This system breaks through the positional limitations of traditional fixed antennas and can provide [unclear meaning] within a limited physical space aperture. Each terminal device has a preset, fixed-position discrete candidate port. The terminal device possesses dynamic reconfiguration capabilities, allowing it to be reconfigured at any time. Through electronic or mechanical switching methods, from this One of the candidate ports is selected and activated for signal reception, thereby utilizing minute spatial differences to obtain diversity gain. Considering that vehicles undergo not only translational shifts but also attitude changes during actual driving, this step introduces rigid body kinematics modeling to accurately calculate the instantaneous coordinates of the port. Location coordinates of each candidate port The calculation is as follows:

[0143] ;

[0144] in, It is a time-varying attitude rotation matrix that not only contains information about the vehicle's direction of travel, but also characterizes changes in pitch, roll, and yaw angles caused by road undulations, turning, or lane changes, thus accurately reflecting the dynamic fluctuations in the receiver port's position with the vehicle's attitude; and Then it means the first Each port has an inherent position offset vector relative to the geometric center of the FAS. This vector is determined by the physical dimensions of the fluid antenna and the port distribution density, and remains relatively stationary during vehicle movement. Through the above modeling, this step achieves an accurate mapping from local array coordinates to global spatial coordinates.

[0145] Step 2: After completing the geometric topology construction of the communication system, this embodiment further establishes a channel model that can accurately reflect the complex electromagnetic environment of the vehicle-to-everything (V2X) network. Considering that V2X communication scenarios are typically located in densely scattering urban canyons or main traffic arteries, and the communication distance is often within the near-field range defined by Rayleigh distance, traditional plane wave models are difficult to accurately characterize the phase changes of the signal. Therefore, this step constructs a complex baseband channel impulse response model that comprehensively considers near-field effects, multipath scattering, and the geometric architecture of the transceiver. Specifically, it is assumed that there is a distance between the base station and the end user... There are 1 main scattering clusters, each containing 12 main scattering clusters. A specific propagation path. For the base station... The root transmitting antenna reaches the user FAS. Each signal path at each receiving port is affected not only by free-space path loss, but also by random phase shifts caused by the scatterer material and phase rotation caused by microscopic geometric differences in the signal wavelength scale. The channel coefficient used in this invention... The calculation formula is a composite function with multiple superpositions. This formula first uses a double summation operation to traverse all scattering clusters and their internal sub-paths, calculating the signal along the . In the scattering cluster, the th Physical distance of propagation along a path In terms of phase calculation, the formula integrates four key exponential terms: the first term characterizes the random phase caused by the scatterer. and the distance from the transmitter to the scatterer and distance from the scatterer to the receiver The propagation delay phase is determined; the second and third terms characterize the array geometry of the base station transmitter, by introducing the transmit antenna spacing. , departure azimuth angle and leaving the angle of elevation The first term precisely describes the spatial phase difference when the signal is emitted from different array elements of the ULA; the fourth and fifth terms characterize the geometric features of the fluid antenna at the user receiver, by introducing the port spacing. 1. Azimuth of arrival (AoA) and the angle of elevation This precisely describes the phase difference when the signal arrives at different spatial ports of the FAS. Ultimately, it will be used to... root antenna to user Aggregate all channel coefficients of each port to construct 3D channel state information vector This vector completely captures the time at time . , No. The spatial channel characteristics of each port provide a precise mathematical basis for subsequent port selection and beamforming.

[0146] Step 3: After establishing channel state information that accurately reflects near-field effects and multipath characteristics, this embodiment further constructs a complete physical layer signal transmission and reception model. This model covers the entire process from precoding processing at the base station transmitter, transmission via the spatial channel, to reception and noise processing at the user terminal's streaming antenna port, aiming to quantify the communication quality of the system under a specific port configuration.

[0147] Step 301: Assume that the base station uses single-stream beamforming technology for the single-user downlink to reduce hardware complexity and pilot overhead. At this time, the base station antenna array transmits... Complex baseband signal vector It can be mathematically modeled as follows:

[0148] ;

[0149] in, The transmit beamforming vector, representing a base station, physically aims to precisely adjust the transmit amplitude and phase weights of each element in the antenna array, causing constructive interference of the emitted electromagnetic wave energy in space, thereby focusing the energy towards a specific target location. To comply with radio management regulations and hardware power consumption limits, the transmit beamforming vector must satisfy a total transmit power constraint, i.e. ,in, This represents the maximum allowable transmit power for the base station. This represents a complex modulation symbol carrying actual data information. For ease of subsequent power calculations and normalization analysis, it is typically assumed that this symbol satisfies the unity power normalization condition, i.e., the statistical expectation. .

[0150] Step 302: When the vehicle-to-everything (V2X) terminal dynamically selects and activates the first antenna system via an electronic switch according to the switching strategy of the fluidic antenna system... When there are multiple candidate ports, the antenna element at that specific spatial location is actually connected to a single radio frequency receiving link. At this point, the scalar received signal, after undergoing complex near-field channel fading and propagation delay... It can be represented as:

[0151] ;

[0152] in, This represents the useful signal received. This constitutes the equivalent scalar channel gain of the link. This term reflects the base station transmit beam. With the current port channel vector The degree of spatial matching between them. The gain is greatest when the two are highly aligned in orientation. This represents additive interference introduced by receiver thermal noise, electronic noise, etc. In this model, it is assumed that this interference term follows a zero mean and a variance of . The distribution of complex Gaussian white noise, i.e. .

[0153] Step 303: Based on the above signal model, this step finally defines the first... Instantaneous received signal-to-noise ratio of each port As a core physical metric for measuring the current link quality, this metric is defined as the ratio of received useful signal power to noise power, and its closed-form mathematical expression is:

[0154] ;

[0155] This expression establishes a direct mapping from physical layer signal characteristics to system performance. Signal-to-noise ratio (SNR) The signal-to-noise ratio (SNR) directly determines the level of modulation and coding strategies that the system can adopt: the higher the SNR, the higher the modulation order that the system can support, thus achieving a higher data transmission rate. Therefore, maximizing this SNR is the mathematical foundation for subsequently constructing a joint optimization problem to maximize channel capacity.

[0156] Step 4: After constructing the above signal transmission model and signal-to-noise ratio expression, the fourth step of this embodiment aims to find the optimal operating state of the system through mathematical modeling to achieve the ultimate improvement in communication performance. Specifically, considering the stringent requirements of vehicle-to-everything (V2X) services for high data transmission rates, this invention aims to maximize the instantaneous channel capacity of the downlink. As the core optimization objective, the system needs to simultaneously adjust the transmission strategy on the base station side and the reception strategy on the user side. Therefore, we will use the continuous variable on the base station side—the transmit beamforming vector—as the core optimization objective. The discrete variable on the user side—the receiver port index They are incorporated into the same joint optimization framework. Based on Shannon information theory, the constructed mathematical optimization problem is expressed as:

[0157] ;

[0158] When solving this objective function, physical and hardware constraints must be strictly observed: First, the constraints... The linear operating range of the base station RF front-end amplifier and the power limits for radio management are described, namely, the square of the L2 norm of the transmit beamforming vector must not exceed the maximum rated power set by the system. Secondly, constraints The physical switching characteristics of a fluid antenna system are described, namely, at any given instant, the receiver can only switch from a preset state. The goal is to select and activate a single port from a finite set of candidate ports for data reception. It's worth noting that the original optimization problem described above is a typical mixed-integer nonlinear programming problem, and its computational complexity is extremely high if solved directly. However, by analyzing the mathematical properties of the objective function, we can see that the logarithmic function... In the domain The function is strictly monotonically increasing. This means that the instantaneous channel capacity... The changing trend and its internal independent variable—namely, the instantaneous received signal-to-noise ratio. The changing trends are completely consistent. In other words, for the channel capacity to reach its global maximum, the necessary and sufficient condition is that the received signal-to-noise ratio (SNR) reaches its maximum. Based on this monotonicity principle, this invention mathematically transforms the originally complex capacity maximization problem into a strictly equivalent problem of maximizing the received SNR, that is... This crucial equivalent transformation step not only preserves the physical properties of the optimal solution but also avoids the non-convexity caused by logarithmic operations, significantly simplifying the solution difficulty of subsequent optimization algorithms and providing solid theoretical support for achieving low-latency real-time computing in rapidly changing vehicle-to-everything (V2X) channels.

[0159] Step 5: After transforming the original problem into a mathematical form that maximizes the received signal-to-noise ratio, this embodiment adopts a step-by-step solution strategy. First, for any candidate port in the vehicular network terminal's flowing antenna system that is assumed to be in an active state... (in This involves determining the optimal transmission parameters for a specific spatial location. Specifically, the base station needs to determine an optimal transmit beamforming vector. This allows the useful signal reception strength at that port to reach the theoretical upper limit. To achieve this goal, the present invention utilizes the Cauchy-Schwarz inequality principle in linear algebra to adjust the signal-to-noise ratio numerator. Analysis reveals that, according to this inequality, the magnitude of the inner product of two vectors reaches its maximum if and only if the two vectors are linearly dependent. Therefore, to obtain maximum gain, the direction of the transmit beamforming vector must be aligned with the current channel vector. The conjugate transpose of the signal must be kept strictly consistent; this strategy is known as the maximum transmission ratio criterion (MRT) in wireless communication. Physically, the MRT criterion means that the base station pre-compensates for amplitude and phase variations caused by the channel through precoding: allocating more transmit power to paths with high channel gain, while simultaneously rotating the phase lag introduced by the channel in the opposite direction. This ensures that signal components from different antenna elements of the base station can be superimposed in phase when they reach the user's receiving port, i.e., generating constructive interference and maximizing the energy of the synthesized wave. Based on this, to fully utilize the base station's hardware capabilities and further improve the signal-to-noise ratio, the transmit power must reach the system's maximum allowable limit. Therefore, for a given port Its corresponding optimal beamforming vector The construction process is as follows: First, extract the channel vector. The direction information, that is, calculating its unit direction vector. Then extend its amplitude to the square root of the maximum transmit power. Based on the above derivation, the closed-form formula for calculating the optimal beamforming vector is:

[0160] ;

[0161] in, This represents the maximum transmit power constraint of the base station. This refers to the actual base station-to-port channel vector obtained in step 2 based on the near-field scattering model. Let be the Euclidean norm of the channel vector. This step directly identifies the potential optimal performance of each port through analytical solutions, avoiding complex iterative searches and providing a standardized benchmark for subsequent port selection.

[0162] Step 6: In step 5, for the first The candidate ports established the optimal transmit beamforming vector that meets the maximum ratio transmission criterion. This step in this embodiment aims to quantify the theoretical upper bound of the port's performance under ideal beam alignment, thereby providing a quantifiable scalar basis for subsequent port selection. Specifically, the system substitutes the derived optimal beamforming vector expression into the original instantaneous signal-to-noise ratio definition. The calculation is performed within this process. During the substitution, the modulus squared term of the numerator is expanded as follows: Using the relationship between the dot product and norm of vectors in linear algebra This formula can be further simplified. Through mathematical derivation and simplification, the final result is obtained. Closed-form maximum signal-to-noise ratio expression for each port This expression has significant physical and engineering value: First, it reveals the relationship between the maximum signal-to-noise ratio at the receiver and the maximum transmit power at the base station under the maximum ratio transmission strategy. Proportional to the noise power at the receiving end It is inversely proportional, and most importantly, it is directly proportional to the square of the Euclidean norm of the channel vector. This means that for any spatial location in a fluid antenna system, its potential communication quality depends entirely on the total energy of the channel impulse response at that location. This conclusion successfully decouples the originally complex beamforming and port selection problem into a simple channel energy comparison problem, greatly simplifying the system's computational logic. It allows the system to predict its performance without actually performing beamforming on each port, laying the theoretical foundation for achieving low-latency, fast port switching.

[0163] Step 7: After completing the theoretical calculation of the potential performance upper bound for each independent candidate port, this embodiment enters the final decision-making and configuration stage. The aim is to maximize the downlink performance of the entire vehicle-to-everything (V2X) communication system by exploring the spatial diversity potential of the fluid antenna system through a global optimization strategy. Specifically, the system's central processing unit or baseband processing unit will execute a traversal search algorithm for all... The maximum instantaneous signal-to-noise ratio of each candidate port calculated in step 6. Numerical comparisons are performed. The mathematical goal of this process is to find a specific port index. This ensures that the signal-to-noise ratio (SNR) of that port reaches its maximum value across all possible sets. It is worth noting that in actual engineering implementation and algorithm computation, based on the aforementioned derivation formula... It can be known that the maximum transmission power of the base station Noise power at the receiver This is a constant factor for all ports. Based on this mathematical property, the present invention cleverly incorporates the signal-to-noise ratio... The comparison problem is equivalently simplified to the square of the Euclidean norm of the channel vector. Direct comparison. This simplification greatly reduces the computational complexity of the algorithm, avoiding unnecessary multiplication and division operations in each comparison. Therefore, the optimal activation port... The final formula for determining it is expressed as follows: Once the optimal index is determined, the vehicular network terminal's fluid antenna controller will immediately issue a command to physically switch the radio frequency link to the next index via an electronic switch. The location of each port, and the base station simultaneously calls the corresponding optimal beamforming vector. The transmission is initiated. This step achieves the following physical effects: the system actively avoids spatial locations in the deep valleys of multipath fading and accurately locks onto the peak position of the strongest signal energy. Thus, significant spatial diversity gain is obtained through millisecond-level dynamic switching of spatial location, ensuring the robustness and high throughput of the communication link in high-speed mobile scenarios of vehicle-to-everything (V2X) networks.

[0164] Step 8: Use a global traversal algorithm to determine the optimal candidate port index that provides the best channel quality. Next, this embodiment enters the final physical link configuration and data transmission stage, aiming to translate the theoretical computational gain into an actual increase in communication speed. Specifically, the communication system performs dual-end cooperative configuration: at the base station transmitter, the baseband processing unit will configure the corresponding port... Optimal beamforming vector Loaded to the RF front end, the parameters of the phase shifters and attenuators in the antenna array are adjusted to precisely synthesize the emitted electromagnetic wavefront in space, focusing the energy at the selected receiving position of the vehicle-to-everything (V2X) terminal. Simultaneously, at the onboard receiver, the controller of the fluidic antenna system issues commands to drive the microelectromechanical system switches or PIN diode circuits, physically connecting the RF receiving link to the first... within microseconds. Each port is activated to perform signal acquisition and processing. Under this optimal configuration, the system achieves the best spatial matching between the transmitting and receiving ends, which maximizes the instantaneous channel capacity. This reaches the theoretical upper limit under the current environment. Based on Shannon's theorem and the aforementioned derivation, the mathematical closed-form solution for this maximum capacity is:

[0165] ;

[0166] This formula profoundly reveals the technical essence of this invention: the final performance of the system is no longer limited by random channel fading at a fixed location, but depends on all... The port with the best channel condition among the candidate ports. Through this mechanism, the present invention fully utilizes the additional spatial degrees of freedom provided by the fluid antenna while maintaining extremely low computational complexity, transforming the dense multipath effects in near-field communication into beneficial spatial diversity gain, effectively overcoming the deep fading problem in high-speed mobile scenarios of vehicle-to-everything (V2X) communication, and significantly improving downlink spectral efficiency and data transmission reliability.

[0167] Figure 2 This demonstrates the channel capacity as a function of normalized port spacing under two configurations: FAS with port selection and fixed port baseline. The relationship between port selection and channel capacity is shown in the figure. As the normalized port spacing increases from 0 to 0.8, the channel capacity of the FAS with port selection increases significantly and eventually stabilizes at its maximum. This indicates that port selection improves system performance, especially with increasing spacing, enabling higher channel capacity. In contrast, the channel capacity of the fixed-port baseline hardly changes with increasing port spacing. This shows that without port selection, system performance remains relatively constant and is unaffected by changes in port spacing. The figure confirms the significant improvement brought by port selection to the system, especially with increasing port spacing. Compared to the baseline configuration, the FAS with port selection shows a clear advantage in achieving higher throughput.

[0168] Figure 3 This demonstrates the channel capacity as a function of normalized port spacing in FAS under scattering-rich and scattering-poor conditions and with a fixed port baseline. The relationship between the normalized port spacing and the FAS under scattering-rich conditions is shown. As the normalized port spacing increases from 0 to 0.8, the channel capacity of the FAS under scattering-rich conditions increases significantly and eventually stabilizes at a higher value than that under scattering-poor conditions. In contrast, the channel capacity of the fixed port baseline shows almost no improvement with increasing port spacing, indicating that system performance is unaffected by changes in port spacing in the absence of port selection. This figure highlights the significant advantage of scattering in improving system performance, especially in the FAS under scattering-rich conditions.

[0169] Figure 4 Demonstrates different numbers of transmitter terminal arrays and the number of receiver terminal arrays Below, the modeling error varies with the normalized antenna spacing ( The relationship between the normalized antenna spacing and the change of the normalized antenna spacing (represented by the red curve) is shown. The modeling error increased significantly, indicating that the error increases with increasing spacing. Similarly, the blue curve represents... The modeling error also increased, but compared with Compared to the previous situation, its growth rate is lower. (Represented by the green curve) The configuration exhibits the smallest modeling error among the three, proving that increasing... and The value of helps reduce modeling errors at larger antenna spacings. This figure illustrates... and How the choice affects the accuracy of modeling error relative to the normalized antenna spacing.

[0170] Figure 5 The relationship between channel capacity and SNR under different FAS configurations and ULA is shown. As SNR increases from 0 to 30 dB, the channel capacity of all configurations increases, and a significant advantage of the FAS configuration over ULA is observed. In the FAS configuration, [the following is a diagram showing the relationship between channel capacity and SNR]. The red curve shows the lowest capacity, while the curve representing... The green curve indicates the highest capacity. (Represents...) The blue curve lies between the two. The ULA, represented by the black diamond, exhibits the lowest capacity across all SNR levels, confirming that the FAS configuration provides better performance, especially at higher SNR values. This graph highlights the increased... and Benefits of achieving higher channel capacity.

[0171] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for dynamic beamforming optimization of vehicle-to-everything (V2X) networks with adjustable antenna assistance, characterized in that, Includes the following steps: S1: Construct a near-field multipath channel model for vehicle-to-everything (V2X) networks that incorporates scattering cluster distribution characteristics and geometric phase shift properties; S2: Combining the distribution characteristics of scattering clusters and geometric phase shift characteristics, and considering the maximum transmit power constraint of the base station, a joint optimization objective of the base station transmit beamforming vector and the port selection of the vehicular network terminal fluid antenna system is constructed with the goal of maximizing instantaneous channel capacity, and the signal-to-noise ratio function of the vehicular network terminal port is defined. S3: Solve the joint optimization objective under power constraints, calculate the optimal transmit beamforming vector for each port based on the maximum transmission ratio criterion, and traverse and filter the optimal ports according to the maximum signal-to-noise ratio criterion to obtain the optimal transmit beamforming vector and optimal port index that are suitable for the current near-field scattering environment. S4: Based on the optimal transmit beamforming vector and the optimal port index, the base station configures the actual transmit beamforming vector as the optimal transmit beamforming vector and sends the signal. At the same time, the vehicle-to-everything (V2X) terminal activates the port corresponding to the optimal port index to receive the signal, thereby achieving the maximum transmission rate under the current channel environment and completing the dynamic beamforming optimization of the V2X.

2. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 1, characterized in that, In step S1, the specific processing procedure is as follows: S11: Construct a three-dimensional spatial transmission distance function expression between the base station transmitting antenna, the scatterer, and the terminal user's fluid antenna system port; S12: Construct the complex impulse response function expression for the signal propagating through the scattering cluster to each port, including the path phase, the geometric phase of the transmitting array, and the geometric phase of the receiving port.

3. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 2, characterized in that, In step S11, the construction of the three-dimensional spatial transmission distance function system is based on the system's geometric topology model, which is defined as follows: The communication scenario is set up to include fixed base stations and mobile vehicle-to-everything (V2X) terminals. On the base station side, a configuration including... A uniform linear array of transmitting antennas, i.e., ULA, in a global three-dimensional Cartesian coordinate system, is the... The location of the root transmitting antenna is defined as ,in The range of values ​​is to Each transmitting antenna is spaced at a fixed interval. Arrangement; On the user side, the vehicle-to-everything (V2X) terminal is equipped with a fluid antenna system, namely FAS, which includes... A set of preset, fixed-position discrete candidate ports, the first... The location coordinates of each port Through formula Calculate, where, For time The changing attitude rotation matrix The center location of FAS For the first The inherent position offset vector of each port relative to the geometric center of FAS The value range is 0 to -1, in subscripts and superscripts Indicates the first An index identifier for each vehicle-to-everything (V2X) terminal user is used to distinguish different V2X terminal users.

4. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 3, characterized in that, In step S11, the specific processing procedure is as follows: S111: For direct coupling, base station antenna With port The instantaneous distance between them is: ； Assume it exists Distinguished rays, grouped into clusters, used Cluster The Path, time setting The relevant scattering or reflecting point is Then from the base station antenna The distance to the scatterer is: ； From scatterer to port The distance is: ； in, Let represent the three-dimensional spatial coordinates of the scatterer corresponding to the i-th propagation path in the l-th scattering cluster at time t; S112: The total path length of the multipath components is calculated as follows: ； S113: The expression for the three-dimensional spatial transmission distance function is then obtained as follows: ； in, Let t represent the three-dimensional spatial coordinates of the equivalent scattering center of the l-th scattering cluster at time t.

5. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 4, characterized in that, In step S12, the complex impulse response function is expressed as follows: ； in, A set representing a cluster; It is a random phase shift that reflects the randomness of scattering; and These are the segmented distances from the transmitter to the scatterer and from the scatterer to the receiver, respectively. Indicates the first The distance of each transmitting antenna element from the center point of the ULA; Used to define the direction of ULA, namely azimuth and elevation; and These are the base station's departure azimuth and departure elevation angles, which change over time. It is the FAS center to the first The distance between ports, Indicates the port spacing; Used to define the direction of FAS; and These are the azimuth and elevation angles at the receiver.

6. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 5, characterized in that, In step S2, the specific processing procedure is as follows: S21: The base station uses single-stream beamforming for downlink transmission. The base station transmits within one symbol period... The dimensional signal vector is: ； in, To shape the transmitted beam vector, The information symbol represents the unit power normalization condition and satisfies the power constraint. , That is the maximum transmission power; S22: When FAS selects the... When receiving data through a port, the distance from the base station to that port... The channel vector is denoted as , No. The complex baseband received signal at each port is represented as follows: ； Among them, superscript This indicates the conjugate transpose. It is the equivalent complex channel gain. It is additive noise; S23: Definition of the The instantaneous received signal-to-noise ratio function for each port is: ； in, Indicates noise power.

7. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 6, characterized in that, Step S2 further includes the following steps: S24: The original form of the joint optimization objective is defined as follows: ； Since the logarithmic function is monotonically increasing, the above joint optimization objective is equivalent to maximizing the received signal-to-noise ratio. The updated joint optimization objective is as follows: ； in, It is the first Channel capacity of each port.

8. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 7, characterized in that, In step S3, based on the maximum ratio transmission criterion, given the first... The closed-form formula for calculating the optimal beamforming vector for each port is: ； in, This represents the maximum transmit power constraint of the base station. Indicates port The relevant real base station-to-port channel vectors, Let be the Euclidean norm of the channel vector; Will Substituting into the signal-to-noise ratio function, we get the first... Maximum closed-loop signal-to-noise ratio of each port for: 。 9. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 8, characterized in that, In step S3, the selection rule for the optimal receiving port is: traverse all... The maximum instantaneous signal-to-noise ratio calculated for each port. Select the port index that provides the highest signal-to-noise ratio as the optimal port. : 。 10. The adjustable antenna-assisted dynamic beamforming optimization method for vehicle-to-everything (V2X) networks according to claim 9, characterized in that, In step S4, the optimal port of the base station is determined. The optimal beamforming vector configuration is as follows Based on the optimal port and optimal beamforming vector The maximum signal-to-noise ratio and maximum instantaneous channel capacity under the optimal configuration are calculated as follows: ； in, For maximum signal-to-noise ratio, Maximum instantaneous channel capacity.

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