5G beam forming system based on Beidou high-precision positioning assistance

Abstract

The invention discloses a 5G beam forming system based on Beidou high-precision positioning assistance, and the system is characterized in that the system comprises a Beidou PPP-B2b positioning module, an IMU inertial measurement module, a motion prediction compensation module, an ocean multipath channel modeling module, a beam optimization module, and a low-power-consumption triggering module; and the motion prediction compensation module adopts a Kalman filtering algorithm to realize data fusion and motion prediction. The ocean multi-path channel modeling module is used for constructing a multi-path channel model in an ocean environment and optimizing a beam forming weight matrix; and the low-power-consumption trigger module is used for dynamically activating or sleeping the beam adjustment calculation module according to the Beidou and IMU data change rate. According to the method, the problem of rapid beam misalignment is solved, and a foundation is laid for subsequent channel optimization; interference is dynamically suppressed by using high-precision positioning data, and the signal quality is improved; the energy consumption is reduced through intelligent decision, and the system endurance is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of intersection of wireless communications and satellite navigation, and in particular to a 5G beamforming system based on Beidou high-precision positioning assistance. Background Art

[0002] In existing technologies, traditional beamforming algorithms in aquatic environments are unable to adapt to the high-frequency motion of ships, resulting in frequent signal interruptions. Furthermore, complex multipath interference, surface reflections, and wave scattering induce strong multipath effects, reducing communication quality. Furthermore, energy constraints, as offshore equipment relies on limited energy, necessitate reducing system power consumption to extend endurance. Existing GPS-based beam tracking solutions, however, fail to address centimeter-level positioning accuracy and dynamic compensation. Others utilize fixed multipath mitigation algorithms, lacking targeted optimization for marine environments. Existing 5G beamforming technologies are primarily targeted at static or low-speed mobile scenarios and lack adaptability to highly dynamic marine environments.

[0003] Therefore, it is necessary to provide a 5G beamforming system based on Beidou high-precision positioning assistance to solve the above technical problems. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the invention.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a 5G beamforming system based on Beidou high-precision positioning assistance, characterized by comprising: a Beidou PPP-B2b positioning module for acquiring centimeter-level precision ship position data in real time; an IMU inertial measurement module for collecting the ship's three-axis acceleration and angular velocity data; and a motion prediction and compensation module that fuses Beidou positioning data and IMU data based on a Kalman filter algorithm to predict the ship's motion offset within a future time window.

[0006] The ocean multipath channel modeling module constructs a multipath channel model based on the sea surface reflection coefficient, wave height and atmospheric attenuation parameters;

[0007] Beam optimization module, which optimizes the transmission pattern of the antenna array based on the channel model and beamforming weight matrix;

[0008] The low-power trigger module dynamically activates or puts the beam optimization calculation unit into sleep mode according to the Beidou positioning data change rate and the IMU data change rate.

[0009] The motion prediction and compensation module uses the Kalman filter algorithm to achieve data fusion and motion prediction; the ocean multipath channel modeling module is used to build a multipath channel model in the ocean environment and optimize the beamforming weight matrix; the low-power trigger module is used to dynamically activate or dormant the beam adjustment calculation module according to the change rate of Beidou and IMU data;

[0010] Motion prediction and compensation algorithm: Integrating BeiDou PPP-B2b module (precision point positioning) and IMU data, the Kalman filter algorithm predicts the ship's motion trajectory within 500ms. Dynamically adjust the 5G antenna beam pointing to achieve pre-alignment compensation.

[0011] Ocean multipath channel modeling: Build a multipath channel model based on measured data, optimize the beamforming weight matrix, and suppress multipath interference;

[0012] Low-power trigger mechanism: The computing module is activated only when the ship's motion or channel change exceeds the threshold, reducing average power consumption by more than 30%.

[0013] As a preferred solution of the 5G beamforming system based on the Beidou high-precision positioning assistance system of the present invention, the Kalman filter algorithm of the motion prediction and compensation module includes the following state equations and observation equations:

[0014] The equation of state is calculated as:

[0015] x k =Ax k-1 +Bu k-1 +w k-1

[0016] Among them, X k is the state vector, A is the state transfer matrix, u k-1 is the IMU input, w k-1 is the process noise;

[0017] The calculation formula of the observation equation is:

[0018] z k =Hz k +v k

[0019] Among them, z k is BeiDou observation data, H is the observation matrix, v k is the observation noise.

[0020] As a preferred solution of the 5G beamforming system based on the Beidou high-precision positioning assistance system of the present invention, the channel impulse response of the ocean multipath channel modeling module is expressed as:

[0021]

[0022] Among them, a i is the attenuation coefficient of the i-th path, T i is the time delay, θ i It is a phase offset, and the parameters are dynamically updated through sea state data.

[0023] As an optimal solution for the 5G beamforming system based on the Beidou high-precision positioning assistance system described in the present invention, the activation condition of the low-power trigger module is the Beidou positioning data change rate: ΔP ≥ 0.1m / s or the IMU angular velocity change rate Δω ≥ 0.05rad / s, otherwise it enters sleep mode.

[0024] As a preferred solution of the 5G beamforming system based on the Beidou high-precision positioning assistance system of the present invention, the beamforming weight matrix of the beam optimization module is calculated by the following formula:

[0025]

[0026] Where H is the channel matrix, d is the target direction vector, and λ is the regularization coefficient.

[0027] The beneficial effects of the present invention are as follows: the present invention adopts the Beidou PPP-B2b positioning module, the IMU inertial measurement module, the motion prediction compensation module, the ocean multipath channel modeling module, the beam optimization module and the low-power trigger module, and through the motion prediction compensation algorithm of Beidou and IMU data fusion, significantly improves the alignment accuracy of the 5G beam under high dynamic sea conditions; reduces the beam alignment error to below 0.5°; the ocean multipath channel modeling and beam optimization algorithm effectively suppress the multipath effect, improves the communication quality, and increases the multipath interference suppression ratio by 15dB; the energy management strategy based on the trigger mechanism reduces the system power consumption, extends the working time of the offshore mobile platform, reduces the overall power consumption of the system, and ensures the safety and efficiency of ship navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0029] Figure 1 A flowchart of a 5G beamforming system based on Beidou high-precision positioning assistance according to an embodiment of the present invention;

[0030] Figure 2A schematic diagram of a 5G TDOA positioning principle of a 5G beamforming system based on Beidou high-precision positioning assistance according to an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of a BeiDou + 5G fusion positioning system based on a BeiDou high-precision positioning-assisted 5G beamforming system according to an embodiment of the present invention;

[0032] Figure 4 A flowchart of the fusion positioning solution of a 5G beamforming system based on Beidou high-precision positioning assistance as described in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0034] Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without making any creative work should fall within the scope of protection of the present invention.

[0035] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0036] Example 1

[0037] Reference Figures 1-4 The first embodiment of the present invention is a 5G beamforming system based on Beidou high-precision positioning assistance, characterized by including: a Beidou PPP-B2b positioning module for acquiring centimeter-level precision ship position data in real time; an IMU inertial measurement module for collecting the ship's three-axis acceleration and angular velocity data; a motion prediction and compensation module for fusing Beidou positioning data and IMU data based on a Kalman filter algorithm to predict the ship's motion offset within a future time window;

[0038] The ocean multipath channel modeling module constructs a multipath channel model based on the sea surface reflection coefficient, wave height and atmospheric attenuation parameters;

[0039] Beam optimization module, which optimizes the transmission pattern of the antenna array based on the channel model and beamforming weight matrix;

[0040] A low-power trigger module dynamically activates or sleeps the beam optimization calculation unit based on the Beidou positioning data change rate and the IMU data change rate;

[0041] The motion prediction and compensation module uses the Kalman filter algorithm to achieve data fusion and motion prediction; the ocean multipath channel modeling module is used to build a multipath channel model in the ocean environment and optimize the beamforming weight matrix; the low-power trigger module is used to dynamically activate or dormant the beam adjustment calculation module according to the change rate of Beidou and IMU data;

[0042] Motion Prediction Compensation Algorithm: This algorithm integrates Beidou PPP-B2b (Precise Point Positioning) and IMU data to predict the ship's trajectory within the next 500ms using a Kalman filter. It also dynamically adjusts the 5G antenna beam pointing to achieve pre-alignment compensation. This algorithm acquires Beidou PPP-B2b high-precision positioning data (centimeter-level accuracy) and IMU inertial measurement data (including acceleration and angular velocity). Using a Kalman filter algorithm, it fuses Beidou positioning data with IMU data to predict the ship's position offset and attitude change within the next 500ms. Based on the predictions, it calculates the beam pointing adjustment for the 5G Massive MIMO antenna array to achieve beam pre-alignment.

[0043] Ocean multipath channel modeling: Construct a multipath channel model based on measured data, optimize the beamforming weight matrix, and suppress multipath interference; establish an ocean multipath channel model, including the effects of sea surface reflection, wave scattering, and atmospheric attenuation; optimize the beamforming weight matrix based on the channel model to suppress multipath interference and improve signal reception strength; introduce a deep learning algorithm to dynamically adjust the beamforming parameters based on historical channel data to adapt to complex ocean environments.

[0044] Low power consumption trigger mechanism: The computing module is activated only when the ship motion or channel change exceeds the threshold, reducing the average power consumption by more than 30%; the Beidou positioning data change rate threshold and the IMU data change rate threshold are set, and the beam adjustment calculation is activated when either threshold is triggered; in the inactive state, some computing modules (such as the deep learning inference unit) are turned off to reduce system power consumption; an energy management module is introduced to dynamically adjust the beam update frequency according to the remaining power to extend the equipment operation time. Implementation and verification of the motion prediction compensation algorithm, hardware and experimental environment configuration,

[0045] To verify the effectiveness of the motion prediction and compensation algorithm, this embodiment built a test platform that simulates a high-dynamic environment at sea: a ship motion simulation device: using a free-degree hydraulic platform, it can simulate typical wave motion with a wave height of 3m and a period of 8s.

[0046] Beidou PPP-B2b positioning module: Integrates a Beidou-3 dual-frequency receiver, supports the PPP-B2b precise point positioning protocol, has a measured horizontal positioning accuracy of 2cm (RMS), and a data output frequency of 10Hz.

[0047] IMU module: MEMS inertial measurement unit (model: ADIS16470) is selected, with an acceleration range of ±16g, an angular velocity range of ±2000° / s, a data output frequency of 100Hz, and a time synchronization error with the Beidou module of less than 1ms.

[0048] The implementation details of the Kalman filter algorithm are as follows: the Kalman filter algorithm of the motion prediction and compensation module includes the following state equations and observation equations:

[0049] The equation of state is calculated as:

[0050] x k =Ax k-1 +Bu k-1 +w k-1

[0051] Among them, X k is the state vector, A is the state transfer matrix, u k-1 is the IMU input, w k-1 is the process noise;

[0052] The calculation formula of the observation equation is:

[0053] z k =Hz k +v k

[0054] Among them, z k is BeiDou observation data, H is the observation matrix, v k is the observation noise.

[0055] The beamforming weight matrix of the beam optimization module is calculated by the following formula:

[0056]

[0057] Where H is the channel matrix, d is the target direction vector, and λ is the regularization coefficient.

[0058] The core of the motion prediction and compensation algorithm lies in the deep integration of BeiDou and IMU data. The specific implementation steps are as follows:

[0059] State space modeling: define the state vector X = [x, y, v x ,v y ,v z ,θ,φ,ψ] T It includes position, velocity and attitude angle (roll, pitch, yaw). The state transfer matrix A is constructed based on the ship kinematic model, taking into account the influence of ship mass distribution and fluid dynamics, and the discretization step size is 10ms.

[0060] Noise covariance optimization: The process noise covariance matrix Q and the observation noise covariance matrix R are calibrated using measured data: The process noise covariance matrix Q reflects the cumulative error characteristics of the IMU data, mainly including the noise characteristics of the accelerometer and gyroscope. Its calculation formula is as follows:

[0061]

[0062] Among them, Q a is the noise covariance matrix of the accelerometer; Q g is the noise covariance matrix of the gyroscope.

[0063] The observation noise covariance matrix R reflects the observation uncertainty of BeiDou positioning data, mainly including the measurement errors of position and velocity. Its calculation formula is as follows:

[0064]

[0065] Among them, R p is the position measurement noise covariance matrix; R v is the velocity measurement noise covariance matrix.

[0066] The value of Q reflects the cumulative error characteristics of the IMU data, and R corresponds to the observation uncertainty of the BeiDou positioning data. Position measurement noise covariance matrix R p , the position measurement noise of Beidou positioning data mainly includes horizontal error and vertical error, and its covariance matrix R p It can be expressed as:

[0067]

[0068] in, The standard deviations of position measurements in the east, north, and celestial directions (unit: meters).

[0069] Velocity measurement noise covariance matrix R v The velocity measurement noise of Beidou positioning data mainly includes the errors of horizontal velocity and vertical velocity, and its covariance matrix R v It can be expressed as:

[0070]

[0071] in, The standard deviations of velocity measurements in the east, north and celestial directions (unit: m / s) are shown respectively.

[0072] Prediction and update process:

[0073] 1. Prediction stage: Use IMU data to estimate the short-term motion state of the ship and generate a priori estimate X k|k-1 ;

[0074] 2. Update phase: Use Beidou positioning data as observation value Z k , correct the prior estimate and get the posterior estimate X k|k ;

[0075] 3. Beam pre-alignment: According to X k|k Predict the ship's position and attitude within the next 500ms and calculate the antenna beam pointing compensation angle where Δ x , Δ y To predict the displacement.

[0076] Experimental results and logic analysis, a continuous 2-hour test was conducted in a simulated wave environment to compare the performance of the three solutions: as shown in Table 1

[0077] Table 1

[0078]

[0079] Key conclusions and logical chain:

[0080] 1. The key role of IMU data: Traditional solutions rely solely on BeiDou positioning data. Due to the 10Hz update frequency, it cannot capture high-frequency ship shaking (such as roll angular velocity up to 10°).

[0081] / s), resulting in beam adjustment lag. By incorporating 100Hz IMU data, the algorithm can track the ship's instantaneous motion in real time, reducing the maximum error from 3.2° to 1.1°.

[0082] 2. Reasonableness of the prediction window: A 500ms prediction window was determined through Monte Carlo simulation optimization. If the window is shorter than 300ms, it cannot cover the execution delay of beam control commands (approximately 200ms). If it is longer than 800ms, the prediction error increases significantly with the randomness of the waves. Experiments show that the standard deviation of the predicted position error in the 500ms window is 0.05m, which meets the beam alignment requirements.

[0083] 3. Computational efficiency optimization: FPGA hardware is used to accelerate Kalman filter operations, compressing the single prediction-update cycle to within 10ms to ensure the real-time performance of the algorithm.

[0084] In summary, the motion prediction compensation algorithm solves the problem of rapid beam misalignment and lays the foundation for subsequent channel optimization.

[0085] 5G communication technology has taken positioning capabilities into consideration during its design, and the time-of-arrival (TDOA) method has been explicitly selected as a candidate algorithm for 5G wireless positioning systems. The TDOA method does not require strict time synchronization between 5G user equipment (UE) and 5G base stations (BS), but only requires precise time synchronization between 5G base stations. Therefore, it is well-suited for 5G positioning systems. TDOA positioning is used by 5G positioning systems to determine the location of UEs. TDOA positioning based on 5G downlink signals is a commonly used method. The specific steps for positioning are as follows:

[0086] (1) The UE autonomously measures the time it takes for 5G downlink signals from multiple 5G base stations to reach the UE, and the locations of these base stations are known.

[0087] (2) Select a base station as a reference base station, and subtract the arrival time of the reference base station signal from the arrival time of the other base station signals to obtain the time difference.

[0088] (3) Multiply the time difference by the speed of light to obtain the distance difference between the other base stations and the reference base station and the UE.

[0089] (4) Taking the two base stations corresponding to the distance difference as the focus, a set of hyperbolic equations is constructed.

[0090] (5) Combine multiple sets of hyperbolic positioning equations and solve them to obtain the UE's position estimate.

[0091] The principle diagram of 5G TDOA positioning is as follows: Figure 2 As shown, the base station BS1 is selected as the reference base station, and the distance d1 from the base station BS1 to the UE and the base station BS i Distance to UE d i , and the difference is d 1,i , based on this, multiple sets of hyperbolas are obtained, and the intersection point of the multiple sets of hyperbolas is the position of the UE.

[0092] If there is N in the 5G positioning system b A base station sends a 5G signal to the UE. Figure 2 The geometric relationship in , we can get the following positioning equations:

[0093]

[0094] In the formula, (xb i , yb i , zb i ) is the spatial coordinate of the base station BSi, (x u ,y u , z u) is the spatial coordinate of the UE. In formula (2-1), there are three unknown quantities in the UE coordinates, so theoretically, at least four base stations are required to form three hyperbolic equations to solve the unknown quantities. After the 5G UE measures the arrival time of the signals from at least four base stations, it establishes a hyperbolic equation group as shown in formula (2-1). Solving this nonlinear equation group can calculate the position of the UE. As can be seen from formula (2-1), in the 5G positioning system, d 1,i The TOA is the most important observation. Therefore, the arrival time of the base station signal measured by the UE has a significant impact on the positioning results. The key issues in 5G positioning systems lie in how to accurately measure the TOA of the base station signal and how to solve the nonlinear equation system.

[0095] Classic methods for solving Equation (2-1) include the Taloy algorithm and the Chan algorithm. When the TOA measurement error of the base station signal is small, the Chan algorithm's estimated result is closer to the maximum likelihood value. The Chan algorithm also requires relatively little computation to solve the hyperbolic equations. The following briefly describes the positioning process using this method.

[0096] By d 1,i =d i -d1 gives:

[0097]

[0098] Where, d i Satisfy the following formula:

[0099]

[0100] Substituting formula (2-2) into formula (2-3) yields:

[0101]

[0102] because Satisfy the following formula:

[0103]

[0104] Substituting formula (2-5) into formula (2-4) yields:

[0105]

[0106] Formula (2-6) includes the UE location parameter x u 、y u 、z u The system of equations can be expressed in matrix form as follows:

[0107]

[0108] Where, X c =[x u y u z u d1] T is the unknown quantity, and the simplified form of the formula is as follows:

[0109] G c X c =h c (2-8)

[0110] The error vector is expressed as follows:

[0111] ψ c =h c -G c X c (2-9)

[0112] Assuming that the elements of Xc are independent of each other, the weighted least squares estimation result of Xc is expressed as follows:

[0113] X c =(G c T W c -1 G c ) -1 G c T W c -1 h c (2-10)

[0114] Where, Wc=c 2BcQBc, c is the speed of light, Q is the covariance matrix of TDOA measurement error.

[0115] In fact, d1 and x u 、y u 、z u The elements of Xc are not completely independent because they are related. A possible approach is to first use the estimated value obtained by formula (2-10) as the initial solution, and then perform a second round of weighted least squares estimation based on this initial solution and its constraint relationship with the parameters to be estimated to optimize the accuracy of the initial solution. When there is an error in formula (2-9), the following relationship holds:

[0116]

[0117] To prove that, ΔX c Obeying a Gaussian distribution with a mean of 0, then ΔX c The elements in can be represented as follows:

[0118]

[0119] Where, is the estimation error, according to h c The constraint relationship between the elements in , we can get the following expression:

[0120] ψ′ c =h′ c -G′ c X′ c (2-13)

[0121] Where:

[0122]

[0123] From formula (2-13), we can see that ψ' c The covariance matrix of

[0124] W′ c =E[ψ′ c (ψ′ c ) T ]=4B′ c cov(X c )B′ c (2-17)

[0125] Where, When the UE is far away from the base station, the covariance matrix of Xc can be approximately expressed as follows:

[0126]

[0127] Then, X′ c The weighted least squares estimate of is:

[0128] X′ c =((G′ c ) T (B′ c ) -1 G c Q -1 G c T (B′ c ) -1 G′ c ) -1 (G′ c ) T (B′ c ) -1 G c T Q -1 G c (B′ c ) -1 h′ c(2-19)

[0129] By X′ c The expression of can be concluded that the real position of UE is:

[0130]

[0131] The Chan algorithm relies on a key assumption in the calculation process described above: the noise level in TDOA measurements is low and follows a Gaussian distribution with a mean of zero. This assumption enables the algorithm to locate user devices with high accuracy, while also minimizing the overall computational effort because it does not require iterative calculations.

[0132] Leveraging the extensive coverage and positioning capabilities of 5G technology, combined with the BeiDou system, a more reliable BeiDou + 5G integrated positioning system can be built. This system can provide more accurate and stable positioning services at sea, compensating for the shortcomings of a single system. Figure 3 It is a schematic diagram of the fusion positioning system.

[0133] This application combines the hyperbolic equations of 5G TDOA positioning and the BeiDou pseudorange single-point positioning equations to form the BeiDou + 5G fusion positioning equations, which are expressed as follows:

[0134]

[0135] Where (x, y, z) is the user's position coordinates, plus the clock error of the user's satellite receiver

[0136] The above equations have four unknowns, so theoretically there are four equations to solve the nonlinear equations. The process of solving the equations with the fusion algorithm is as follows: Figure 4 As shown;

[0137] After determining the user's location through the fusion algorithm, this application uses the root mean square error (RMSE) as an indicator to evaluate positioning accuracy, which is expressed as follows:

[0138]

[0139] Where Y is the true value, Y i is the sample value.

[0140] Example 2

[0141] Reference Figures 1-4 The second embodiment of the present invention discusses the implementation and verification of ocean multipath channel modeling and beam optimization. The channel impulse response of the ocean multipath channel modeling module is expressed as:

[0142]

[0143] Among them, a i is the attenuation coefficient of the i-th path, T i is the time delay, θ i The phase offset parameter is dynamically updated based on sea state data. The data foundation and theoretical framework for channel modeling, the complexity of ocean multipath effects, requires the establishment of an environment-adaptive channel model. This embodiment is achieved through the following steps: field data acquisition, reflection coefficient modeling, multipath delay distribution, regularization coefficient optimization, performance testing, and causal relationship analysis.

[0144] Field data collection: Buoy-type channel detection equipment was deployed in a certain area of ​​the East China Sea to continuously collect channel impulse response (CIR) data for 30 days, totaling more than 100,000 sets of samples.

[0145] Reflection coefficient modeling: The relationship between the sea surface reflection coefficient α and the wave height h is fitted by the least squares method, and the goodness of fit R 2 =0.89, indicating that the model can effectively characterize the inhibitory effect of wave height on reflection intensity.

[0146] Multipath delay distribution: The multipath delay τ obeys the Rayleigh distribution, and its mean is linearly related to the wave height.

[0147] Regularization coefficient optimization: Cross-validation determined that λ = 0.1, at which point the algorithm strikes a balance between noise suppression and overfitting. When λ = 0.05, the bit error rate (BER) increases by 20%, while when λ = 0.2, the signal strength decreases by 8dBm.

[0148] Performance testing and causal analysis: In a real-world test environment with a wave height of 2.5m and a wind speed of 10m / s, the differences between traditional beamforming and this solution are compared, as shown in Table 2:

[0149] Table 2

[0150]

[0151] In summary, the technical logic relevance is explained above. The existing technology uses a fixed multipath suppression template and cannot adapt to dynamic sea conditions. The solution of the present invention updates the channel model parameters (such as α and τ) in real time, so that the beamforming weight matrix W always matches the current environment, thereby improving the multipath suppression ratio by 15dB. The practical significance of the regularization term: λ||W|| 2 This item limits the fluctuation of the antenna array's transmission power, avoiding power amplifier overload caused by extreme waves. In actual measurements, the power amplifier efficiency has increased by 18%.

[0152] In summary, ocean multipath channel modeling uses high-precision positioning data to dynamically suppress interference and improve signal quality.

[0153] Example 3

[0154] Reference Figures 1-4 The third embodiment of the present invention discusses the implementation and verification of the low power trigger mechanism: the activation condition of the low power trigger module is the Beidou positioning data change rate

[0155] ΔP ≥ 0.1 m / s or IMU angular velocity change rate Δω ≥ 0.05 rad / s, otherwise it enters sleep mode.

[0156] The beamforming weight matrix of the beam optimization module is calculated using the following formula:

[0157]

[0158] Where H is the channel matrix, d is the target direction vector, and λ is the regularization coefficient. Theoretical and experimental basis for threshold setting. The core of the low-power trigger mechanism is to balance response speed and energy consumption. This embodiment determines the threshold through the following steps:

[0159] 1. Motion sensitivity analysis: Within the typical motion range of a ship (roll angle ±15°, pitch angle ±10°), the impact of different Beidou positioning change rates (ΔP) and IMU angular velocity change rates (Δω) on communication quality was tested. The results show that:

[0160] When ΔP < 0.1 m / s and Δω < 0.05 rad / s, the signal attenuation caused by beam misalignment is less than 3 dB, allowing for a brief degradation in communication quality. When either threshold is exceeded, the signal attenuation rate increases dramatically, requiring immediate beam adjustment.

[0161] 2. Threshold optimization verification: We compared system performance and energy consumption under different threshold combinations and ultimately selected one that maintained a bit error rate below 1e-4 while reducing the number of invalid beam adjustments by 65%.

[0162] Energy consumption test and energy efficiency logic chain, in a 12-hour continuous test, the status of the two working modes is compared through Table 3:

[0163] Table 3

[0164]

[0165] The logic behind energy efficiency improvement is as follows:

[0166] Effectiveness of the sleep strategy: In low-power mode, the system monitors ΔP and Δω in real time, activating the computing module only when necessary. Measured data in Table 3 shows that when the ship is stationary or sailing at low speed, the system sleeps for 63% of the time, minimizing wasted computing resources. Dynamic frequency adjustment: When the remaining battery power falls below 20%, the beam update frequency is reduced from 10Hz to 5Hz, further reducing power consumption. At this point, the signal strength drops by only 2dBm, still meeting the minimum maritime communication requirement (-75dBm).

[0167] In summary, the low-power trigger mechanism reduces energy consumption and extends system endurance through intelligent decision-making. Through the progressive verification of the three aforementioned embodiments, the technical solution of the present invention forms a complete logical closed loop. Through the coordinated operation of these three elements, the comprehensive performance indicators of maritime 5G communications reach industry-leading levels.

[0168] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0169] It will be appreciated that in the development of any actual embodiment, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will, for those of ordinary skill having the benefit of this disclosure, be a routine undertaking of design, fabrication, and production without undue experimentation.

[0170] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A 5G beamforming system based on Beidou high-precision positioning assistance, characterized in that: include: Beidou PPP-B2b positioning module, used to obtain centimeter-level accuracy of ship position data in real time; IMU inertial measurement module, used to collect the ship's three-axis acceleration and angular velocity data; The motion prediction and compensation module uses the Kalman filter algorithm to fuse BeiDou positioning data and IMU data to predict the ship's motion offset within the future time window; The ocean multipath channel modeling module constructs a multipath channel model based on the sea surface reflection coefficient, wave height and atmospheric attenuation parameters; Beam optimization module, which optimizes the transmission pattern of the antenna array based on the channel model and beamforming weight matrix; A low-power trigger module dynamically activates or sleeps the beam optimization calculation unit based on the Beidou positioning data change rate and the IMU data change rate; The motion prediction and compensation module uses the Kalman filter algorithm to achieve data fusion and motion prediction; The ocean multipath channel modeling module is used to construct a multipath channel model in an ocean environment and optimize the beamforming weight matrix; The low-power trigger module is used to dynamically activate or sleep the beam adjustment calculation module according to the change rate of Beidou and IMU data.

2. The 5G beamforming system based on Beidou high-precision positioning assistance according to claim 1 is characterized in that: The Kalman filter algorithm of the motion prediction and compensation module includes the following state equations and observation equations: The equation of state is calculated as follows: x k =Ax k-1 +Bu k-1 +w k-1 Among them, X k is the state vector, A is the state transfer matrix, u k-1 is the IMU input, w k-1 is the process noise; The calculation formula of the observation equation is as follows: with k =Hz k +v k Among them, z k is BeiDou observation data, H is the observation matrix, v k is the observation noise.

3. The 5G beamforming system based on BeiDou high-precision positioning assistance according to claim 1 is characterized in that: The channel impulse response of the ocean multipath channel modeling module is expressed as: Among them, a i is the attenuation coefficient of the i-th path, T i is the time delay, θ i It is a phase offset, and the parameters are dynamically updated through sea state data.

4. The 5G beamforming system based on Beidou high-precision positioning assistance according to claim 1 is characterized in that: The activation condition of the low-power trigger module is that the Beidou positioning data change rate ΔP ≥ 0.1m / s or the IMU angular velocity change rate Δω ≥ 0.05rad / s, otherwise it enters the sleep mode.

5. The 5G beamforming system based on Beidou high-precision positioning assistance according to claim 1 is characterized in that: The beamforming weight matrix of the beam optimization module is calculated by the following formula: Where H is the channel matrix, d is the target direction vector, and λ is the regularization coefficient.

6. The 5G beamforming system based on Beidou high-precision positioning assistance according to claim 1 is characterized in that: It also includes a positioning algorithm, which is divided into two stages: an offline stage and an algorithm stage.

7. The 5G beamforming system based on Beidou high-precision positioning assistance according to claim 1 is characterized in that: The Kalman filter algorithm is integrated with the particle filter algorithm, and the data information obtained in the positioning stage is subjected to Kalman filtering to eliminate errors, and the particle filter algorithm is used to perform weight distribution.

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