Adaptive signal processing method for wireless communication

Abstract

This invention discloses an adaptive signal processing method for wireless communication, relating to the field of communication data processing technology. It employs a linearly constrained minimum variance algorithm to generate a beamforming weight matrix. The base station configures a programmable antenna array and calculates a directional suppression coefficient. If the coefficient does not reach a preset first threshold, it calculates interference suppression mode parameters based on terminal status data, encodes them as a downlink reference signal, and sends it to the terminal. After parsing the signal, the terminal configures an interference suppressor and calculates a joint interference suppression coefficient, feeding it back to the network side. If this coefficient exceeds a preset second threshold, the beamforming weight matrix is ​​dynamically adjusted based on the difference between the coefficient and the threshold, as well as the spatial differences of each target antenna array, completing closed-loop optimization. This invention, by constructing a two-way feedback mechanism between the base station and the terminal, achieves dynamic coordination between beamforming and interference suppression, significantly improving communication quality and system resource utilization efficiency in complex interference environments.

Description

Technical Field

[0001] This invention relates to the field of communication data processing technology, and more specifically, this application relates to an adaptive signal processing method for wireless communication. Background Technology

[0002] In wireless communication in ultra-dense user scenarios, existing technologies generally suffer from a lack of coordinated processing of spatial domain beamforming and signal domain interference suppression when dealing with complex interference environments. Traditional methods treat beam configuration and interference cancellation as independent processes, making it difficult to achieve optimal resource allocation in dynamic environments. Especially in scenarios with uneven user distribution and multiple interference sources, static strategies cannot effectively balance coverage performance and interference suppression requirements.

[0003] Traditional beamforming technologies rely on pre-set, fixed parameter models, making it difficult to respond in real-time to rapid changes in the interference environment. For example, when user mobility increases or sudden interference occurs, existing technologies often require periodic sensing and policy updates to adjust the beam direction, resulting in a lag in interference suppression. Furthermore, differences in terminal interference suppression capabilities are not fully incorporated into the beam optimization process, leading to some terminals being unable to effectively eliminate residual interference due to insufficient suppression capabilities, impacting overall network capacity. At the data fusion level, existing technologies lack sufficient multi-dimensional information correlation analysis of network status, terminal capabilities, and interference characteristics, making it difficult to construct a global optimization decision-making basis. This results in a disconnect between interference suppression strategies and channel conditions, exacerbating the blind allocation of resources.

[0004] To address the aforementioned issues, the field requires an adaptive processing solution capable of dynamically coordinating beamforming and interference suppression, and adapting to real-time changes in ultra-dense user environments. Existing technologies lack rapid response mechanisms in dynamic interference environments and struggle to accommodate differences in suppression capabilities among different terminals, leading to significant interference residue and resource waste in high-density scenarios, severely restricting communication performance in complex environments such as airports and stadiums. Summary of the Invention

[0005] To address the aforementioned technical problems, this technical solution provides an adaptive signal processing method for wireless communication, resolving the issues raised in the background section.

[0006] This application provides a wireless communication adaptive signal processing method, including the following steps: receiving uplink signals from base stations in a target area and processing them to obtain interference feature data; receiving state information from terminals in the target area and processing it to obtain terminal state data; processing the interference feature data using a linear constrained minimum variance algorithm to obtain a beamforming weight matrix, and loading the beamforming weight matrix onto the corresponding base station; obtaining the directional suppression coefficient fed back by the base station, the directional suppression coefficient being calculated by the base station by parsing the beamforming weight matrix and configuring a programmable antenna array in the target area; if the directional suppression coefficient does not exceed a preset first threshold, calculating the corresponding interference suppression mode parameters based on the terminal state data and the directional suppression coefficient; encoding the interference suppression mode parameters into a downlink reference signal according to a preset rule, and sending it to the corresponding terminal in the target area; obtaining the joint interference suppression coefficient fed back by the terminal, the joint interference suppression coefficient being calculated by the terminal by parsing the downlink reference signal; if the joint interference suppression coefficient exceeds a preset second threshold, adjusting the beamforming weight matrix according to the difference between the joint interference suppression coefficient and the preset second threshold.

[0007] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0008] 1. By constructing a closed-loop feedback control mechanism, the beamforming weight matrix is ​​dynamically adjusted based on the difference between the joint interference suppression coefficient and the preset threshold, as well as the average value of weight changes. This allows for rapid optimization of beam pointing and null depth based on real-time terminal feedback and algorithm convergence status, overcoming the lag of traditional periodic update strategies. It enables real-time tracking and suppression of dynamic interference environments, achieving dynamic response and solving the problem of interference suppression lag.

[0009] 2. By constructing a comprehensive cost function with interference suppression level and terminal power consumption level as the core objectives, interference suppression mode parameters are generated. The base station beam configuration is deeply integrated with the actual capabilities of the terminal, such as algorithm power consumption, to ensure that instructions match the actual processing capabilities of the terminal, avoid resource mismatch, and achieve precise coordination and efficient allocation of resources between the network side and the terminal side.

[0010] 3. Starting with the collection of interference characteristic data and terminal status data, the directional suppression coefficient is calculated by integrating multi-dimensional parameters such as the azimuth angle of each interference source, the three-dimensional spatial coordinates of the antenna array, and the interference intensity. A complete multi-dimensional data perception and quantitative evaluation system has been established, providing accurate decision-making basis for beamforming weight adjustment and terminal interference suppressor configuration, significantly improving the level of intelligence in complex scenarios. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the structure of the wireless communication adaptive signal processing method provided in the embodiments of this application;

[0012] Figure 2 This is a schematic diagram of the logic flow of the wireless communication adaptive signal processing method provided in the embodiments of this application. Detailed Implementation

[0013] This application embodiment solves the technical problem in the prior art where it is difficult to balance interference residue and terminal suppression resource waste in high-density scenarios through a wireless communication adaptive signal processing method.

[0014] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0015] like Figure 1 The diagram shows a schematic of the wireless communication adaptive signal processing method provided in this application embodiment, including the following steps: receiving uplink signals from base stations in the target area and processing them to obtain interference feature data; receiving terminal status information from terminals in the target area and processing it to obtain terminal status data; processing the interference feature data using a linear constrained minimum variance algorithm to obtain a beamforming weight matrix, and loading the beamforming weight matrix onto the corresponding base station; obtaining the directional suppression coefficient fed back by the base station, which is calculated by the base station by parsing the beamforming weight matrix and configuring a programmable antenna array in the target area; if the directional suppression coefficient does not exceed a preset first threshold, calculating the corresponding interference suppression mode parameters based on the terminal status data and the directional suppression coefficient; encoding the interference suppression mode parameters into a downlink reference signal according to a preset rule, and sending it to the corresponding terminal in the target area; obtaining the joint interference suppression coefficient fed back by the terminal, which is calculated by the terminal parsing the downlink reference signal; if the joint interference suppression coefficient exceeds a preset second threshold, adjusting the beamforming weight matrix according to the difference between the joint interference suppression coefficient and the preset second threshold.

[0016] A preset first threshold is used to determine whether the interference suppression effect of beamforming on the base station side is sufficient. It is set based on the minimum directional suppression coefficient that, under typical interference scenarios, can guarantee the basic communication quality of the target user, determined through network simulation or field testing.

[0017] A preset second threshold is used to determine whether the actual performance of the interference suppressor on the terminal side meets expectations. It is set based on the minimum joint interference suppression coefficient obtained through laboratory testing of the terminal, under typical operating modes, when the interference suppressor effectively improves the received signal-to-interference-plus-noise ratio. In actual systems, these two thresholds can be dynamically configured by network operators according to different scenario requirements (such as stadiums and airports).

[0018] Interference characteristic data can be understood as a set of information reflecting the distribution and intensity of interference sources within a target area. It can be collected by multiple sensor nodes deployed within the target area, such as using electromagnetic signal detection equipment to obtain information such as the azimuth angle, frequency range, and power density of the interference sources.

[0019] Terminal status data refers to the ability of a terminal device to perform interference suppression operations in its current state. It can be obtained by the terminal device actively reporting, for example, by the terminal device sending a data packet to the base station containing its hardware performance parameters, current remaining battery power, and supported interference suppression algorithm types.

[0020] The linear constraint minimum variance algorithm is a mathematical tool for optimizing beamforming weight matrices. Its core is to minimize the variance of the output signal by constructing specific constraints, thereby enhancing the target signal and suppressing interference signals.

[0021] In practical applications, this algorithm can be implemented in various ways, such as using a gradient descent-based numerical optimization method to solve for the optimal weight vector, or using a genetic algorithm to search for the global optimal solution that satisfies the constraints.

[0022] By dynamically integrating interference environment data and terminal capability data, a closed-loop collaborative mechanism for beamforming and terminal interference suppression was constructed. This not only achieved deep linkage between spatial domain and signal domain processing, but also effectively solved the problems of blind resource allocation and interference residue in ultra-dense user scenarios. At the same time, it avoided resource waste caused by excessive suppression, thereby improving adaptive capability and overall performance.

[0023] Furthermore, the beamforming weight matrix is ​​obtained by processing the interference feature data using a linearly constrained minimum variance algorithm. Specifically, this includes: dividing the programmable antenna array into several target antenna arrays; obtaining the position vectors of the target antenna arrays in a preset coordinate system with the geometric center of the programmable antenna array as the reference point; obtaining the base station signal carrier wavelength; extracting the interference azimuth angles of each interference source from the interference feature data and combining them with the position vectors of the target antenna arrays in the programmable antenna array and the base station signal carrier wavelength; calculating the array steering vectors of the interference azimuth angles based on a planar wavefront model; and combining the array steering vectors of the interference azimuth angles. The process involves: constructing a linear constraint matrix; obtaining a preset desired response vector, where each element corresponds to a constraint response value for an interference azimuth angle; constructing a Capon beamforming optimization problem, the objective function of which is to minimize the array output variance, with the constraint condition that the product of the conjugate transpose of the linear constraint matrix and the complex weight vector equals the desired response vector; solving the Capon beamforming optimization problem using the Lagrange multiplier method to obtain the optimal complex weight vector; configuring the optimal complex weight vector as a beamforming weight matrix, where each weight in the beamforming weight matrix uniquely corresponds to a target antenna array in the programmable antenna array.

[0024] In this embodiment, a preset desired response vector is defined, where each element defines the desired array response gain at the corresponding interference azimuth angle. To suppress interference to the maximum extent, these elements are set to zero, indicating that a null trap is expected to be formed in the interference direction. In some scenarios, to avoid excessive suppression leading to beam distortion, it can also be set to a non-zero small value (such as -20dB).

[0025] Interference characteristic data refers to a set of data that reflects the distribution, intensity and changing trend of interference in the current communication environment. It can be obtained by estimating the covariance matrix of multi-channel received signals or by collecting real-time channel state information.

[0026] Azimuth angle can be understood as the spatial position angle of the interference source relative to the antenna array, which is usually extracted by spatial spectrum estimation techniques such as the MUSIC algorithm or multi-beam scanning method.

[0027] The geometry of an antenna array refers to the arrangement of antenna elements in physical space, such as a uniform linear array, a uniform planar array, or a circular array. Its purpose is to provide an accurate phase relationship model to support subsequent calculations.

[0028] A linear constraint matrix is ​​a mathematical matrix composed of arrayed steering vectors of multiple interference azimuth angles. Its function is to integrate the directional information of multiple interference sources into a unified constraint framework, thereby ensuring that the beamforming weight matrix can handle multiple interference directions simultaneously.

[0029] The desired response vector refers to the set of constraint response values ​​set for different interference azimuth angles. It can flexibly adjust the suppression strength according to the interference characteristics, thereby optimizing the balance between interference suppression and signal fidelity.

[0030] The goal of the Capon beamforming optimization problem is to minimize the array output variance while satisfying linear constraints. This approach transforms the interference suppression objective into a mathematical optimization problem, making the weight calculation closely aligned with actual communication needs.

[0031] The Lagrange multiplier method is an efficient analytical solution method that avoids the computational delay of iterative algorithms and is suitable for real-time, rapid response to changes in disturbances.

[0032] By extracting the azimuth angles of each interference source from the interference feature data, real-time and accurate interference source location information is provided for subsequent calculations. In particular, the dynamic acquisition of azimuth angles can adapt to scenarios where interference sources move or are added, avoiding the shortcomings of static models in capturing environmental changes.

[0033] The array steering vector for each interference azimuth angle is calculated based on the azimuth angle and the geometry of the antenna array. A phase relationship model is customized using specific hardware layout to ensure accurate matching between the physical array and the mathematical model, effectively reducing calculation deviations caused by differences in antenna design.

[0034] By combining the array steering vectors of all interference azimuth angles to construct a linear constraint matrix, and integrating information from multiple interference sources to form a unified constraint framework, robustness in complex interference environments is enhanced.

[0035] The system obtains the preset expected response vector and specifies the constraint response value for each interference azimuth angle, allowing for flexible adjustment of the suppression strength according to the interference characteristics, thus optimizing the balance between interference suppression and signal fidelity.

[0036] We construct a Capon beamforming optimization problem with the objective of minimizing the array output variance and set linear constraints to ensure that the signal-to-noise ratio is maximized while meeting interference suppression requirements.

[0037] The optimal complex weight vector is obtained by solving the optimization problem using the Lagrange multiplier method, providing an efficient analytical solution and avoiding the computational delay of iterative algorithms. The optimal complex weight vector is then configured as a beamforming weight matrix, achieving a seamless transition from theoretical optimization to practical application and effectively improving the overall performance of interference suppression.

[0038] This solves the problem of inaccurate beamforming weight matrix generation under dynamic interference environments, ensuring real-time response to interference changes and improving the accuracy of interference suppression, thereby significantly improving the performance of wireless communication.

[0039] Furthermore, configuring the programmable antenna array in the target area specifically includes: decomposing the beamforming weight matrix into amplitude adjustment coefficients and phase adjustment coefficients, wherein the amplitude adjustment coefficients control the power of the transmitted signal of each target antenna array, and the phase adjustment coefficients control the phase of the transmitted signal of each target antenna array; precoding the transmitted signal of each antenna link using the baseband digital beamformer of the base station; adjusting the precoded transmitted signal in the analog domain using the radio frequency phase shifter and attenuator of the base station, wherein the phase shifter adjusts the signal phase, and the attenuator adjusts the signal amplitude; and configuring the adjusted precoded transmitted signal into the target antenna array of the corresponding programmable antenna array.

[0040] In this embodiment, the beamforming weight matrix is ​​a complex matrix whose elements correspond to the weighting coefficients of each antenna element in the antenna array. This matrix can be generated using various algorithms, such as methods based on the minimum mean square error criterion or maximum signal-to-noise ratio optimization.

[0041] The amplitude adjustment factor can be understood as a parameter used to adjust the power of the transmitted signal of each target antenna array. It can be implemented by digitally controlling attenuators or power amplifiers. The purpose is to ensure that the power of the transmitted signal of different antenna elements can be dynamically adjusted according to environmental requirements.

[0042] The phase adjustment coefficient is a parameter used to control the phase of the transmitted signal of each target antenna array. It can be implemented by a phase shifter or a phase modulator, and is intended to achieve precise beam directionality.

[0043] A baseband digital beamformer is a device that preprocesses signals in the digital domain. It can pre-encode signals using methods such as Fast Fourier Transform or Discrete Cosine Transform, thereby providing an optimization basis for subsequent analog domain adjustments.

[0044] Radio frequency phase shifters and attenuators are hardware modules used to finely adjust signals in the radio frequency domain. Their function is to compensate for errors that may be introduced by digital precoding and to adapt to the non-ideal characteristics of actual radio frequency links.

[0045] By decomposing the beamforming weight matrix into amplitude adjustment coefficients and phase adjustment coefficients, the complex weights can be directly mapped to the operating parameters of the antenna hardware, solving the problem that the weights cannot be resolved by physical devices, thus supporting precise control of the beam direction.

[0046] Based on this, the baseband digital beamformer precodes the transmitted signal of each antenna link, using its flexibility to generate optimized signals, laying the foundation for subsequent analog adjustments.

[0047] Meanwhile, the RF phase shifter and attenuator further adjust the signal in the analog domain, compensating for quantization errors that may exist in digital precoding, and adapting to the non-ideal characteristics of actual RF links, thus enhancing robustness in dynamic environments.

[0048] Finally, the adjusted signal is distributed to specific antenna elements, completing the closed loop from theoretical weights to physical implementation. This enables beamforming to be updated in real time according to the interference environment, effectively suppressing interference.

[0049] By using a hybrid digital and analog processing approach, real-time performance and accuracy are improved, thereby significantly enhancing the performance of wireless communication in complex scenarios.

[0050] Further, the specific process for calculating the directional suppression coefficient is as follows: Obtain the interference azimuth angle and the three-dimensional coordinates of the target antenna array in a preset coordinate system; based on the interference azimuth angle and the preset coordinate system, convert the interference azimuth angle into the direction of the three-dimensional incident wave relative to the target antenna array; calculate the path difference between the three-dimensional incident wave reaching each target antenna array and reaching the phase center of the programmable antenna array based on the three-dimensional coordinates of the target antenna array and the direction of the three-dimensional incident wave; calculate the phase delay generated by the incident wave on each target antenna array based on the path difference and the base station signal carrier wavelength; convert the phase delay on each target antenna array into a complex phase factor; and convert the complex phase delay corresponding to all target antenna arrays... The position factors are combined to form the array steering vector for the interference azimuth angle; the beamforming weight vector is multiplied by the array steering vector for each interference azimuth angle to obtain the array response value for each interference azimuth angle; the array response value is squared modulo the array response value to obtain the array response gain for each interference azimuth angle; the array response gain for the preset desired signal direction is obtained and calculated as the reference gain; the ratio of the array response gain for each interference azimuth angle to the reference gain is calculated; the interference source intensity data corresponding to each interference azimuth angle is extracted from the interference feature data and normalized to obtain the weight coefficient of each interference source; based on the weight coefficient, the gain ratio of each interference azimuth angle is weighted and averaged to obtain the directional suppression coefficient.

[0051] In this embodiment, the three-dimensional incident wave refers to the propagation direction of the electromagnetic wave in three-dimensional space. It can be represented by a spherical coordinate system or a Cartesian coordinate system. A spatial geometric transformation algorithm can be used to convert the azimuth angle to the three-dimensional incident wave direction. The purpose is to ensure that the interference azimuth angle information is strictly matched with the actual physical layout of the antenna array.

[0052] Path difference refers to the phase difference caused by the difference in path length when electromagnetic waves arrive at different antenna elements. It can be calculated using geometric optics principles or electromagnetic field theory. In practical applications, ray tracing algorithms or finite element analysis methods can be used to achieve accurate path difference calculation, with the aim of eliminating errors caused by planar array approximations.

[0053] Phase delay refers to the phase change of an electromagnetic wave during propagation due to path differences, and it can be described by the electromagnetic wave propagation equation. In practical applications, the Fast Fourier Transform algorithm or digital signal processing techniques can be used to accurately calculate the phase delay, with the aim of ensuring the accuracy of phase information.

[0054] Complex phase factors refer to phase information expressed in complex form, which can be expressed using Euler's formula. In practical applications, complex number operations can be implemented using digital signal processors or application-specific integrated circuits (ASICs) to maintain the linearity of signal processing.

[0055] By establishing a complete three-dimensional spatial geometric model, precise quantification of the directional suppression coefficient was achieved. First, the azimuth angles of each interference source were obtained, providing the basic input for subsequent direction conversion and ensuring that the interference azimuth angle information was consistent with the actual environment. Next, by obtaining the three-dimensional spatial coordinates of all target antenna arrays in the programmable antenna array within a preset coordinate system, an accurate physical layout model was constructed, enabling the path difference calculation to be based on the actual antenna distribution rather than idealized assumptions.

[0056] By converting the azimuth angle into a three-dimensional incident wave direction, the location of the interference source is mapped to the array's dedicated coordinate system, ensuring a strict match between the directional information and the antenna's spatial position. Subsequently, the path difference is calculated based on the three-dimensional spatial coordinates and the incident wave direction, quantifying the spatial differences in the signal propagation path, and the phase delay is calculated accordingly. The physical path difference is then converted into a phase change using the principles of electromagnetic wave propagation.

[0057] By converting the phase delay into a complex phase factor and combining these factors to form the array steering vector, the response characteristics of the array to specific interference directions are accurately described. The beamforming weight vector and the array steering vector are then multiplied to directly evaluate the suppression effect of the current weight configuration on each interference source. Finally, by calculating the ratio of interference gain to reference gain and combining it with interference source strength data for a weighted average, a directional suppression coefficient that truly reflects the dominant role of strong interference sources is obtained.

[0058] Combined with the process of obtaining interference characteristic data and beamforming weight matrix described above, this method solves the evaluation bias problem in traditional evaluation methods by considering the physical layout of the antenna array and the differences in the intensity of interference sources, thus providing a reliable basis for subsequent interference suppression decisions.

[0059] Furthermore, the corresponding interference suppression mode parameters are calculated, specifically including: obtaining the preset minimum suppression gain coefficient and the power consumption coefficient corresponding to the preset terminal interference suppression algorithm; constructing an objective function with the maximum interference suppression level and the minimum terminal power consumption level as objective functions, resulting in the interference suppression level objective function and the terminal power consumption level objective function, wherein the interference suppression level objective function is expressed as: ,in, For interference suppression level, This is the directional suppression coefficient. The minimum suppression gain coefficient is preset; the objective function for terminal power consumption level is expressed as: ,in, For terminal power consumption level, The power consumption coefficient is... Let be the ratio of the terminal's current battery level to its full battery level; the final optimization objective is to minimize the overall cost function. ,in, To preset the first weight, To preset the second weight, The minimized comprehensive cost obtained from the solution is used as the interference suppression mode parameter.

[0060] In this embodiment, in scenarios with high network load and severe interference, α>β should be set to prioritize ensuring communication quality; in scenarios where terminal battery power is generally low, α<β should be set to prioritize extending terminal battery life.

[0061] The minimum suppression gain coefficient Gr is a system-level performance benchmark, typically set as the minimum interference suppression gain required to meet basic service needs. The power consumption coefficient Pa, obtained through laboratory measurements, represents the proportion of additional power consumption relative to standby state when the terminal executes different interference suppression algorithms. By introducing Gr and Pa, the interference suppression level Fpr and the terminal power consumption level Fpo become dimensionless relative values, allowing for weighted summation within a unified cost function J.

[0062] The interference suppression level objective function refers to the technical means of quantifying the interference suppression effect through mathematical expressions. It can be achieved by methods such as linear weighting and nonlinear mapping.

[0063] The purpose of this function is to dynamically correlate the directional suppression coefficient fed back by the base station with the minimum suppression gain coefficient, thereby flexibly adjusting the terminal's suppression strategy under different interference environments. The terminal power consumption level objective function refers to the technical means of evaluating terminal energy consumption through a mathematical model.

[0064] The purpose of this function is to dynamically adjust the power consumption of the suppression algorithm based on the real-time battery status of the terminal, avoiding excessive power consumption that could affect battery life. The comprehensive cost function refers to the technical means of integrating interference suppression and power control through a multi-objective optimization framework, which can be implemented using methods such as weighted summation and Pareto optimality. The core objective of this function is to find the optimal balance between performance and energy consumption, ensuring that the minimum suppression requirements are met while minimizing the negative impact on the terminal's battery life.

[0065] A multi-objective optimization framework was constructed to achieve a synergistic balance between interference suppression performance and terminal power consumption. First, the preset minimum suppression gain coefficient and the power consumption coefficient corresponding to the preset terminal interference suppression algorithm were obtained; these parameters provide key benchmarks for the optimization process. Next, an interference suppression level objective function was designed based on the directional suppression coefficient and the minimum suppression gain coefficient. This function can dynamically adjust the terminal's suppression strength according to the real-time interference status fed back by the base station. Simultaneously, a terminal power consumption level objective function was constructed using the terminal's current battery level and power consumption coefficient. This function can select an appropriate suppression algorithm based on the terminal's battery pressure.

[0066] Ultimately, a comprehensive cost function organically combines the costs of insufficient interference suppression with power consumption, and the optimal trade-off point is determined by minimizing this function. This process not only considers the residual interference level at the base station but also fully incorporates the real-time power status of the terminal, thereby achieving real-time coordinated optimization of performance and energy consumption. Combined with other technical features in the aforementioned adaptive signal processing method for wireless communication, it can more effectively address the challenges of limited terminal resources and dynamically changing interference in ultra-dense scenarios.

[0067] Furthermore, the interference suppression mode parameters are encoded into a downlink reference signal according to a preset first rule, specifically including: obtaining the corresponding pilot sequence configuration based on a preset mapping relationship between the interference suppression mode parameters and the pilot sequence configuration parameters, according to the minimized comprehensive cost mapping; the pilot sequence configuration includes at least one of the following parameters: the cyclic shift value of the pilot sequence, the initialization seed value of the pilot sequence, and the generator polynomial index of the pilot sequence; obtaining the corresponding subcarrier allocation scheme based on a preset mapping relationship between the interference suppression mode parameters and the subcarrier allocation mode, according to the minimized comprehensive cost mapping; the subcarrier allocation scheme includes at least one of the following configurations: a predefined subcarrier position combination, a subcarrier power distribution mode, and a subcarrier phase rotation mode; during the physical layer baseband processing of the terminal, the following encoding operations are performed: generating pilot symbols according to the determined pilot sequence configuration; mapping the pilot symbols to the corresponding subcarrier positions according to the determined subcarrier allocation scheme; applying corresponding power adjustment and phase adjustment to the mapped subcarrier signals; generating a complete downlink reference signal waveform containing interference suppression command information.

[0068] like Figure 2The diagram shown is a logical flow diagram of the wireless communication adaptive signal processing method provided in an embodiment of this application.

[0069] In this embodiment, the predefined minimum comprehensive cost refers to the core parameters used to guide the coding process, calculated through an optimization algorithm.

[0070] It can be achieved in various ways, such as using a linear weighted model or a nonlinear function to construct a formula for calculating the comprehensive cost, with the aim of ensuring that the coding process can dynamically adapt to different interference environments.

[0071] Pilot sequence configuration parameters can be understood as a set of basic parameters used to generate pilot symbols, which may include, but are not limited to, cyclic shift values, initialization seed values, or generation polynomial indices. The selection of these parameters aims to improve the signal's anti-interference capability in complex channels.

[0072] Subcarrier allocation mode refers to the strategy of rationally dividing frequency domain resources. It can be achieved through predefined position combinations, power distribution modes or phase rotation modes, with the aim of optimizing spectrum utilization efficiency and avoiding high interference frequency bands.

[0073] By constructing a parameter-driven intelligent coding mechanism, the adaptability problem of interference suppression command transmission under dynamic interference environments is effectively solved. Based on the predefined mapping relationship between the minimized comprehensive cost and the pilot sequence configuration parameters, the optimized parameters are directly transformed into physical layer executable pilot parameters. This design avoids the rigidity of traditional fixed coding and ensures that an adaptive pilot structure can be generated in real time when the interference environment changes rapidly.

[0074] Based on the predefined mapping relationship between the minimized overall cost and the subcarrier allocation mode, parameter information is accurately mapped to frequency domain resource allocation. By flexibly selecting subcarrier position combinations, power distribution modes, or phase rotation modes, signal transmission can actively avoid high interference frequency bands and optimize energy distribution.

[0075] In the terminal physical layer baseband processing, pilot symbols are generated according to a determined pilot sequence and mapped to low-interference subcarrier regions, avoiding the resource waste caused by traditional uniform allocation.

[0076] By applying power and phase adjustments to the mapped subcarrier signals, distortion effects during channel propagation are further compensated, maintaining the integrity of the signal waveform. The final generated downlink reference signal waveform integrates all optimization steps, enabling the terminal to efficiently extract parameters and configure interference suppressors, significantly shortening the response time from command transmission to execution.

[0077] Furthermore, the joint interference suppression coefficient is calculated, specifically including: after the terminal interference suppressor is working, measuring the received signal interference power before and after configuration; based on the measured interference power change value, calculating the joint interference suppression coefficient, which is determined by: calculating the power difference between the interference power before configuration and the interference power after configuration; performing a ratio operation between the power difference and the interference power before configuration; and using the ratio operation result as the joint interference suppression coefficient.

[0078] In this embodiment, the received signal interference power refers to the power intensity of the interference component in the signal received by the terminal, which can be achieved through the power detection module built into the terminal or a dedicated interference measurement unit.

[0079] The change in interference power can be understood as the difference in interference power before and after the terminal interference suppressor is activated, and its purpose is to reflect the actual working effect of the interference suppressor.

[0080] The joint interference suppression coefficient is a key indicator for measuring the interference suppression effectiveness of a terminal. Its calculation process first obtains the absolute reduction of interference power through difference calculation, and then converts it into a relative suppression rate through ratio calculation, thereby eliminating the influence of signal strength fluctuations and ensuring that the coefficient has environmental adaptability and standardization characteristics.

[0081] By establishing a real-time quantification mechanism for terminal interference suppression effectiveness, the problem of lacking feedback on the actual suppression performance at the terminal side is effectively solved. In the specific implementation process, interference power data of the received signal is collected before and after the terminal interference suppressor is activated. This directly captures the state changes before and after the interference suppressor is activated, avoiding potential biases caused by relying solely on theoretical models or base station-side estimations. Based on the collected interference power data, the power difference between the interference power before and after configuration is calculated, accurately reflecting the absolute reduction in interference power and demonstrating the actual working effectiveness of the interference suppressor.

[0082] By comparing the power difference with the interference power before configuration, the absolute change is converted into a relative suppression rate. This processing method not only eliminates the influence of absolute signal strength fluctuations, but also makes the resulting joint interference suppression coefficient environmentally adaptable and standardized.

[0083] Finally, the ratio calculation result is used as the joint interference suppression coefficient, providing an objective and quantifiable indicator of the terminal interference suppression effect. This indicator is directly related to the actual operating state of the terminal and supports dynamic adjustment of beamforming weights based on the actual suppression effect, thereby improving the accuracy and closed-loop control capability of the overall interference suppression strategy. Furthermore, the introduction of the joint interference suppression coefficient enables a better evaluation of the terminal interference suppressor's performance and provides a reliable basis for subsequent beamforming weight adjustments, ultimately optimizing the overall performance of wireless communication.

[0084] Furthermore, the specific process of adjusting the weights in the beamforming weight matrix based on the difference between the joint interference suppression coefficient and the preset second threshold and the corresponding difference in the spatial position of the target antenna array is as follows: Obtain the three-dimensional spatial coordinates of the target antenna array in the programmable antenna array corresponding to each weight; re-extract the azimuth angles of each interference source contained in the interference feature data based on the three-dimensional spatial coordinates of the target antenna array in the programmable antenna array to obtain the direction of the three-dimensional incident wave of the target antenna array, which is the direction of the interference wave arriving at the target antenna array; obtain the preset desired signal direction of the target antenna array; and adjust the weights corresponding to the i-th target antenna array in the beamforming weight matrix. The corresponding weight adjustment amount The calculation formula is as follows: ,in, To adjust the step size globally, This represents the difference between the joint interference suppression coefficient and the preset second threshold. Indicates the preset desired signal direction angle. This represents the array steering vector component of the i-th target antenna array in the preset desired signal direction. This represents the angle of the i-th target antenna array in the direction of the incoming interference wave. Let represent the array steering vector component of the i-th target antenna array in the direction of the interference wave; add the corresponding weights of the target antenna array to the corresponding weight adjustment amounts one by one to obtain the adjusted weights and update the beamforming weight matrix to obtain the adjusted beamforming weight matrix.

[0085] In this embodiment, the target antenna array refers to the basic unit that constitutes the programmable antenna array, which can be implemented in different forms such as microstrip antenna, dipole antenna or slot antenna.

[0086] In practical applications, three-dimensional spatial position coordinates refer to the specific positioning information of the target antenna array in a preset coordinate system, which can be accurately measured by laser rangefinders, ultrasonic positioning, or satellite-based positioning devices.

[0087] The global adjustment step size is a dynamic parameter that can be implemented using a fixed value, an exponential decay function, or an adaptive adjustment mechanism, depending on the convergence speed requirements. The purpose is to ensure the stability and efficiency of the weight adjustment process.

[0088] The difference between the joint interference suppression coefficient and the preset second threshold quantifies the degree of deviation between the current interference suppression level and the expected target, and it is obtained through real-time monitoring and calculation.

[0089] The array steering vector component is a mathematical description that characterizes the response characteristics of an antenna array in a specific direction. It can be calculated using methods such as Fourier transform, spatial spectrum estimation, or beam scanning.

[0090] By obtaining the three-dimensional spatial coordinates of the target antenna array corresponding to each weight in the programmable antenna array, a precise geometric benchmark is provided for subsequent spatial difference analysis, effectively avoiding the global adjustment deviation caused by ignoring the positional distribution of the antenna array in traditional methods.

[0091] Based on this, the azimuth angle of the interference source is re-extracted from the interference feature data according to these three-dimensional spatial coordinates. This allows for the calculation of the direction of arrival of the interference wave from the unique perspective of each target antenna array. This is because the spatial position difference of the antenna array directly affects the incident path of the interference signal, thus ensuring that the interference direction identification is consistent with the actual physical layout. At the same time, obtaining the preset desired signal direction of the target antenna array provides a clear signal enhancement reference for weight adjustment, enabling the adjustment process to accurately distinguish the spatial characteristics of the desired signal and the interference.

[0092] When calculating the adjustment for each weight, the difference between the joint interference suppression coefficient and the preset second threshold, as well as the difference in the array steering vector components, are comprehensively considered. The difference reflects the degree of inadequacy in current interference suppression, while the difference in the steering vector components precisely characterizes the spatial relative relationship between the desired signal direction and the direction of incoming interference. This combined approach ensures that the weight adjustment responds to the actual requirements of suppression effectiveness while conforming to the spatial physical characteristics of the antenna array. Finally, by adding the weights and the adjustment amount to update the beamforming weight matrix, iterative optimization of the weights is achieved, enabling beamforming to adapt to spatial variations in the interference environment and significantly improving the accuracy and stability of interference suppression.

[0093] Furthermore, the specific calculation steps for the array steering vector components are as follows: Obtain the position vector corresponding to the three-dimensional spatial position coordinates of the i-th target antenna array in the preset coordinate system. The array steering vector component of the i-th target antenna array in the preset desired signal direction is calculated in the following way: ,in, Represents an exponential function. Represents the imaginary unit. Represents pi (π). Indicates the carrier wavelength of the base station signal. This represents a unit vector in the direction of the desired signal; the array steering vector component of the i-th target antenna array in the direction of the incoming interference wave is calculated as follows: ,in, Represents an exponential function. Represents the imaginary unit. Represents pi (π). Indicates the carrier wavelength of the base station signal. This represents the unit vector in the direction of the incoming interference wave.

[0094] In this embodiment, the position vector refers to the mathematical expression used to describe the specific position of the target antenna array in three-dimensional space. It can be implemented by using a high-precision positioning sensor or a parametric modeling method based on a known geometric layout, with the aim of ensuring the accurate digital representation of the physical layout of the antenna array.

[0095] In practical applications, the unit vector in the desired signal direction can be a unit vector defined by the direction angle and elevation angle. It can be realized by converting spherical coordinates to component representation in rectangular coordinates. The purpose is to accurately quantify the path difference caused by the difference in signal propagation path.

[0096] The unit vector in the direction of the incoming interference wave can also be achieved in a similar way, aiming to distinguish the spatial orientation characteristics of different interference sources and avoid interference suppression failure due to direction confusion.

[0097] By establishing a precise physical model in a three-dimensional spatial coordinate system, the geometric mismatch problem in the calculation of array steering vector components is solved. The position vectors corresponding to the three-dimensional spatial coordinates of each target antenna array in the preset coordinate system are obtained. This process ensures an accurate digital representation of the antenna array's physical layout, avoiding the loss of spatial position information caused by simplification to a two-dimensional plane in traditional methods, and providing a reliable geometric basis for subsequent phase calculations. For the calculation of array steering vector components in the preset desired signal direction, the array steering vector components are generated based on the dot product relationship between the position vector and the unit vector of the desired signal direction. This dot product operation accurately quantifies the path difference caused by differences in signal propagation paths. Combined with the carrier wavelength and the imaginary characteristics of the exponential function, the physical process of phase delay in the desired signal direction can be realistically reproduced, thereby ensuring that beamforming accurately points to the target area.

[0098] Similarly, in the calculation of the component of the direction of interference wave, the array steering vector component is generated by the dot product of the position vector and the unit vector of the interference direction. This orientation modeling based on three-dimensional spatial coordinates can distinguish the spatial orientation characteristics of different interference sources and avoid interference suppression failure due to direction confusion.

[0099] The carrier wavelength and imaginary unit introduced in the formula strictly follow the laws of electromagnetic wave propagation, ensuring the matching between phase calculation and actual physical environment, thereby supporting the accurate execution of weight adjustment and ultimately improving the coordinated stability of beamforming and interference suppression under dynamic interference environment.

[0100] Furthermore, the specific process for obtaining the global adjustment step size is as follows: If the iteration number of the current beamforming weight matrix is ​​less than or equal to one, the global adjustment step size adopts a preset initial step size; if the iteration number of the current beamforming weight matrix is ​​greater than one, the current beamforming weight matrix and the beamforming weight matrix of the previous iteration are obtained, and the difference matrix between the two is calculated accordingly. The arithmetic mean of the absolute values ​​of all elements in the difference matrix is ​​then calculated to obtain the current weight change mean; the weight change mean of the previous iteration is obtained. If the current weight change mean is greater than a preset first multiple of the previous weight change mean, the current step size is multiplied by a preset first attenuation factor and used as the global adjustment step size for the next iteration; if the current weight change mean is less than a preset second multiple of the previous weight change mean, the current step size is multiplied by a preset first gain factor; otherwise, the current global adjustment step size remains unchanged.

[0101] In this embodiment, the global adjustment step size refers to the step size parameter that is dynamically adjusted during the iteration of the beamforming weight matrix, which can be implemented using an adaptive algorithm based on historical iteration data.

[0102] The difference matrix is ​​a matrix generated by comparing the beamforming weight matrix of the current iteration with that of the previous iteration. Its purpose is to quantify the magnitude of weight changes, thereby providing a basis for step size adjustment.

[0103] The average weight change can be understood as the arithmetic mean of the absolute values ​​of all elements in the difference matrix. It can be obtained through simple mathematical operations and is intended to reflect the overall trend of weight fluctuations.

[0104] The preset first multiple and preset second multiple are threshold parameters used to determine whether the average change in weights deviates significantly from historical data. The preset first multiple is usually set to a value slightly greater than 1, such as 1.1, to detect whether the change in weights is diverging; the preset second multiple is usually set to a value slightly less than 1, such as 0.9, to detect whether the change in weights tends to stagnate.

[0105] A preset first attenuation factor is used to reduce the step size and enhance stability when the system may oscillate.

[0106] A preset first gain factor is used to increase the step size and accelerate the convergence process when the system converges slowly. The specific values ​​of these factors can be determined through offline simulation optimization during system initialization.

[0107] The optimization of the beamforming weight matrix iteration process is achieved through a dynamic global step size adjustment mechanism. In the initial stage, when the number of iterations is less than or equal to one, a preset initial step size is used to ensure rapid entry into the optimization state and avoid slow response due to an excessively small step size. As the number of iterations increases, the average weight change is calculated based on the difference matrix between the current and previous weight matrices. This operation provides an objective basis for step size adjustment by quantifying the weight fluctuation amplitude in historical iterations.

[0108] Based on a comparison between the current average weight change and the previous average weight change, a differentiated adjustment is implemented: if the current average weight change increases significantly, it is multiplied by a decay factor to suppress oscillations caused by sudden weight changes; if the current average weight change decreases significantly, it is multiplied by a gain factor to accelerate the convergence process; if the average weight change is stable, the step size remains unchanged to maintain the continuity of the iteration process. This entire mechanism, by closely linking historical iteration data with real-time trends, ensures that the step size adjustment precisely matches the dynamic needs of weight optimization, fundamentally solving the problem of insufficient adaptability of fixed step size strategies in complex interference environments.

[0109] Closely integrated with the beamforming weight matrix adjustment process in the aforementioned adaptive signal processing method for wireless communication, this method improves the convergence efficiency and stability of interference suppression optimization through a dynamic step size adjustment mechanism, and shows significant advantages, especially in dealing with dynamic interference environments in ultra-dense user scenarios.

[0110] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, are implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0115] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A wireless communication adaptive signal processing method, characterized in that, Includes the following steps: It receives uplink signals from base stations in the target area and processes them to obtain interference characteristic data; it also receives and processes the status information of terminals in the target area to obtain terminal status data. The beamforming weight matrix is ​​obtained by processing the interference feature data using the linear constrained minimum variance algorithm, and then the beamforming weight matrix is ​​loaded into the corresponding base station. The directional suppression coefficient fed back by the base station is obtained. The directional suppression coefficient is calculated by the base station by analyzing the beamforming weight matrix and configuring the programmable antenna array of the target area. If the directional suppression coefficient does not exceed the preset first threshold, the corresponding interference suppression mode parameters are calculated based on the terminal status data and the directional suppression coefficient. The interference suppression mode parameters are encoded into downlink reference signals according to preset rules and sent to the corresponding terminals in the target area. The joint interference suppression coefficient fed back by the terminal is obtained, which is calculated by the terminal parsing the downlink reference signal; If the joint interference suppression coefficient exceeds a preset second threshold, the beamforming weight matrix is ​​adjusted according to the difference between the joint interference suppression coefficient and the preset second threshold.

2. The wireless communication adaptive signal processing method according to claim 1, characterized in that, The beamforming weight matrix is ​​obtained by processing the interference feature data using a linearly constrained minimum variance algorithm, specifically including: The programmable antenna array can be divided into several target antenna arrays, and the position vector of the target antenna array can be obtained in a preset coordinate system with the geometric center of the programmable antenna array as the reference point. Obtain the carrier wavelength of the base station signal; From the interference feature data, the interference azimuth angle of each interference source is extracted and combined with the position vector of the target antenna array in the programmable antenna array and the carrier wavelength of the base station signal. The array steering vector of the interference azimuth angle is calculated according to the planar wavefront model. Combine the array steering vectors of the interference azimuth angle to construct a linear constraint matrix; Obtain the preset expected response vector, where each element corresponds to a constraint response value of the interference azimuth angle; Construct a Capon beamforming optimization problem with the objective function of minimizing the array output variance and the constraint that the product of the conjugate transpose of the linear constraint matrix and the complex weight vector equals the desired response vector. The Lagrange multiplier method is used to solve the Capon beamforming optimization problem to obtain the optimal complex weight vector; The optimal complex weight vector is configured as a beamforming weight matrix, wherein each weight in the beamforming weight matrix uniquely corresponds to a target antenna array in the programmable antenna array.

3. The wireless communication adaptive signal processing method according to claim 2, characterized in that, The programmable antenna array configured for the target region specifically includes: The beamforming weight matrix is ​​decomposed into amplitude adjustment coefficients and phase adjustment coefficients, where the amplitude adjustment coefficients control the power of the transmitted signal of each target antenna array, and the phase adjustment coefficients control the phase of the transmitted signal of each target antenna array. The baseband digital beamformer at the base station precodes the transmitted signal of each antenna link. The precoded transmit signal is adjusted in the analog domain using the radio frequency phase shifter and attenuator of the base station, where the phase shifter adjusts the signal phase and the attenuator adjusts the signal amplitude. The adjusted precoded transmit signal is configured into the target antenna array of the corresponding programmable antenna array.

4. The wireless communication adaptive signal processing method according to claim 2, characterized in that, The specific process for calculating the directional inhibition coefficient is as follows: Obtain the azimuth angle of the interference and the three-dimensional coordinates of the target antenna array in the preset coordinate system; Based on the interference azimuth angle and the preset coordinate system, the interference azimuth angle is converted into the direction of the three-dimensional incident wave relative to the target antenna array; The path difference between the three-dimensional incident wave reaching each of the target antenna arrays and reaching the phase center of the programmable antenna array is calculated based on the three-dimensional coordinates of the target antenna array and the direction of the three-dimensional incident wave. Based on the path difference and the base station signal carrier wavelength, the phase delay generated by the incident wave on each target antenna array is calculated; Convert the phase delay on each target antenna array into a complex phase factor; The complex phase factors corresponding to all target antenna arrays are combined to form the array steering vector for the interference azimuth angle; The beamforming weight vector is multiplied by the array steering vector at each interference azimuth angle to obtain the array response value at each interference azimuth angle. The array response gain for each interference azimuth angle is obtained by taking the square of the modulus of the array response value. Obtain and calculate the array response gain in the desired signal direction as a reference gain; Calculate the ratio of the array response gain to the reference gain for each interference azimuth angle; The interference source intensity data corresponding to each interference azimuth angle is extracted from the interference feature data and normalized to obtain the weight coefficient of each interference source. Based on the aforementioned weighting coefficients, the gain ratios of each interference azimuth angle are weighted and averaged to obtain the directional suppression coefficient.

5. The wireless communication adaptive signal processing method according to claim 1, characterized in that, The corresponding interference suppression mode parameters are calculated, including: Obtain the preset minimum suppression gain coefficient and the power consumption coefficient corresponding to the preset terminal interference suppression algorithm; An objective function is constructed with the goal of maximizing the interference suppression level and minimizing the terminal power consumption level. This yields the objective function for both the interference suppression level and the terminal power consumption level. The objective function for the interference suppression level is expressed as follows: ,in, For interference suppression level, This is the directional suppression coefficient. The preset minimum suppression gain coefficient; The objective function for the terminal power consumption level is expressed as: ,in, For terminal power consumption level, The power consumption coefficient is... This is the ratio of the terminal's current battery level to its full battery level. The ultimate optimization objective is to minimize the overall cost function: ,in, To preset the first weight, To preset the second weight, As a comprehensive cost; The minimized comprehensive cost obtained from the solution is used as the interference suppression mode parameter.

6. The wireless communication adaptive signal processing method according to claim 5, characterized in that, The interference suppression mode parameters are encoded into downlink reference signals according to a preset first rule, specifically including: Based on the preset mapping relationship between interference suppression mode parameter valence and pilot sequence configuration parameters, the corresponding pilot sequence configuration is obtained according to the minimized comprehensive cost mapping; The pilot sequence configuration includes at least one of the following parameters: the cyclic shift value of the pilot sequence, the initialization seed value of the pilot sequence, and the generator polynomial index of the pilot sequence; Based on the preset mapping relationship between interference suppression mode parameters and subcarrier allocation mode, the corresponding subcarrier allocation scheme is obtained according to the minimized comprehensive cost mapping. The subcarrier allocation scheme includes at least one of the following configurations: predefined subcarrier position combinations, subcarrier power distribution patterns, and subcarrier phase rotation patterns; During the physical layer baseband processing of the terminal, the following encoding operations are performed: Pilot symbols are generated according to the determined pilot sequence; Map the pilot symbols to the corresponding subcarrier positions according to the determined subcarrier allocation scheme; Apply appropriate power and phase adjustments to the mapped subcarrier signals; Generate a complete downlink reference signal waveform that includes interference suppression command information.

7. The wireless communication adaptive signal processing method according to claim 6, characterized in that, The joint interference suppression coefficient was calculated, specifically including: After the terminal interference suppressor is activated, the interference power of the received signal is measured before and after the configuration. Based on the measured interference power change, the joint interference suppression coefficient is calculated, and the joint interference suppression coefficient is determined in the following way: Calculate the power difference between the interference power before and after configuration; The power difference is compared with the interference power before configuration. The result of the ratio calculation is used as the joint interference suppression coefficient.

8. The wireless communication adaptive signal processing method according to claim 4, characterized in that, The specific process of adjusting the weights in the beamforming weight matrix based on the difference between the joint interference suppression coefficient and the preset second threshold and the difference in the spatial position of the corresponding target antenna array is as follows: Obtain the three-dimensional spatial coordinates of the target antenna array corresponding to each weight in the programmable antenna array; Based on the three-dimensional spatial coordinates of the target antenna array in the programmable antenna array, the azimuth angles of each interference source contained in the interference feature data are re-extracted to obtain the direction of the three-dimensional incident wave of the target antenna array, which is the direction of the interference wave of the target antenna array. Obtain the preset desired signal direction of the target antenna array; The weights corresponding to the i-th target antenna array in the beamforming weight matrix The corresponding weight adjustment amount The calculation formula is as follows: ,in, To adjust the step size globally, This represents the difference between the joint interference suppression coefficient and the preset second threshold. Indicates the preset desired signal direction angle. This represents the array steering vector component of the i-th target antenna array in the preset desired signal direction. This represents the angle of the i-th target antenna array in the direction of the incoming interference wave. This represents the array steering vector component of the i-th target antenna array in the direction of the incoming interference wave; The weights corresponding to the target antenna array are added one by one with the corresponding weight adjustment amounts to obtain the adjusted weights, and the beamforming weight matrix is ​​updated to obtain the adjusted beamforming weight matrix.

9. The wireless communication adaptive signal processing method according to claim 8, characterized in that, The specific calculation steps for the array steering vector components are as follows: Obtain the position vector corresponding to the three-dimensional spatial coordinates of the i-th target antenna array in the preset coordinate system. ; The array steering vector component of the i-th target antenna array in the preset desired signal direction is calculated in the following way: ,in, Represents an exponential function. Represents the imaginary unit. Represents pi (π). Indicates the carrier wavelength of the base station signal. Represents a unit vector in the direction of the preset desired signal; The array steering vector component of the i-th target antenna array in the direction of the incoming interference wave is calculated in the following way: ,in, Represents an exponential function. Represents the imaginary unit. Represents pi (π). Indicates the carrier wavelength of the base station signal. This represents the unit vector in the direction of the incoming interference wave.

10. The wireless communication adaptive signal processing method according to claim 8, characterized in that, The specific process for obtaining the global adjustment step size is as follows: If the number of iterations of the current beamforming weight matrix is ​​less than or equal to one, the global adjustment step size adopts the preset initial step size; If the number of iterations of the current beamforming weight matrix is ​​greater than one, then obtain the current beamforming weight matrix and the beamforming weight matrix of the previous iteration, calculate the difference matrix between the two, and take the arithmetic mean of the absolute values ​​of all elements in the difference matrix to obtain the average change of the current weight. Obtain the average weight change of the previous iteration. If the current average weight change is greater than the preset first multiple of the average weight change of the previous iteration, multiply the current step size by the preset first decay factor and use it as the global adjustment step size for the next iteration. If the current average weight change is less than the second preset multiple of the previous average weight change, then the current step size is multiplied by the first preset gain factor. Otherwise, keep the current global adjustment step size unchanged.

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