A channel state based communication resource allocation system

Abstract

The application discloses a communication resource allocation system based on a channel state, belongs to the technical field of wireless communication, and relates to a dielectric pollution evaluation module, a structural coherence evaluation module, a hardware stress evaluation module and a synergy evaluation module.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and particularly relates to a communication resource allocation system based on channel state. Background Technology

[0002] In wireless communication systems, resource allocation based on channel state information is a core means to improve spectral efficiency and user service quality. Existing methods mainly rely on conventional channel parameters such as channel quality indications, signal-to-noise ratio, and Doppler shift fed back by user terminals, based on which the scheduler dynamically adjusts subcarriers, power, and modulation and coding schemes. However, such methods implicitly assume that the communication equipment's own radio frequency front-end is always in an ideal operating state, and that the erosion of the equipment's hardware performance by the external environment can be ignored.

[0003] In real-world deployment environments, communication nodes are constantly exposed to complex and ever-changing physical and electromagnetic conditions. Numerous hidden factors gradually degrade the quality of the radio frequency link through physical effects, thereby indirectly altering the equivalent channel state. For example, rain film buildup and atmospheric particulate matter adhesion on the radome surface can change the dielectric constant of the antenna aperture, leading to beam pointing deviation and deterioration of return loss. Power frequency vibration and wind-induced swaying of base station towers can introduce micro-Doppler modulation, disrupting the phase coherence of large-scale antenna arrays. DC power supply ripple and accumulated electrostatic discharge damage can cause irreversible degradation of the linearity of power amplifiers and the noise figure of low-noise amplifiers. High-altitude, low-pressure environments further reduce convective heat dissipation efficiency, exacerbating junction temperature rise and isolation drift in devices.

[0004] While these factors are not directly reflected in the conventional channel sounding reference signal, they constitute a hidden bottleneck restricting the gain of high-order modulation and spatial multiplexing. In particular, the local oscillator phase noise tolerance threshold directly determines the highest modulation and coding scheme level the system can support. Traditional resource allocation architectures fail to establish a closed-loop mapping from environmental physical quantities to local oscillator performance requirements. This leads to the scheduler blindly pursuing high spectral efficiency even when hardware conditions deteriorate or environmental stresses intensify, causing bit error rate spikes and retransmission storms, ultimately reducing system throughput.

[0005] Therefore, there is an urgent need for a communication resource allocation system that can integrate multi-dimensional environmental perception information, dynamically quantify the stress levels of hardware and propagation environment, and optimize the local oscillator phase noise tolerance threshold accordingly, so as to achieve cross-layer adaptive adjustment from physical environment to scheduling decision. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a communication resource allocation system based on channel state, which solves the aforementioned problems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a communication resource allocation system based on channel state, comprising: The dielectric contamination assessment module obtains the dielectric contamination coefficient based on the rain film thickness on the radome surface, the concentration of inhalable particulate matter in the atmosphere, and the insulation resistance of the printed circuit board surface through a dielectric contamination model. The structural coherence assessment module obtains the structural coherence failure coefficient based on the peak-to-peak value of the electric tunable antenna drive motor current ripple, the effective value of vibration acceleration at a specific frequency on the equipment backplane, and the instantaneous amplitude of the geomagnetic azimuth angle through a structural coherence failure model. The hardware stress assessment module obtains the hardware stress aging coefficient based on the peak-to-peak value of high-frequency noise voltage of DC power supply bus, electrostatic discharge event count rate and ambient atmospheric pressure through hardware stress aging model. The synergy assessment module, based on the dielectric contamination coefficient and hardware stress aging coefficient, combined with the power amplifier junction temperature and duplexer isolation temperature drift, obtains the RF front-end synergy degree through a synergy fit model. The threshold optimization module combines the current local oscillator phase noise tolerance threshold with the difference between the RF front-end cooperative adaptation degree and the target RF front-end cooperative adaptation degree, and uses the structural coherence destruction coefficient as an adjustment factor to obtain the optimized local oscillator phase noise tolerance threshold through the threshold optimization model.

[0008] Based on the above technical solutions, the present invention also provides the following optional technical solutions: Further technical solution: The dielectric contamination assessment model is configured as follows: Based on the ratio of the rain film thickness to a preset reference constant, the ratio of the particulate matter concentration to a preset reference concentration, and the normalization relationship of the insulation resistance to a preset half-value constant, a dielectric contamination coefficient with a value range of [0,1] is generated, where a larger value of this coefficient indicates that the electromagnetic wave transmission path is less affected by dielectric contamination.

[0009] A further technical solution: The structural coherence failure model is configured as follows: Based on the Gaussian attenuation relationship between the peak-to-peak value of the current ripple and the preset standard deviation of the current ripple, the effective value of the vibration acceleration and the preset standard deviation of the vibration acceleration, and the proportional relationship between the instantaneous amplitude of the geomagnetic azimuth angle and the preset reference value, a structural coherence disruption coefficient with a value range of [0,1] is generated. The larger the value of this coefficient, the more stable the antenna array structure and the better the beamforming phase coherence.

[0010] A further technical solution: The hardware stress aging model is configured as follows: Based on the exponential decay relationship between the peak-to-peak value of the noise voltage and the preset noise reference value, and the electrostatic discharge event count rate and the preset count rate reference value, and combined with the stress correction term determined by the pressure difference when the atmospheric pressure is determined to be lower than the preset reference pressure, a hardware stress aging coefficient with a value range of [0,1] is generated, wherein the larger the value of the coefficient, the smaller the electrical and thermodynamic stress on the RF hardware.

[0011] A further technical solution: The collaborative adaptation model is configured as follows: Based on the complementary values ​​of dielectric contamination coefficient and hardware stress aging coefficient, a weighted multidimensional deviation vector is constructed by combining the normalized temperature rise of the power amplifier junction temperature and the normalized slope of the duplexer isolation temperature drift. The Mahalanobis distance kernel function is calculated through a preset weighted diagonal matrix to generate an RF front-end cooperative fit degree with a value range of [0,1]. The larger the fit degree value, the better the comprehensive fit of the transmit link in the thermal, electrical and impedance matching dimensions.

[0012] A further technical solution: The threshold optimization model is configured as follows: Based on the current local oscillator phase noise tolerance threshold, and combined with the nonlinear incremental term determined by the ratio of the RF front-end cooperative fit to the preset target fit, and using the structural coherence destruction coefficient as the convergence speed adjustment factor, an optimized local oscillator phase noise tolerance threshold is generated within the preset maximum adjustment range.

[0013] A further technical solution: The optimized local oscillator phase noise tolerance threshold output by the threshold optimization module is configured to guide the media access control layer scheduler of the base station to determine the highest modulation and coding scheme level allowed in the downlink of the orthogonal frequency division multiple access system.

[0014] A communication device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the functions of each module in the above system.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the functions of the modules in the above system.

[0016] This invention provides a communication resource allocation system based on channel state, which has the following advantages compared with the prior art: This invention, by introducing a dielectric contamination assessment module, a structural coherence assessment module, and a hardware stress assessment module, achieves for the first time a quantitative perception of the degree of implicit degradation of the radio frequency front-end and propagation path of communication equipment, making up for the shortcomings of traditional channel state feedback mechanisms that only focus on electromagnetic propagation characteristics and ignore the health of the equipment itself.

[0017] This invention constructs a cooperative fit model based on the Mahalanobis distance kernel function, which integrates the deviations of four dimensions—dielectric contamination, hardware aging, power amplifier junction temperature, and duplexer isolation temperature drift—into a single cooperative fit index. This index can accurately characterize the comprehensive fit status of the transmit link in terms of thermal, electrical, and impedance matching, providing a highly sensitive and physically meaningful decision basis for subsequent threshold optimization.

[0018] This invention uses a threshold optimization model to combine the difference between the current cooperative fit and the preset target cooperative fit with the structural coherence destruction coefficient, dynamically generating an optimized local oscillator phase noise tolerance threshold. This allows the system to proactively relax the stringent requirements on local oscillator purity when hardware conditions deteriorate or antenna jitter intensifies, guiding the scheduler to select a more robust low-order modulation and coding scheme. This effectively avoids the spread of bit errors and waste of radio resources caused by link distortion and beam mismatch.

[0019] This invention directly configures the optimized local oscillator phase noise tolerance threshold in the media access control layer scheduler to constrain the highest modulation and coding scheme level allowed in the orthogonal frequency division multiple access downlink. It opens up a cross-layer closed-loop control link from environmental sensors and RF front-end monitoring to baseband resource scheduling decisions, significantly improving the adaptive capability and long-term operational reliability of the communication system in complex deployment environments. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0023] Please see Figure 1 According to one embodiment of the present invention, a communication resource allocation system based on channel state includes: The dielectric contamination assessment module obtains the dielectric contamination coefficient through a dielectric contamination model based on the thickness of the rain film on the radome surface, the concentration of inhalable particulate matter in the atmosphere (the mass concentration of particulate matter with an aerodynamic equivalent diameter of less than or equal to 10 micrometers suspended in the near-ground atmosphere), and the insulation resistance of the printed circuit board surface. The structural coherence assessment module, based on the peak-to-peak value of the ripple of the electrically tunable antenna drive motor current, the effective value of the vibration acceleration at a specific frequency (50Hz or 100Hz) of the equipment backplane, and the instantaneous amplitude of the geomagnetic azimuth angle, obtains the structural coherence failure coefficient through the structural coherence failure model. The hardware stress assessment module obtains the hardware stress aging coefficient through a hardware stress aging model based on the peak-to-peak value of the high-frequency noise voltage of the DC power supply bus, the electrostatic discharge event count rate (the number of electrostatic discharge pulses detected and successfully counted by the dedicated electrostatic discharge monitoring circuit located at the input port of the low-noise amplifier per unit time) and the ambient atmospheric pressure. The synergy assessment module, based on the dielectric contamination coefficient and hardware stress aging coefficient, combined with the power amplifier (PA) junction temperature and duplexer isolation temperature drift (the slope of the isolation curve as a function of temperature), obtains the RF front-end synergy degree through a synergy fit model. The threshold optimization module combines the current local oscillator phase noise tolerance threshold with the difference between the RF front-end cooperative adaptation degree and the target RF front-end cooperative adaptation degree, and uses the structural coherence destruction coefficient as an adjustment factor to obtain the optimized local oscillator phase noise tolerance threshold through the threshold optimization model.

[0024] The following example will provide a more detailed explanation of the above technical solution: Suppose a communication base station is deployed at a high-altitude, windy, and variable-climate location A. This base station operates for a long time, and user A reports unstable communication quality, especially with a significantly increased bit error rate under severe weather conditions, leading to data transmission interruptions or reduced speeds. Traditional resource allocation systems, relying solely on conventional channel parameters such as signal-to-noise ratio, cannot effectively solve this problem.

[0025] At this point, the communication resource allocation system of this embodiment begins to function. First, the dielectric contamination assessment module continuously monitors the system. When sandstorms occur, a large amount of dust accumulates on the surface of the radome. Simultaneously, humidity changes in high-altitude areas may cause a decrease in the surface insulation resistance of the printed circuit board. Based on these inputs, the dielectric contamination assessment module calculates a low dielectric contamination coefficient using a dielectric contamination model, indicating that the electromagnetic wave transmission path is significantly contaminated.

[0026] Meanwhile, the structural coherence assessment module was also operating. Location A was windy, causing minor vibrations to the base station tower and antenna array. The electrically tunable antenna drive motor might also generate current ripple due to long-term operation. Furthermore, the geomagnetic azimuth angle might experience instantaneous amplitude changes due to environmental factors. Based on this data, the structural coherence assessment module calculated a low structural coherence failure coefficient using a structural coherence failure model, indicating a decrease in the structural stability of the antenna array and an impact on beamforming phase coherence.

[0027] Next, the hardware stress assessment module detected that due to reduced heat dissipation efficiency caused by the high-altitude, low-pressure environment, and potential aging of the power module due to long-term operation, the peak-to-peak value of the high-frequency noise voltage on the DC power supply bus increased, and the electrostatic discharge event count rate may also increase. Based on these inputs, the hardware stress assessment module calculated a low hardware stress aging coefficient using a hardware stress aging model, indicating that the RF hardware is experiencing significant electrical and thermodynamic stress.

[0028] Subsequently, the synergy assessment module receives the dielectric contamination coefficient and hardware stress aging coefficient, and combines them with the power amplifier (PA) junction temperature (which may increase due to poor heat dissipation) and duplexer isolation temperature drift (which may increase due to temperature changes). Using a synergy fit model, the synergy assessment module comprehensively evaluates and arrives at a low RF front-end synergy fit, clearly indicating that the RF front-end is in poor condition across thermal, electrical, and impedance matching dimensions.

[0029] Finally, the threshold optimization module receives the current system-set local oscillator phase noise tolerance threshold and the RF front-end cooperative fit. This module compares the lower RF front-end cooperative fit with the preset target fit and finds a significant difference. Simultaneously, the module uses the structural coherence violation coefficient as an adjustment factor to correct the threshold adjustment range. Ultimately, the threshold optimization module calculates an optimized local oscillator phase noise tolerance threshold using a threshold optimization model, which is more lenient than the current threshold. This optimized threshold is passed to the communication resource allocator to guide it in selecting a more robust modulation and coding scheme, such as downgrading from 64-QAM to 16-QAM or QPSK.

[0030] Through the above process, even in harsh environments and under hardware degradation, the system can adaptively adjust the local oscillator phase noise tolerance threshold, thereby avoiding the increase in bit error rate and retransmission storm caused by blindly pursuing high spectral efficiency, and ensuring the stability of user A's communication service quality and system throughput.

[0031] The communication resource allocation system in this embodiment, by introducing a dielectric contamination assessment module, a structural coherence assessment module, and a hardware stress assessment module, achieves quantitative perception of implicit degradation factors in the physical environment and equipment hardware that cannot be reflected by traditional channel state information. For example, existing technologies typically only focus on macroscopic channel parameters such as signal-to-noise ratio, while this system can identify and quantify the impact of microscopic physical quantities such as radome rain film, particulate matter adhesion, equipment vibration, power supply noise, electrostatic discharge, and atmospheric pressure on the radio frequency link.

[0032] Furthermore, the collaborative assessment module integrates these multi-dimensional environmental and hardware status information to generate a unified RF front-end collaborative adaptability, providing a comprehensive RF front-end health indicator that surpasses the limitations of single indicators in existing technologies. For example, existing technologies may monitor PA junction temperature individually, but cannot collaboratively assess it with dielectric contamination or hardware stress.

[0033] Ultimately, the threshold optimization module dynamically optimizes the local oscillator phase noise tolerance threshold based on this cooperative fit and combined with the structural coherence violation coefficient as an adjustment factor. This mechanism enables the system to directly map changes in the physical environment to the requirements for local oscillator performance, thereby achieving cross-layer adaptive adjustment. Compared to existing technologies where the scheduler blindly pursues high spectral efficiency, this system can proactively adjust the modulation and coding scheme according to the actual carrying capacity of the RF front-end, effectively avoiding bit error rate spikes and retransmission storms, and significantly improving the system's robustness and reliability in complex and variable environments. By perceiving and quantifying latent degradation factors, this system achieves refined management of communication resource allocation, providing a new approach to improving the overall performance of wireless communication systems.

[0034] Preferably, the dielectric contamination assessment model is configured as follows: Based on the ratio of the rain film thickness to a preset reference constant, the ratio of the particulate matter concentration to a preset reference concentration, and the normalized relationship of the insulation resistance to a preset half-value constant, a dielectric contamination coefficient with a value range of [0,1] is generated. A larger value for this coefficient indicates that the electromagnetic wave transmission path is less affected by dielectric contamination. Specifically, the dielectric contamination assessment model is expressed as follows: in, The dielectric contamination coefficient is... This is a reference constant for rain film thickness. The thickness of the rain film on the radome surface. This is the reference concentration for particulate matter. This refers to the concentration of inhalable particulate matter in the atmosphere. It is the half-value constant of insulation resistance. The dielectric contamination coefficient is the output value range of the surface insulation resistance of the printed circuit board. Furthermore, a larger value indicates that the electromagnetic wave transmission path is less affected by dielectric contamination.

[0035] This dielectric contamination assessment model provides a method for quantitatively evaluating the degree of dielectric contamination along electromagnetic wave transmission paths. It integrates multiple environmental and material-specific parameters into a single coefficient. Among these, the rain film thickness on the radome surface measures the amount of water film accumulated on the radome surface. This water film directly affects dielectric properties and signal attenuation. This thickness can be measured in real-time by integrating a rain sensor or capacitive sensor onto the radome surface, or estimated based on local meteorological data (e.g., rainfall rate, humidity) combined with a predictive model. Atmospheric inhalable particulate matter concentration refers to the concentration of particulate matter in the atmosphere. These particles can accumulate on device surfaces, altering dielectric properties, especially at high frequencies. This concentration can be measured using an optical particle counter or gravity sampler, or obtained from environmental monitoring stations or satellite data. The insulation resistance of the printed circuit board (PCB) surface reflects the electrical insulation integrity of the PCB surface. It can deteriorate due to moisture, dust, or chemical contamination, leading to signal leakage or impedance mismatch. This resistance can be monitored using a dedicated insulation resistance tester or a leakage current sensor integrated on the PCB, or indirectly assessed by measuring changes in circuit performance related to insulation degradation (e.g., signal loss, noise level). The dielectric contamination coefficient is the output of the model. It is a normalized value that comprehensively reflects the overall dielectric contamination level. The larger the value, the lower the degree of contamination. This coefficient provides a unified metric for the system to understand the impact of the environment on signal transmission and serves as input for subsequent evaluation modules (such as the synergy evaluation module) to make informed resource allocation decisions.

[0036] The solution in this application, through the aforementioned dielectric contamination assessment model, enables the dielectric contamination assessment module to accurately quantify the combined impact of rain film thickness on the radome surface, atmospheric inhalable particulate matter concentration, and printed circuit board surface insulation resistance on dielectric contamination of the electromagnetic wave transmission path. The first exponential term in this model... This term is used to characterize the effect of rain film thickness on dielectric contamination; the thicker the rain film, the smaller this value, and the higher the degree of contamination. The second exponential term... This term is used to characterize the effect of atmospheric particulate matter concentration on dielectric pollution; the higher the particulate matter concentration, the smaller the value of this term. (The third term...) This term characterizes the impact of the surface insulation resistance of a printed circuit board on dielectric contamination; the lower the insulation resistance, the smaller the value. These three terms are combined in a product form, representing the combined, multiplicative effect of these independent contamination sources. The final output dielectric contamination coefficient is a normalized coefficient between 0 and 1, comprehensively reflecting the overall dielectric contamination state. Values ​​close to 1 indicate minimal contamination, while values ​​close to 0 indicate severe contamination. This precise quantification method enables communication resource allocation systems to more accurately assess channel quality, thereby making more reliable decisions in subsequent collaborative assessments and threshold optimization processes.

[0037] As a specific implementation, multiple sensors can be deployed in a communication base station to acquire the input parameters required for the model. For example, a thin-film rain sensor can be installed on the radome surface to measure the rain film thickness on the radome surface in real time; a miniature air quality monitor can be deployed near the base station to obtain the concentration of inhalable particulate matter in the atmosphere; simultaneously, a microelectrode array can be integrated on a critical radio frequency printed circuit board to indirectly calculate the surface insulation resistance of the printed circuit board by measuring its surface leakage current. The data collected by these sensors is transmitted to the central processing unit of the base station. The software module running in the central processing unit, based on preset reference constants... , and The current dielectric contamination coefficient is calculated using the aforementioned dielectric contamination assessment model. For example, when the rain film thickness is 0.3 mm, the particulate matter concentration is 30 μg / m³, and the insulation resistance is 5 GΩ, the system will calculate a specific value based on the model. Value. This calculated The value is then passed to the cooperability evaluation module as an important input parameter for evaluating the RF front-end cooperability. Among them, the reference constant... , and The model can be determined by statistical analysis (e.g., taking the average value) and calibration of long-term operating data of the communication system under different environmental conditions to ensure that the model can accurately reflect the actual dielectric contamination situation. It can also be determined by expert experience.

[0038] Through the above technical solution, this application can accurately quantify the combined effects of rain film thickness on the radome surface, concentration of inhalable particulate matter in the atmosphere, and insulation resistance on the printed circuit board surface on dielectric contamination of the electromagnetic wave transmission path. This refined dielectric contamination assessment provides more accurate and reliable channel state information for channel state-based communication resource allocation systems, thereby avoiding improper resource allocation caused by single or coarse assessments of dielectric contamination, and significantly improving the robustness and transmission efficiency of the communication system.

[0039] Preferably, the structural coherence failure model is configured as follows: Based on the Gaussian attenuation relationship between the peak-to-peak value of the current ripple and the preset standard deviation of the current ripple, the effective value of the vibration acceleration and the preset standard deviation of the vibration acceleration, and the proportional relationship between the instantaneous amplitude of the geomagnetic azimuth angle and the preset reference value, a structural coherence destruction coefficient with a value range of [0,1] is generated. The larger the value of this coefficient, the more stable the antenna array structure and the better the beamforming phase coherence. Specifically, the structural coherence failure model is expressed as follows: in, The structural coherence failure factor is... This represents the peak-to-peak value of the current ripple in the electrically tunable antenna drive motor. The standard deviation of current ripple. The effective value of vibration acceleration at a specific frequency (50Hz or 100Hz) for the equipment backplate. The standard deviation of vibration acceleration. This is a reference value for the geomagnetic azimuth. The instantaneous amplitude of the geomagnetic azimuth angle is given, and the output range of the structural coherence failure coefficient is given. Furthermore, a larger value indicates a more stable antenna array structure and better beamforming phase coherence.

[0040] This structural coherence violation model aims to quantify the structural stability of antenna arrays and their beamforming phase coherence. Its role is to provide a comprehensive index to reflect the physical health of the antenna array, which is crucial for ensuring accurate beamforming and reliable signal transmission. The model can be integrated as a software module into the processor of a communication resource allocation system to receive real-time data from various sensors and perform calculations; alternatively, it can be implemented through dedicated hardware accelerators, such as field-programmable gate arrays (FPGAs) or digital signal processors (DSPs), to meet the needs of real-time processing.

[0041] The peak-to-peak value of the current ripple in an electrically adjustable antenna drive motor refers to the maximum difference between the peak and valley values ​​of the ripple component in the current signal driving the electrically adjustable antenna motor. Its function is to reflect the smoothness of motor operation and the health of the drive system. Excessive current ripple may indicate motor wear, unstable control, or mechanical vibration. This peak-to-peak value can be obtained by connecting a current sensor in series in the motor drive circuit and performing high-frequency sampling and analysis of the sensor's output signal; alternatively, the current ripple characteristics can be indirectly calculated by monitoring the voltage changes across the motor windings and combining them with a motor model.

[0042] The standard deviation of current ripple is a statistical measure used to measure the dispersion of current ripple in an electrically adjustable antenna drive motor. Its function is to provide a reference benchmark for the peak-to-peak value of current ripple, making the evaluation of the peak-to-peak value of current ripple in an electrically adjustable antenna drive motor more relative and robust. This standard deviation can be a fixed value preset according to the motor type and design parameters; or it can be dynamically calculated and updated by statistical analysis of long-term operating data of the motor under normal operating conditions.

[0043] The effective value of vibration acceleration at a specific frequency (50Hz or 100Hz) on the equipment backplane refers to the root mean square (RMS) value of the vibration acceleration at a specific frequency (e.g., 50Hz or 100Hz, usually related to power supply harmonics or mechanical resonance) on the equipment backplane. Its function is to quantify the mechanical vibration level of the entire equipment (including antenna array). Excessive vibration may lead to a decrease in antenna pointing accuracy or structural fatigue. This effective value can be obtained by installing an acceleration sensor on the equipment backplane and performing specific frequency filtering and RMS calculation on the sensor output signal; alternatively, it can also be obtained by scanning the backplane with a non-contact laser vibrometer and performing spectrum analysis to extract the vibration components at specific frequencies.

[0044] The standard deviation of vibration acceleration is a statistical measure used to measure the dispersion of vibration acceleration on the back plate of equipment. Its function is to provide a reference benchmark for the effective value of vibration acceleration in order to assess the degree of abnormality of the current vibration level. The standard deviation can be a constant preset based on equipment design specifications or historical operating data; or it can be dynamically adjusted by performing real-time statistics on the vibration data of the equipment under different operating conditions.

[0045] The geomagnetic azimuth reference value refers to the preset or calibrated azimuth angle of the antenna array relative to the Earth's magnetic field. Its function is to provide a stable reference for calculations in the structural coherence failure model to evaluate the azimuth stability of the antenna array. This reference value can be set and stored during system installation or periodic calibration; or it can be obtained by averaging the geomagnetic azimuth angle during long-term stable operation of the antenna array.

[0046] The instantaneous amplitude of the geomagnetic azimuth refers to the geomagnetic azimuth measured in real time by the antenna array. Its function is to directly reflect the instantaneous angular position of the antenna array, which is crucial for maintaining beamforming accuracy. This instantaneous amplitude can be measured in real time by a magnetometer or inertial measurement unit (IMU) integrated on the antenna structure; or, the antenna attitude information can be obtained by high-precision sensors such as optical encoders or gyroscopes, and then the geomagnetic azimuth can be calculated.

[0047] This application's solution, by introducing a structural coherence failure model, further improves the comprehensive assessment of communication channel status based on dielectric contamination evaluation. This model collects key physical parameters such as the peak-to-peak value of the electric antenna drive motor current ripple, the effective value of vibration acceleration at a specific frequency on the equipment backplane, and the geomagnetic azimuth reference value, and combines these with the corresponding standard deviations. , The structural coherence failure factor is calculated using a pre-defined mathematical expression. Current ripple and vibration acceleration reflect the mechanical stability of the antenna array, while the geomagnetic azimuth represents the stability of the antenna's pointing direction. These parameters are integrated using exponential and reciprocal functions, enabling the model to sensitively capture minute mechanical and attitude changes in the antenna array. The output range of the structural coherence failure factor is [range missing]. A higher value indicates a more stable antenna array structure and better beamforming phase coherence. In this way, the system can quantify the physical health of the antenna array, providing more comprehensive and accurate input for subsequent coordination evaluation and threshold optimization modules. This ensures that communication resource allocation fully considers physical-level influences, thereby improving the robustness and performance of the entire communication system.

[0048] The following concrete example illustrates how the structural coherence failure model can be deployed on an embedded processor, such as a high-performance microcontroller or a dedicated signal processing chip. The peak-to-peak value of the current ripple of the electrically tunable antenna drive motor can be obtained by connecting a high-precision shunt resistor in series with the motor power line and sampling the voltage across the shunt resistor using a high-speed analog-to-digital converter (ADC). The peak-to-peak value of the current ripple is then extracted using a digital signal processing algorithm. The effective value of the vibration acceleration at a specific frequency on the device backplane can be acquired by a MEMS accelerometer mounted on the backplane. Its output signal is filtered to remove irrelevant frequency components using an analog filter, and then its effective value is calculated using an RMS-DC converter or digital signal processing. The instantaneous amplitude of the geomagnetic azimuth angle can be provided by a triaxial magnetoresistive sensor or Hall sensor array integrated on the antenna support. This sensor array measures the geomagnetic field vector in real time and calculates the azimuth angle of the antenna relative to the geomagnetic north pole using an attitude calculation algorithm. These real-time acquired physical quantities are input into the embedded processor, which calculates the structural coherence failure coefficient based on the mathematical expression of the structural coherence failure model.

[0049] Through the above technical solution, this application further introduces a quantitative assessment of the coherence of the antenna array structure, building upon existing dielectric contamination assessment methods. By comprehensively considering physical parameters such as the peak-to-peak value of the electric tunable antenna drive motor current ripple, the effective value of vibration acceleration at a specific frequency on the equipment backplane, and the geomagnetic azimuth reference value, the system can accurately capture potential problems in the antenna array regarding mechanical stability, vibration suppression, and pointing accuracy. This in-depth insight into the physical layer's health status allows the communication resource allocation system to more comprehensively understand the channel state, rather than being limited to environmental factors. For example, when the structural coherence violation coefficient is low, the system can identify potential problems such as mechanical loosening, motor performance degradation, or attitude drift in the antenna array, thereby enabling timely adjustments to beamforming strategies, reduction of transmission power to avoid signal distortion, or triggering maintenance warnings. This significantly improves the adaptability and reliability of the communication system in complex and dynamic environments, ensures the effective implementation of beamforming technology, and thus optimizes the efficiency of communication resource allocation and user experience.

[0050] Preferably, the hardware stress aging model is configured as follows: Based on the exponential decay relationship between the peak-to-peak noise voltage and the preset noise reference value, and the electrostatic discharge event count rate and the preset count rate reference value, and combined with the stress correction term determined by the pressure difference when the atmospheric pressure is determined to be lower than the preset reference pressure, a hardware stress aging coefficient with a value range of [0,1] is generated, wherein the larger the value of the coefficient, the smaller the electrical and thermodynamic stress on the radio frequency hardware. Specifically, the hardware stress aging model is expressed as follows: in, The stress aging coefficient of the hardware. Peak-to-peak value of high-frequency noise voltage for DC power supply bus. This is a reference value for noise voltage. This represents the electrostatic discharge event count rate. This is a reference value for the electrostatic discharge count rate. This refers to the ambient atmospheric pressure. For reference atmospheric pressure, For indicator functions, if but ,like but The output range of the hardware stress aging coefficient is as follows: Furthermore, a larger value indicates that the electrical and thermodynamic stress on the radio frequency hardware is smaller.

[0051] This hardware stress assessment module aims to monitor and quantify the electrical and thermodynamic stresses experienced by the internal hardware of a communication system. Its role is to provide critical data on hardware health to support more accurate system status assessments and resource allocation decisions. This module can be integrated into the communication equipment as a standalone hardware unit, for example, through a dedicated microcontroller or digital signal processor responsible for data acquisition and model calculation; alternatively, its function can run as a software component on the main control unit of the communication system, completing the assessment task by reading sensor data and executing corresponding algorithms. The peak-to-peak value of the high-frequency noise voltage on the DC power supply bus is an important indicator of the power supply quality within the communication equipment. An increase in high-frequency noise voltage usually indicates power module aging, decreased filtering performance, or the presence of external interference, all of which can adversely affect the stable operation of the RF circuit. This peak-to-peak value can be monitored in real time by connecting a high-bandwidth oscilloscope to the DC power supply bus, or sampled and quantified by a dedicated high-frequency noise detection circuit integrated into the power management unit. The electrostatic discharge (ESD) event count rate reflects the frequency with which communication equipment is subjected to ESD shocks per unit time. A high ESD event count rate indicates that the equipment may be in a high-static environment or its ESD protection capability is weakening, which may lead to performance degradation or even permanent damage to sensitive radio frequency devices. This count rate can be detected and counted in real time by deploying a dedicated ESD monitoring circuit near the input port of a low-noise amplifier. Ambient atmospheric pressure is an important environmental factor affecting the heat dissipation efficiency and thermal stress of internal components in communication equipment. Changes in atmospheric pressure affect air density, thereby altering convective heat dissipation, especially at high altitudes or in extreme weather conditions, which may cause internal temperatures to rise and accelerate hardware aging. This pressure can be measured in real time using miniature barometric pressure sensors integrated inside or outside the equipment, and the measured value is transmitted to the evaluation module. The hardware stress aging model is used to synthesize the above input parameters and quantify a hardware stress aging coefficient. The model uses mathematical expressions to weight and normalize the effects of peak-to-peak voltage of high-frequency noise on the DC power supply bus, electrostatic discharge event count rate, and ambient atmospheric pressure, thereby outputting a value between 0 and 1. The larger the value, the smaller the electrical and thermodynamic stress on the RF hardware, that is, the better the hardware health.

[0052] This application's solution utilizes a hardware stress assessment module to monitor three key parameters in real time: the peak-to-peak value of the high-frequency noise voltage of the DC power supply bus, the electrostatic discharge event count rate, and the ambient atmospheric pressure. This comprehensively captures the electrical and thermodynamic stress conditions faced by the internal hardware of the communication system. Specifically, the peak-to-peak value of the high-frequency noise voltage of the DC power supply bus directly reflects the stability and purity of the power supply system. Its increase often indicates performance degradation of power supply components or increased external interference, which directly affects the normal operation of the RF circuit. The electrostatic discharge event count rate quantifies the frequency of transient high-voltage impacts on the equipment; a high count rate means a higher risk of hardware damage. Ambient atmospheric pressure indirectly affects the heat dissipation efficiency and internal temperature distribution of the equipment. Especially in extreme environments, low atmospheric pressure may lead to poor heat dissipation and accelerate thermal stress aging. These raw data are input into a hardware stress aging model, which quantifies the impact of noise voltage and electrostatic discharge events using an exponential decay function. This ensures that the hardware stress aging coefficient decreases rapidly as these stresses increase, thus accurately reflecting the deterioration trend of hardware performance. Simultaneously, the model introduces an indicator function to additionally consider the impact of ambient atmospheric pressure below the reference baseline on hardware stress aging. This allows the model to more precisely capture the effects of environmental changes on hardware health. In this way, the hardware stress assessment module can comprehensively consider multiple stress sources to generate a unified hardware stress aging coefficient. This coefficient, together with the dielectric contamination coefficient and the structural coherence failure coefficient, constitutes a comprehensive assessment of the overall health of the communication system. This enables the system to consider not only the external environment and mechanical stability but also the actual health status of the internal hardware when allocating communication resources, thus making more informed and reliable decisions and effectively avoiding performance degradation or sudden failures caused by internal hardware vulnerabilities.

[0053] In one specific implementation, the hardware stress assessment module can be integrated into the baseband processing unit of the communication device. Specifically, the measurement of the peak-to-peak value of the high-frequency noise voltage of the DC power supply bus can be achieved by configuring a high-frequency sampling circuit at the output of the power management chip. This sampling circuit includes a high-pass filter and an analog-to-digital converter (ADC) to convert the analog noise voltage into a digital signal. The electrostatic discharge (ESD) event count rate can be obtained using a dedicated ESD monitoring circuit at the input of a low-noise amplifier (LNA). This circuit can identify and count transient ESD pulses and transmit the count results to the baseband processing unit via a serial interface. The ambient atmospheric pressure can be measured in real time using a microelectromechanical system (MEMS) barometric pressure sensor, which is typically integrated inside the device and communicates with the baseband processing unit via an I2C or SPI bus. After receiving these digitized measurement data, the baseband processing unit runs a hardware stress aging model in its firmware. For example, the firmware can preset noise voltage reference values, ESD count rate reference values, and reference atmospheric pressure. Upon receiving real-time measurement values, the firmware calculates based on the mathematical expression of the hardware stress aging model and ultimately outputs a hardware stress aging coefficient between 0 and 1. This coefficient can then be used by other modules in the system to comprehensively assess the overall health of the RF front end.

[0054] Through the above technical solution, this application effectively solves the problem of incomplete assessment and inaccurate decision-making caused by the failure to fully consider the internal electrical and thermodynamic stress of hardware when allocating resources in communication systems. The hardware stress assessment module introduces the peak-to-peak value of high-frequency noise voltage of the DC power supply bus, the electrostatic discharge event count rate, and the ambient atmospheric pressure as assessment criteria, and combines them with a hardware stress aging model to accurately quantify the health status of RF hardware. This allows the system to monitor the "sub-health" state of the hardware in real time, such as potential risks like increased power supply noise, frequent ESD events, or deterioration of the heat dissipation environment. By combining this hardware stress aging coefficient with external environmental and mechanical stability indicators such as dielectric contamination coefficient and structural coherence failure coefficient, the system can obtain a more comprehensive and three-dimensional view of the communication link status. This comprehensive assessment capability makes the communication resource allocation strategy more intelligent and robust. For example, when a low hardware stress aging coefficient is detected, the system can proactively reduce the transmission power, adjust the modulation and coding scheme, or switch to a backup link, thereby avoiding communication interruptions or service quality degradation caused by hardware performance degradation. This not only improves the reliability and stability of the communication system and extends the service life of the equipment, but also optimizes resource utilization efficiency and ensures the continuity and high quality of communication services under various complex operating conditions.

[0055] Preferably, the collaborative adaptation model is configured as follows: Based on the complementary values ​​of dielectric contamination coefficient and hardware stress aging coefficient, a weighted multidimensional deviation vector is constructed by combining the normalized temperature rise of the power amplifier junction temperature and the normalized slope of the duplexer isolation temperature drift. The Mahalanobis distance kernel function is calculated through a preset weighted diagonal matrix to generate an RF front-end cooperative fit degree with a value range of [0,1]. The larger the fit degree value, the better the comprehensive fit of the transmit link in the thermal, electrical and impedance matching dimensions. Specifically, the collaborative adaptability model is expressed as follows: in, For RF front-end co-adaptation, It is a column vector. The pre-defined diagonal weight matrix. The dielectric contamination coefficient is... The stress aging coefficient of the hardware. This refers to the junction temperature of the power amplifier (PA). For ambient reference temperature, To allow the highest junction temperature, Temperature drift of duplexer isolation (slope of the isolation curve as a function of temperature) This represents the maximum permissible temperature drift for duplexer isolation. , , , These are the weighting coefficients for the corresponding items, and the output value range of the RF front-end collaborative adaptation degree is as follows: Furthermore, a larger value indicates a better overall fit of the transmission link in terms of thermal, electrical, and impedance matching dimensions.

[0056] The synergy assessment module is a functional unit designed to comprehensively analyze multiple independent or related system parameters to quantify the overall health or performance fit of the system or its subsystems (such as the RF front-end). It integrates discrete assessment results into a unified index by establishing mathematical relationships between parameters. This module can be implemented through software algorithms that receive input data from different sensors or computing units and perform predefined mathematical operations; or through dedicated hardware circuits that integrate analog or digital signal processing units to calculate and output synergy fit in real time. The dielectric contamination coefficient serves as a key indicator in the synergy assessment, measuring the degree of environmental influence on the electromagnetic wave transmission path. Its value reflects the impact of rain film on the radome surface, atmospheric particulate matter, and the insulation state of the printed circuit board surface on signal transmission quality. The hardware stress aging coefficient characterizes the health of RF hardware under electrical and thermodynamic stresses in the synergy assessment. Its value reflects the impact of peak-to-peak high-frequency noise voltage of the DC power supply bus, electrostatic discharge event count rate, and ambient atmospheric pressure on hardware performance and lifespan. The junction temperature of a power amplifier refers to the actual operating temperature of the PN junction of the semiconductor device inside the power amplifier. It is a key parameter affecting the performance, reliability, and lifespan of the power amplifier. Excessively high junction temperatures can lead to decreased power amplifier efficiency, increased nonlinear distortion, and even permanent damage. This junction temperature can be measured directly by a temperature sensor integrated inside the power amplifier package; or non-contactly by using an external infrared thermometer; or estimated by monitoring electrical parameters such as the power amplifier's current and voltage, combined with a thermal model. The duplexer isolation temperature drift refers to the rate or sensitivity of change in the isolation between the transmitter and receiver at different operating temperatures. Isolation is an important indicator of duplexer performance, and its temperature drift can affect mutual interference between transmitted and received signals, thus affecting communication quality. This drift can be obtained by measuring the duplexer's isolation at different temperature points and then calculating the slope or fitting curve of the isolation change with temperature; or by looking up tables or interpolating from temperature characteristic curve data provided by the duplexer manufacturer. Coordination fit models can employ machine learning-based regression models, such as support vector regression and neural networks, to learn complex relationships between parameters through training data; or they can use weighted summations, products, or more complex combinations of nonlinear functions based on expert experience and physical models. RF front-end coordination fit is a comprehensive indicator used to quantify the overall health and performance optimization of the communication system's RF front-end across multiple key dimensions, including thermal, electrical, and impedance matching. A higher value indicates better overall performance of the RF front-end and more coordinated cooperation between subsystems. This fit value can serve as a basis for system adaptive adjustment, fault prediction, or resource allocation.

[0057] This application's solution comprehensively considers multiple key parameters, including dielectric contamination coefficient, hardware stress aging coefficient, power amplifier junction temperature, and duplexer isolation temperature drift, through a collaborative evaluation module. Specifically, the dielectric contamination coefficient and hardware stress aging coefficient reflect the environmental impact on the electromagnetic transmission path and the electrical and thermodynamic stress state of the hardware itself, respectively. The power amplifier junction temperature is directly related to the thermal performance and reliability of the transmit link, while the duplexer isolation temperature drift characterizes the stability of impedance matching and signal isolation of the RF front-end at different temperatures. The collaborative evaluation module uses these parameters as input to construct a column vector. This includes various indicators that have been normalized or transformed. It uses a preset diagonal weight matrix. This model assigns different weights to various indicators based on their importance to the overall performance of the RF front-end. Finally, the cooperative fit model uses this weighted comprehensive information and a nonlinear mapping in the form of an exponential function to calculate the cooperative fit of the RF front-end. This comprehensive evaluation method overcomes the limitations of single-index evaluation and can more comprehensively and accurately reflect the overall adaptability of the RF front-end in terms of thermal, electrical, and impedance matching dimensions, thereby effectively solving the problem of system performance degradation caused by the combined effect of multiple factors.

[0058] As a specific implementation, the coordination assessment module can be deployed in the digital signal processing unit or field-programmable gate array (FPGA) of a communication base station. This module periodically acquires data from the following sources: dielectric contamination coefficient and hardware stress aging coefficient, calculated by their respective assessment modules and transmitted to the coordination assessment module; the power amplifier junction temperature can be read in real time by a digital temperature sensor integrated within the power amplifier chip, and the digital signal is sent to the digital signal processing unit via an SPI or I2C interface; the duplexer isolation temperature drift can be obtained in advance through laboratory testing and stored in the memory of the digital signal processing unit in the form of a lookup table. The digital signal processing unit queries the corresponding drift value based on the current ambient temperature. After receiving this data, the coordination assessment module substitutes it into the coordination fit model for calculation. For example, column vectors... In and The parameters can reflect the degree of negative impact of contamination and stress on performance, while and The terms are normalized by the ratio of the reference value and the maximum allowable value. Diagonal weight matrix Σ -1 Weighting coefficients in , , , The configuration can be determined based on the actual system design and performance requirements, or through the analytic hierarchy process (AHP), orthogonal experimental design (OEP), or data fitting. For example, if the junction temperature of the power amplifier has the most critical impact on system performance, then... It can be set to a relatively large value. The final calculated RF front-end co-fit degree The value can serve as an important basis for subsequent system decisions (such as resource allocation and fault warning).

[0059] Through the above technical solution, this application can comprehensively and quantitatively evaluate multiple key factors affecting RF front-end performance, such as dielectric contamination, hardware stress aging, power amplifier junction temperature, and duplexer isolation temperature drift. This multi-dimensional collaborative evaluation mechanism enables the system to more accurately grasp the overall health status and performance adaptability of the RF front-end, avoiding the one-sidedness that may result from single-index evaluation. Therefore, when allocating communication resources or optimizing the system, more reasonable and effective decisions can be made based on a more comprehensive and accurate RF front-end collaborative adaptability, thereby improving the overall performance, stability, and reliability of the communication system. Especially under complex and changing environmental conditions, it can effectively cope with various potential performance degradation risks.

[0060] Preferably, the threshold optimization model is configured as follows: Based on the current local oscillator phase noise tolerance threshold, and combined with the nonlinear incremental term determined by the ratio of the RF front-end cooperative fit to the preset target fit, and using the structural coherence destruction coefficient as the convergence speed adjustment factor, an optimized local oscillator phase noise tolerance threshold is generated within the preset maximum adjustment range. Specifically, the threshold optimization model is expressed as: in, The optimized local oscillator phase noise tolerance threshold. This is the current local oscillator phase noise tolerance threshold. The maximum adjustable span of the threshold. The structural coherence failure factor is... For RF front-end co-adaptation, For target RF front-end co-fit, It is a very small positive number (10) -6 This is to ensure numerical stability.

[0061] This threshold optimization model aims to dynamically adjust the local oscillator phase noise tolerance threshold based on a comprehensive assessment of the current operating status of the communication system. Its function is to provide an intelligent decision-making mechanism to adapt to constantly changing external environments and internal hardware conditions. This model can be implemented using a dedicated digital signal processor (DSP) or field-programmable gate array (FPGA) and calculated through firmware or software algorithms; alternatively, its logic can be integrated into the main control unit of the communication system as part of its resource management or performance optimization module. The optimized local oscillator phase noise tolerance threshold is the output result calculated by the threshold optimization model, representing the upper limit of local oscillator phase noise tolerance that the system should adopt under current conditions. This threshold can be directly configured into the control register of the local oscillator circuit to adjust its phase noise suppression capability; or it can be used as a reference parameter for the baseband processing unit during signal demodulation, guiding it to compensate for or tolerate the phase noise of the received signal. The current local oscillator phase noise tolerance threshold refers to the local oscillator phase noise tolerance threshold used by the system before this optimization calculation. It can be the default value when the system is powered on, the result of the previous optimization calculation, or a baseline value set according to a certain preset strategy. The maximum adjustable threshold range defines the maximum range that the local oscillator phase noise tolerance threshold can be adjusted during a single optimization process. Its function is to limit the magnitude of threshold adjustment, preventing system instability or exceeding hardware capabilities due to over-adjustment. This range can be preset based on the physical characteristics of the local oscillator circuit (such as the tuning range of the voltage-controlled oscillator VCO) or dynamically configured according to the overall system performance requirements and robustness needs. The structural coherence violation coefficient serves as an adjustment factor in this threshold optimization model, measuring the stability of the antenna array structure and beamforming phase coherence. Its function is to weight the sensitivity of threshold adjustment based on the physical state of the antenna array. When structural coherence is good, this coefficient is larger, allowing for more aggressive threshold adjustment; conversely, when structural coherence is poor, this coefficient is smaller, making threshold adjustment more conservative to avoid misjudgments due to structural instability. The RF front-end co-fit comprehensively reflects the overall fit status of the transmit link in thermal, electrical, and impedance matching dimensions. It is one of the important inputs to the threshold optimization model, indicating the health and performance level of the RF front-end. The target RF front-end co-fit is the ideal or optimal level of RF front-end co-fit that the system aims to achieve. This value is typically a preset constant based on system design goals and performance requirements, used to compare with the current RF front-end co-fit, thereby driving the threshold optimization process. (Minimum positive number) It is a tiny positive number used to ensure numerical stability, and is usually set to 10. -6 Its function is to prevent the denominator from becoming zero when the RF front-end compatibility is close to or equal to zero, thereby avoiding calculation errors or numerical overflow.

[0062] The threshold optimization model in this application dynamically adjusts the local oscillator phase noise tolerance threshold through an adaptive mechanism. The core of this model lies in using the current local oscillator phase noise tolerance threshold as a benchmark, and then superimposing an adjustment amount driven by the difference between the RF front-end cooperative fit and the target RF front-end cooperative fit. Specifically, when the current RF front-end cooperative fit deviates from the target fit, the model generates an adjustment signal, the strength of which is related to the degree of deviation. To ensure the rationality and robustness of the adjustment, this adjustment signal is not directly applied to the threshold, but is first subjected to a nonlinear transformation through an exponential function. Furthermore, the sensitivity of this transformation is further adjusted by the structural coherence destruction coefficient. When the structural coherence destruction coefficient is high, it indicates that the antenna array structure is stable and the beamforming phase coherence is good. In this case, the model is more sensitive to differences in RF front-end cooperative fit, allowing for a larger threshold adjustment. Conversely, when the structural coherence violation factor is low, it indicates potential instability in the antenna array structure. In this case, the model reduces its response to differences in RF front-end coherence adaptability, making the threshold adjustment more conservative to avoid misjudgments or over-optimization due to structural issues. Ultimately, this adjusted amount is limited to the maximum adjustable range of the threshold and superimposed on... This allows the system to comprehensively consider the overall health of the RF front-end and the physical stability of the antenna array, achieving intelligent and adaptive management of the local oscillator phase noise tolerance threshold, thereby optimizing the use of system resources while ensuring communication quality.

[0063] The following is a concrete example to illustrate this. The threshold optimization model described above can be deployed in the digital processing unit of a communication base station. This digital processing unit can be a high-performance FPGA chip, which integrates dedicated computational logic. At the beginning of each communication frame or at periodic time intervals (e.g., every 100 milliseconds), the FPGA reads the current local oscillator phase noise tolerance threshold from the system memory. Simultaneously, the structural coherence degradation coefficient and RF front-end coherence fit calculated by the dielectric contamination assessment module, structural coherence assessment module, hardware stress assessment module, and synergy assessment module are also transmitted to the FPGA in real time. Preset target RF front-end coherence fit (e.g., 0.9), maximum adjustable threshold range (e.g., 5 dB), and minimum positive values ​​are also specified. (e.g., 10) -6 This parameter is stored as a fixed parameter in the FPGA's configuration register. The FPGA then uses the formula... Calculations were performed to obtain the optimized local oscillator phase noise tolerance threshold. After the calculations were completed, the... The value will be written to the control interface of the local oscillator circuit, for example, via the Serial Peripheral Interface (SPI) or I / O. 2C-bus updates the phase noise tolerance parameters of the local oscillator circuit.

[0064] Through the above technical solution, this application provides an intelligent local oscillator phase noise tolerance threshold optimization mechanism. This mechanism can comprehensively consider the overall cooperative adaptability of the RF front-end and the structural coherence of the antenna array, and dynamically adjust the local oscillator phase noise tolerance threshold. This avoids the limitations of traditional fixed thresholds or simple empirical adjustments, which may be overly conservative when the system is in good condition, or overly aggressive when the system is in poor condition. By using the structural coherence degradation coefficient as an adjustment factor, this solution can intelligently adjust the sensitivity of threshold optimization according to the physical stability of the antenna array, ensuring more aggressive optimization when the structure is stable, and adopting a more robust strategy when the structure is unstable. This enables the communication system to better adapt to complex and changing operating environments, effectively improve the robustness, reliability, and spectral efficiency of the communication link, and optimize the utilization of system resources, thereby maintaining high-quality communication services under various operating conditions.

[0065] Preferably, the optimized local oscillator phase noise tolerance threshold output by the threshold optimization module is configured to guide the media access control layer scheduler of the base station to determine the highest modulation and coding scheme level allowed in the downlink of the orthogonal frequency division multiple access system.

[0066] A communication device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the functions of each module in the above system.

[0067] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the functions of the modules in the above system.

[0068] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0069] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A communication resource allocation system based on channel state, characterized in that, include: The dielectric contamination assessment module obtains the dielectric contamination coefficient based on the rain film thickness on the radome surface, the concentration of inhalable particulate matter in the atmosphere, and the insulation resistance of the printed circuit board surface through a dielectric contamination model. The structural coherence assessment module obtains the structural coherence failure coefficient based on the peak-to-peak value of the electric tunable antenna drive motor current ripple, the effective value of vibration acceleration at a specific frequency on the equipment backplane, and the instantaneous amplitude of the geomagnetic azimuth angle through a structural coherence failure model. The hardware stress assessment module obtains the hardware stress aging coefficient based on the peak-to-peak value of high-frequency noise voltage of DC power supply bus, electrostatic discharge event count rate and ambient atmospheric pressure through hardware stress aging model. The synergy assessment module, based on the dielectric contamination coefficient and hardware stress aging coefficient, combined with the power amplifier junction temperature and duplexer isolation temperature drift, obtains the RF front-end synergy degree through a synergy fit model. The threshold optimization module combines the current local oscillator phase noise tolerance threshold with the difference between the RF front-end cooperative adaptation degree and the target RF front-end cooperative adaptation degree, and uses the structural coherence destruction coefficient as an adjustment factor to obtain the optimized local oscillator phase noise tolerance threshold through the threshold optimization model.

2. The communication resource allocation system based on channel state according to claim 1, characterized in that, The dielectric contamination assessment model is configured as follows: Based on the ratio of the rain film thickness to a preset reference constant, the ratio of the particulate matter concentration to a preset reference concentration, and the normalization relationship of the insulation resistance to a preset half-value constant, a dielectric contamination coefficient with a value range of [0,1] is generated, where a larger value of this coefficient indicates that the electromagnetic wave transmission path is less affected by dielectric contamination.

3. The communication resource allocation system based on channel state according to claim 1, characterized in that, The structural coherence failure model is configured as follows: Based on the Gaussian attenuation relationship between the peak-to-peak value of the current ripple and the preset standard deviation of the current ripple, the effective value of the vibration acceleration and the preset standard deviation of the vibration acceleration, and the proportional relationship between the instantaneous amplitude of the geomagnetic azimuth angle and the preset reference value, a structural coherence disruption coefficient with a value range of [0,1] is generated. The larger the value of this coefficient, the more stable the antenna array structure and the better the beamforming phase coherence.

4. The communication resource allocation system based on channel state according to claim 1, characterized in that, The hardware stress aging model is configured as follows: Based on the exponential decay relationship between the peak-to-peak value of the noise voltage and the preset noise reference value, and the electrostatic discharge event count rate and the preset count rate reference value, and combined with the stress correction term determined by the pressure difference when the atmospheric pressure is determined to be lower than the preset reference pressure, a hardware stress aging coefficient with a value range of [0,1] is generated, wherein the larger the value of the coefficient, the smaller the electrical and thermodynamic stress on the RF hardware.

5. The communication resource allocation system based on channel state according to claim 1, characterized in that, The collaborative adaptability model is configured as follows: Based on the complementary values ​​of dielectric contamination coefficient and hardware stress aging coefficient, a weighted multidimensional deviation vector is constructed by combining the normalized temperature rise of the power amplifier junction temperature and the normalized slope of the duplexer isolation temperature drift. The Mahalanobis distance kernel function is calculated through a preset weighted diagonal matrix to generate an RF front-end cooperative fit degree with a value range of [0,1]. The larger the fit degree value, the better the comprehensive fit of the transmit link in the thermal, electrical and impedance matching dimensions.

6. The communication resource allocation system based on channel state according to claim 1, characterized in that, The threshold optimization model is configured as follows: Based on the current local oscillator phase noise tolerance threshold, and combined with the nonlinear incremental term determined by the ratio of the RF front-end cooperative fit to the preset target fit, and using the structural coherence destruction coefficient as the convergence speed adjustment factor, an optimized local oscillator phase noise tolerance threshold is generated within the preset maximum adjustment range.

7. The communication resource allocation system based on channel state according to claim 1, characterized in that, The optimized local oscillator phase noise tolerance threshold output by the threshold optimization module is configured to guide the base station's media access control layer scheduler in determining the highest modulation and coding scheme level allowed in the downlink of the orthogonal frequency division multiple access system.

8. A communication device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the functions of each module in any of the systems of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the functions of each module in any of the systems of claims 1-6.

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