A millimeter wave waveguide transmission loss compensation method

Abstract

The application discloses a kind of millimeter wave waveguide transmission loss compensation methods, it is related to millimeter wave waveguide transmission technical field, including multidimensional transmission loss real-time monitoring step, collection signal amplitude, phase and other electrical parameters and temperature, humidity, structural deformation and other parameters, ensure data time synchronization;Loss mechanism hierarchical analysis step, distinguish three kinds of loss proportion, clear dominant factor;Self-adapting compensation strategy matching step, according to loss type select exclusive or collaborative compensation mode;Dynamic compensation parameter optimization step, adapt signal frequency adjustment device parameter;Real-time feedback and fine adjustment step, compare signal quality dynamic correction parameter;Closed-loop iterative optimization step, construct mapping model iterative update, form whole-process closed loop.The application realizes the accurate and efficient compensation of millimeter wave waveguide transmission loss, improves signal transmission stability and environmental adaptability;With fast response and long-term operation reliability, provide support for millimeter wave technology scale application.

Description

A method for compensating transmission loss in millimeter-wave waveguides Technical Field

[0001] This invention relates to the field of millimeter-wave waveguide transmission technology, and in particular to a method for compensating for transmission loss in millimeter-wave waveguides. Background Technology

[0002] Millimeter waves, with their advantages of wide bandwidth, strong directionality, and high resolution, are widely used in 5G communications, radar detection, satellite communications, and precision measurement. Waveguides, as the core carrier of millimeter wave transmission, possess characteristics such as high power capacity, strong anti-interference capability, and high transmission efficiency, making them a key waveguide transmission line device covered in the H01P classification. However, the extremely short wavelength of millimeter waves leads to significant transmission loss during transmission. This loss problem becomes increasingly prominent with increasing transmission distance and operating frequency, severely limiting the transmission distance and quality of millimeter wave signals and becoming a key bottleneck restricting the large-scale application of millimeter wave technology.

[0003] Millimeter-wave waveguide transmission losses mainly include three categories: conductor loss, dielectric loss, and radiation loss. Conductor loss originates from the resistive loss of the waveguide's inner wall and is directly related to the material's conductivity and surface roughness. In the millimeter-wave band, the skin effect is significant, and even minor roughness of the inner wall can lead to a substantial increase in loss. Dielectric loss is caused by the polarization relaxation and conductivity effect of the dielectric filling medium within the waveguide and is significantly affected by the dielectric constant, loss tangent, and ambient temperature and humidity. Radiation loss is caused by discontinuities in the waveguide structure, inadequate interface sealing, or defects in the shielding layer, resulting in signal energy leakage into space. Furthermore, waveguides experience structural deformation, material aging, and interface oxidation during long-term operation. Dynamic changes in ambient temperature, humidity, and air pressure also exacerbate loss fluctuations. Coupling and conversion between different transmission modes generate additional losses. The superposition of these factors causes the loss characteristics to exhibit a complex and dynamic changing trend.

[0004] Existing millimeter-wave waveguide transmission loss compensation methods have several limitations. Traditional compensation methods often employ passive compensation with fixed parameters, using amplifiers with preset gains or couplers with fixed coupling coefficients to cancel losses. These methods cannot dynamically adjust based on real-time loss characteristics and environmental changes, resulting in low compensation accuracy and a tendency for over- or under-compensation. Some methods only design compensation schemes for a single loss type, lacking the ability to coordinate compensation in scenarios with multiple loss types, making them unsuitable for complex real-world applications. Loss monitoring is often limited to a single dimension, focusing only on signal amplitude attenuation while ignoring the effects of phase shift, frequency shift, and environmental and structural parameters. This leads to incomplete loss mechanism analysis and a lack of precise basis for matching compensation strategies. Furthermore, existing methods lack a full-process closed-loop optimization mechanism, failing to continuously optimize strategies based on historical data and compensation results. This makes them ill-suited for dynamic changes such as waveguide aging and frequency band switching, resulting in poor loss compensation performance in multi-mode transmission scenarios. Contact loss and reflection loss at the interface are also not effectively addressed. These shortcomings severely impact the stability and reliability of millimeter-wave transmission systems. Summary of the Invention

[0005] This invention proposes a millimeter-wave waveguide transmission loss compensation method to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a millimeter-wave waveguide transmission loss compensation method, comprising the following steps:

[0007] Multi-dimensional transmission loss real-time monitoring steps: Deploy various sensors at the input end, output end and intermediate key nodes of the millimeter waveguide to collect signal loss data and environmental parameters, and dynamically adjust the sampling frequency;

[0008] The steps for hierarchical analysis of loss mechanisms are as follows: Based on multi-dimensional data collection, distinguish the proportion of various types of loss, extract characteristic parameters of different loss types, and associate them with corresponding influencing factors;

[0009] Adaptive compensation strategy matching steps: Select the corresponding compensation method according to the type and degree of loss. When a single loss dominates, a dedicated compensation method is activated. When multiple losses coexist, a collaborative compensation mode is adopted.

[0010] Dynamic compensation parameter optimization steps: Based on the loss analysis results, adjust the operating parameters of the compensation device, combined with the waveguide transmission frequency characteristics;

[0011] Real-time feedback and fine-tuning steps: Collect the compensated signal data through the output monitoring device, compare it with the preset standard value to calculate the compensation deviation, and dynamically fine-tune the compensation parameters;

[0012] Closed-loop iterative optimization steps: Based on historical data, loss analysis results and compensation effects, construct a loss-compensation mapping model, use machine learning algorithms to optimize compensation strategies and parameter adjustment rules, update the model regularly, and form a closed-loop process.

[0013] Furthermore, it also includes loss feature extraction and classification steps. The acquired amplitude attenuation and phase shift data are decomposed into multi-scale values ​​using wavelet transform to extract multi-dimensional feature parameters. Waveguide structure parameters and environmental sensing data are fused to construct a comprehensive feature vector. The loss type is automatically classified using the support vector machine algorithm to establish a dynamically updated loss feature library. Loss feature samples under new scenarios are continuously incorporated through an online self-learning mechanism.

[0014] Furthermore, it also includes waveguide environment adaptive calibration steps. Based on the collected temperature, humidity, structural deformation and air pressure data, a multi-parameter coupled environment and loss correlation model is established. The environmental change trend and loss increment in the future period are predicted by a long short-term memory neural network. Pre-compensation measures are initiated before the loss becomes significant. When the temperature rises, the coupling strength of the coupler is adjusted in advance according to the predicted increment. When the humidity increases, the ultrasonic dehumidification module on the inner wall of the waveguide is activated. When the structure deforms, the waveguide shape is corrected by an adjustable support mechanism and the interface matching parameters are optimized simultaneously. When the air pressure changes, the equivalent impedance of the shielding layer is adjusted.

[0015] Furthermore, in the hierarchical analysis step of the loss mechanism, a multi-factor coupling model is used to calculate the comprehensive loss coefficient, and the calculation expression is as follows: in To account for the overall transmission loss factor, , , These are the weighting coefficients for conductor loss, dielectric loss, and radiation loss, respectively. This is the baseline value for conductor loss. This is the basic value for dielectric loss. This is the baseline value for radiation loss. This is the temperature influence coefficient. This is the difference between the actual temperature and the standard temperature. The surface roughness variation coefficient is... This is the difference between the actual surface roughness and the standard value. Humidity influence coefficient This represents the difference between the actual humidity and the standard humidity. The dielectric constant variation coefficient, It is the difference between the actual dielectric constant and the standard value. This is the structural deformation influence coefficient. This is the difference between the actual deformation and the standard state. The sealing condition influence coefficient is... The weighting factor is the difference between the actual sealing degree and the standard value, and is dynamically updated based on the real-time loss ratio.

[0016] Furthermore, it also includes a multi-mode transmission collaborative compensation step. For TE mode, TM mode and hybrid mode in millimeter waveguide, a high-precision mode separator is used to identify the field distribution characteristics and loss characteristics of each mode. When TE mode loss is dominant, the waveguide cross-sectional size and coupler polarization direction are optimized. When TM mode loss is prominent, the radial distribution state and dielectric constant gradient of the filling medium are adjusted. When hybrid modes coexist, a mode isolation filter is used to suppress mode conversion. At the same time, independent compensation and collaborative optimization of each mode are achieved through multi-channel compensation devices.

[0017] Furthermore, it also includes a waveguide interface coupling efficiency optimization step. For the contact loss and reflection loss at the waveguide connection, a tunable interface matcher with an elastic contact layer is used to dynamically adjust the interface gap, contact pressure and equivalent impedance. The reflection coefficient, transmission coefficient and standing wave ratio at the interface are monitored in real time by a vector network analyzer. The matching parameters are optimized based on the coupling efficiency model. An integrated ultrasonic cleaning module is used to periodically remove the oxide layer and micro-dust impurities on the interface surface. The interface temperature change is monitored simultaneously, and the contact parameters are adjusted through a thermal expansion compensation mechanism.

[0018] Furthermore, in the dynamic compensation parameter optimization step, a multi-objective optimization algorithm is used to solve for the optimal combination of compensation parameters, and the calculation expression is as follows: in This is the optimal compensation parameter vector. To compensate the parameter vector, , , These are the signal amplitude compensation weight, phase compensation weight, and energy consumption compensation weight, respectively. For the target transmission coefficient, The transmission coefficient under the current parameters. For the target phase value, This represents the phase value under the current parameters. To compensate for energy consumption under the current parameters, set compensation parameter constraint boundaries, limiting the amplifier gain to not exceed the device's rated value and the coupling length to be within the mechanical travel range.

[0019] Furthermore, it also includes fault self-diagnosis and emergency compensation steps. By continuously monitoring the abrupt changes in loss data, the adjustment range of compensation parameters, and the operating current and voltage of devices, combined with the difference analysis of multi-node monitoring data, the specific location and severity of the fault can be located. When a minor fault is detected, a local enhanced compensation strategy is activated to increase the compensation intensity in the corresponding area. When a serious fault is detected, it immediately switches to the backup compensation channel, activates the preset emergency compensation parameters, and sends out fault information through audible and visual alarms and remote push.

[0020] Furthermore, in the closed-loop iterative optimization step, an incremental learning algorithm is used to update the loss and compensation mapping model. The model parameters are adjusted in batches using newly added monitoring data, environmental change data, and compensation effect feedback information. A cross-scenario transfer learning mechanism is incorporated to transfer and train loss compensation data from different application scenarios and different waveguide models. A model evaluation index system is set up, and the model is comprehensively evaluated regularly. When any index falls below the preset threshold multiple times in a row, the model reconstruction process is triggered. The model structure and parameter mapping relationship are optimized by combining the latest waveguide characteristics, environmental change patterns, and device performance degradation trends.

[0021] Furthermore, it includes a cross-band adaptive compensation extension step. Addressing the differences in transmission loss characteristics across different frequency bands, a multi-dimensional association library is established, linking frequency bands, loss types, compensation parameters, and device adaptation. When the transmission signal frequency band switches, the optimal compensation strategy and initial parameters for the corresponding frequency band are automatically invoked. Based on real-time loss data, rapid fine-tuning is performed, employing a smooth transition algorithm. Specialized compensation optimizations are implemented for the prominent radiation loss in high-frequency bands and the high proportion of conductor loss in low-frequency bands. High-frequency bands feature enhanced waveguide shielding structure and radiation suppression compensation, while low-frequency bands optimize conductor surface treatment and coupling enhancement compensation. Dynamic frequency band extension functionality is supported, and newly added frequency bands can automatically generate initial compensation parameters through feature matching.

[0022] Compared with existing technologies, the beneficial effects of this invention are:

[0023] The present invention provides a millimeter-wave waveguide transmission loss compensation method that comprehensively addresses the pain points of existing technologies through multi-dimensional innovative design. It achieves significant breakthroughs in loss monitoring, mechanism analysis, compensation strategies, and dynamic optimization, and possesses outstanding technical advantages and application value.

[0024] At the loss perception level, the multi-dimensional transmission loss real-time monitoring step breaks through the limitations of traditional single-dimensional monitoring. It simultaneously collects electrical parameters such as signal amplitude, phase, and frequency, as well as environmental and structural parameters such as temperature, humidity, and structural deformation, ensuring the comprehensiveness and time synchronization of loss data. The loss feature extraction and classification step achieves accurate identification of loss types through multi-dimensional feature fusion and intelligent classification algorithms, providing solid data support for compensation strategy matching. Compared with traditional monitoring methods, the timeliness and accuracy of loss identification are greatly improved, effectively avoiding compensation failures caused by misjudgments of loss mechanisms.

[0025] At the compensation adaptation level, the layered analysis of loss mechanisms clarifies the proportion and dominant factors of different loss types, providing a scientific basis for targeted compensation. The adaptive compensation strategy matching step designs specific compensation methods for different loss types, employing a collaborative compensation mode when multiple losses coexist to achieve precise loss cancellation. The multi-mode transmission collaborative compensation step addresses the characteristic differences of TE, TM, and hybrid modes by combining mode isolation and independent compensation to suppress additional losses caused by inter-mode interference. The waveguide interface coupling efficiency optimization step significantly reduces contact and reflection losses at the interface by dynamically adjusting interface parameters and performing cleaning and maintenance. The cross-band adaptive compensation extension step achieves efficient compensation for different millimeter-wave frequency bands, greatly improving the adaptability and versatility of the method.

[0026] At the dynamic optimization level, the dynamic compensation parameter optimization step uses a multi-objective optimization algorithm to balance compensation energy consumption while ensuring signal transmission quality, avoiding resource waste caused by over-compensation. The real-time feedback and fine-tuning step dynamically corrects parameters based on the compensated signal quality, ensuring transmission quality remains stable within the target range. The closed-loop iterative optimization step employs incremental learning and transfer learning mechanisms to continuously update the loss-compensation mapping model, adapting to dynamic scenarios such as waveguide aging and environmental changes, thus continuously optimizing the compensation effect. The fault self-diagnosis and emergency compensation step can quickly identify faults and activate emergency plans, ensuring basic transmission functions and improving system reliability.

[0027] At the overall performance level, this method achieves precise, dynamic, and intelligent loss compensation through a closed-loop mechanism of "monitoring-analysis-compensation-feedback-optimization." It effectively suppresses various transmission losses, improves the transmission distance and quality of millimeter-wave signals, and enhances the stability and anti-interference capabilities of the transmission system. The waveguide environment adaptive calibration step reduces the impact of environmental fluctuations on losses from the source, extends waveguide lifespan, and reduces operation and maintenance costs. This method is adaptable to multiple frequency bands, multiple modes, and multiple application scenarios, providing solid technical support for the large-scale application of millimeter waves in communications, radar, satellites, and other fields, and possesses broad promotional value and application prospects. Attached Figure Description

[0028] Figure 1 is a schematic block diagram of the millimeter-wave waveguide transmission loss compensation method proposed in this invention;

[0029] Figure 2 is a grouped bar chart comparing the transmission loss suppression rate of different millimeter wave frequency bands;

[0030] Figure 3 is a line graph comparing signal transmission stability under extreme environments;

[0031] Figure 4 is a grouped bar chart comparing the signal-to-noise ratio of the signal after multimodal transmission compensation;

[0032] Figure 5 is a line graph comparing the retention rate of long-term operating loss compensation accuracy. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0035] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0036] Referring to Figures 1 to 5: A method for compensating transmission loss in a millimeter-wave waveguide includes the following steps:

[0037] The multi-dimensional real-time transmission loss monitoring steps involve deploying broadband power sensors, phase detectors, and frequency analyzers at the input, output, and key intermediate nodes of the millimeter waveguide to collect signal amplitude attenuation, phase shift, and frequency shift data. Simultaneously, distributed temperature, humidity, and strain sensors are used to collect waveguide cavity temperature, inner wall humidity, and structural deformation parameters. The sampling frequency is dynamically adjusted according to the signal transmission rate to ensure the time synchronization of loss data and environmental parameters.

[0038] The loss mechanism is analyzed in layers. Based on the collected multi-dimensional data, the proportions of conductor loss, dielectric loss and radiation loss are distinguished. The characteristic parameters corresponding to different loss types are extracted through spectrum analysis. Conductor loss is related to the conductivity and surface roughness of the waveguide material. Dielectric loss is combined with the dielectric constant of the filling medium and the loss tangent. Radiation loss is matched with the waveguide structure size and sealing state to identify the dominant loss factors.

[0039] The adaptive compensation strategy matching steps select the corresponding compensation method according to the loss type and degree. When conductor loss is dominant, the adjustable coupler is activated to enhance energy coupling efficiency. When dielectric loss is prominent, the dielectric characteristic control module is used to optimize the filling dielectric state. When radiation loss is significant, the waveguide interface sealing structure and shielding layer parameters are adjusted. When multiple loss types coexist, a collaborative compensation mode of coupling enhancement, phase calibration and gain adjustment is adopted.

[0040] The dynamic compensation parameter optimization steps involve adjusting the operating parameters of the compensation device based on the loss analysis results, optimizing the coupling gap and coupling length of the adjustable coupler, calibrating the phase shifter for the phase offset, and matching the gain amplitude of the low-noise amplifier. At the same time, the waveguide transmission frequency characteristics are combined to ensure that the compensation parameters are accurately matched with the millimeter-wave signal frequency, avoiding over-compensation or under-compensation.

[0041] The real-time feedback and fine-tuning process involves collecting the compensated signal amplitude, phase, and signal-to-noise ratio data through the output monitoring device, comparing them with preset standard values, calculating the compensation deviation, and dynamically fine-tuning the compensation parameters based on the deviation. The adjustment step size is adaptively set according to the magnitude of the deviation to ensure that the signal transmission quality remains stable within the target range.

[0042] The closed-loop iterative optimization process involves constructing a loss-compensation mapping model based on historical monitoring data, loss analysis results, and compensation effects. Machine learning algorithms are used to optimize the compensation strategy selection logic and parameter adjustment rules. The model parameters are updated regularly to adapt to changes in loss characteristics caused by waveguide aging and environmental changes, forming a complete closed-loop process of "monitoring-analysis-compensation-feedback-optimization".

[0043] This invention also includes loss feature extraction and classification steps. Wavelet transform is used to decompose the collected amplitude attenuation and phase shift data into multiple scales, extracting multi-dimensional feature parameters such as time-domain peaks, frequency-domain spectral peaks, and phase change rates. A comprehensive feature vector is constructed by fusing waveguide structure parameters and environmental sensing data. A support vector machine algorithm is used to automatically classify loss types, establishing a dynamically updated loss feature library. An online self-learning mechanism continuously incorporates loss feature samples from new scenarios, constantly optimizing the classifier's decision boundary. The classification accuracy remains stable at a high level, ensuring the timeliness and accuracy of identifying dominant loss factors and providing a precise and comprehensive basis for matching compensation strategies.

[0044] This invention also includes a waveguide environment adaptive calibration step. Based on the collected temperature, humidity, structural deformation, and air pressure data, a multi-parameter coupled environment-loss correlation model is established. The environmental change trend and loss increment in the future period are predicted by a long short-term memory neural network. Pre-compensation measures are initiated before the loss becomes significant. When the temperature rises, the coupling strength of the coupler is adjusted in advance according to the predicted increment. When the humidity increases, the ultrasonic dehumidification module on the inner wall of the waveguide is activated and the dielectric loss compensation parameters are optimized. When the structure deforms, the waveguide shape is corrected by an adjustable support mechanism and the interface matching parameters are optimized simultaneously. When the air pressure changes, the equivalent impedance of the shielding layer is adjusted. This reduces the impact of environmental fluctuations on transmission loss from the source and improves the predictability and stability of compensation.

[0045] In this invention, during the hierarchical analysis of the loss mechanism, a multi-factor coupling model is used to calculate the comprehensive loss coefficient, quantifying the influence weights of different loss types. The calculation expression is as follows: in To account for the overall transmission loss factor, , , These are the weighting coefficients for conductor loss, dielectric loss, and radiation loss, respectively, and their sum is 1. This is the baseline value for conductor loss. This is the basic value for dielectric loss. This is the baseline value for radiation loss. This is the temperature influence coefficient. This is the difference between the actual temperature and the standard temperature. The surface roughness variation coefficient is... This is the difference between the actual surface roughness and the standard value. Humidity influence coefficient This represents the difference between the actual humidity and the standard humidity. The dielectric constant variation coefficient, It is the difference between the actual dielectric constant and the standard value. This is the structural deformation influence coefficient. This is the difference between the actual deformation and the standard state. The sealing condition influence coefficient is... The difference between the actual sealing degree and the standard value is used as the weighting coefficient, which is dynamically updated based on the real-time loss ratio. Through multi-factor quantification, the loss mechanism can be accurately analyzed.

[0046] This invention also includes a multi-mode transmission collaborative compensation step. For the TE mode, TM mode, and mixed modes that may exist in the millimeter waveguide, a high-precision mode separator is used to identify the field distribution characteristics and loss characteristics of each mode. A dedicated compensation strategy is matched for each mode. When the TE mode loss is dominant, the waveguide cross-sectional dimensions and coupler polarization direction are optimized to enhance the transverse electric field confinement capability. When the TM mode loss is prominent, the radial distribution state and dielectric constant gradient of the filling medium are adjusted to improve the longitudinal electric field transmission efficiency. When mixed modes coexist, a mode isolation filter is used to suppress mode conversion. At the same time, multi-channel compensation devices are used to achieve independent compensation and collaborative optimization of each mode, avoiding additional losses caused by inter-mode interference and ensuring that the transmission loss of each mode is effectively suppressed.

[0047] This invention also includes a waveguide interface coupling efficiency optimization step. Addressing the contact loss and reflection loss at the waveguide connection points, a tunable interface matcher with an elastic contact layer is used to dynamically adjust the interface gap, contact pressure, and equivalent impedance. A vector network analyzer is used to monitor the reflection coefficient, transmission coefficient, and VSWR at the interface in real time. Matching parameters are optimized based on a coupling efficiency model to reduce energy reflection and loss at the interface. An integrated ultrasonic cleaning module periodically removes the oxide layer and micro-dust impurities from the interface surface, while simultaneously monitoring interface temperature changes. A thermal expansion compensation mechanism is used to adjust contact parameters to prevent poor contact caused by temperature increases and maintain a stable coupling state.

[0048] In this invention, the dynamic compensation parameter optimization step employs a multi-objective optimization algorithm to solve for the optimal combination of compensation parameters, and the calculation expression is as follows: in This is the optimal compensation parameter vector, which includes parameters such as coupler coupling length, phase shifter phase offset, and amplifier gain. To compensate the parameter vector, , , These are the signal amplitude compensation weight, phase compensation weight, and energy consumption compensation weight, respectively, and their sum is 1. For the target transmission coefficient, The transmission coefficient under the current parameters. For the target phase value, This represents the phase value under the current parameters. To compensate for energy consumption under the current parameters, compensation parameter constraints are set, limiting the amplifier gain to no more than the device's rated value and the coupling length to within the mechanical travel range. Through multi-objective optimization, a balance between transmission quality and compensation energy consumption is achieved.

[0049] This invention also includes fault self-diagnosis and emergency compensation steps. By continuously monitoring the abrupt changes in loss data, the adjustment range of compensation parameters, and the operating current and voltage of devices, combined with the difference analysis of multi-node monitoring data, it accurately identifies abnormalities such as waveguide damage, loose interfaces, and compensation device failures, pinpointing the specific location and severity of the fault. When a minor fault is detected, a local enhanced compensation strategy is activated to increase the compensation intensity in the corresponding area. When a serious fault is detected, it immediately switches to the backup compensation channel, activates preset emergency compensation parameters, maintains basic signal transmission functions, and simultaneously issues fault information through audible and visual alarms and remote push notifications, marking the fault location and preliminary diagnostic results, providing accurate guidance and a sufficient time window for fault diagnosis and repair.

[0050] In this invention, the closed-loop iterative optimization step employs an incremental learning algorithm to update the loss-compensation mapping model. Utilizing newly added monitoring data, environmental change data, and compensation effect feedback, model parameters are adjusted in batches, eliminating the need to retrain the entire model and improving iteration efficiency. A cross-scenario transfer learning mechanism is integrated, allowing for the transfer and training of loss compensation data from different application scenarios and waveguide models, enhancing the model's generalization ability. A model evaluation index system is established, including compensation accuracy, signal stability, energy consumption control effect, and response speed. The model is periodically comprehensively evaluated, and when any index falls below a preset threshold multiple times consecutively, a model reconstruction process is triggered. Combining the latest waveguide characteristics, environmental change patterns, and device performance degradation trends, the model structure and parameter mapping relationship are optimized to ensure the model maintains high efficiency and adaptability over the long term.

[0051] This invention also includes a cross-band adaptive compensation extension step. Addressing the differences in transmission loss characteristics across millimeter-wave frequency bands from 24GHz to 100GHz, a multi-dimensional correlation library is established, linking frequency band, loss type, compensation parameters, and device adaptation. When the transmission signal frequency band switches, the optimal compensation strategy and initial parameters for the corresponding frequency band are automatically invoked. Rapid fine-tuning is performed based on real-time loss data, and a smooth transition algorithm is used to avoid signal fluctuations caused by sudden changes in compensation parameters. Specialized compensation optimizations are implemented to address the prominent radiation loss in high-frequency bands and the high proportion of conductor loss in low-frequency bands. High-frequency bands feature enhanced waveguide shielding structures and radiation suppression compensation, while low-frequency bands optimize conductor surface treatment and coupling enhancement compensation. Dynamic frequency band extension is supported; newly added frequency bands can automatically generate initial compensation parameters through feature matching. Efficient compensation can be achieved with minimal iterative optimization, adapting to diverse application scenarios such as multi-band communication, radar detection, and satellite communication.

[0052] The following two examples further illustrate the specific implementation of this system:

[0053] Example 1: Application of 5G millimeter-wave communication base station waveguide transmission system

[0054] This embodiment is applied to the waveguide transmission system of a 5G millimeter-wave communication base station. The operating frequency band covers 24GHz to 40GHz, and the waveguide transmission distance is 50 meters. It passes through outdoor open areas and equipment rooms, and faces environmental fluctuations of -20℃ to 60℃ and humidity of 10% to 90%. At the same time, there is a multi-mode transmission scenario where TE mode and TM mode coexist. Precise loss compensation is required to ensure signal transmission quality and meet the high bandwidth and low latency communication requirements of the base station.

[0055] In the multi-dimensional real-time transmission loss monitoring step, a broadband power sensor, phase detector, and frequency analyzer are deployed at each of the three key nodes—the waveguide input, output, and intermediate nodes—to collect signal amplitude attenuation, phase offset, and frequency offset data. The sampling frequency is set to 100Hz when the signal transmission rate is below 1Gbps and increased to 1kHz when it is above 1Gbps. A set of distributed temperature, humidity, and strain sensors is deployed every 10 meters outside the waveguide cavity to collect cavity temperature, internal wall humidity, and structural deformation parameters. All sensor data are synchronized via a synchronous clock module to ensure that the timestamp error between the loss data and environmental parameters is less than 1ms.

[0056] In the loss feature extraction and classification step, wavelet transform is used to perform a 5-level multi-scale decomposition of the acquired amplitude attenuation and phase shift data, extracting eight feature parameters, including time-domain peak value, peak interval, frequency-domain spectral peak intensity, and phase change rate. These parameters are then fused with waveguide inner wall roughness, dielectric constant of the filling medium, and ambient temperature and humidity data to construct a 12-dimensional comprehensive feature vector. A support vector machine algorithm is used to train and classify the feature vector, establishing a dynamically updated loss feature library. The classifier decision boundary is updated every 100 new valid data sets, achieving a stable classification accuracy of over 95%, and quickly identifying conductor loss, dielectric loss, radiation loss, and mixed loss types.

[0057] In the hierarchical analysis of loss mechanisms, based on classification results and multi-dimensional data, characteristic parameters of different loss types are extracted through spectral analysis. Conductor loss is correlated with the conductivity of the waveguide copper material and the 0.1-micron-level surface roughness of the inner wall; dielectric loss is combined with the dielectric constant and loss tangent of the polytetrafluoroethylene-filled dielectric; and radiation loss is matched with the sealing gap of the waveguide flange interface and the thickness of the shielding layer. The proportion of each loss type is determined by weight allocation, and when the proportion of conductor loss exceeds 50%, it is determined to be the dominant loss factor.

[0058] In the waveguide environment adaptive calibration step, a multi-parameter coupled environment-loss correlation model is established based on the collected temperature, humidity, structural deformation, and air pressure data. A long short-term memory neural network is used to predict the environmental change trend within the next 30 minutes. When the predicted temperature rise exceeds 5°C, the coupling strength of the adjustable coupler is increased by 10% in advance. When the predicted humidity increase exceeds 20%, the ultrasonic dehumidification module on the inner wall of the waveguide is activated, and the dielectric loss compensation parameter adjustment coefficient is set to 1.2. When structural deformation exceeds 0.5 mm, the waveguide morphology is corrected through an adjustable support mechanism, and the interface matching parameters are optimized simultaneously.

[0059] In the adaptive compensation strategy matching step, for scenarios where conductor loss is dominant, an electrically adjustable coupler is activated, and the coupling gap and coupling length are adjusted by a stepper motor; for scenarios where dielectric loss is prominent, the electric field distribution of the filling dielectric is changed by a dielectric characteristic control module; for scenarios where radiation loss is significant, the sealing ring of the waveguide interface is tightened and the shielding layer is thickened; in mixed loss scenarios, the adjustable coupler, phase shifter and low-noise amplifier are activated simultaneously, and a collaborative compensation mode of coupling enhancement, phase calibration and gain adjustment is adopted.

[0060] In the dynamic compensation parameter optimization step, the coupling gap of the adjustable coupler is adjusted from 0.1mm to 1mm, the coupling length is adjusted from 5mm to 20mm, the phase calibration accuracy of the phase shifter is 0.1°, and the gain adjustment range of the low-noise amplifier is 10dB to 30dB. Based on a multi-objective optimization algorithm, while ensuring that the signal transmission coefficient meets the standard and the phase offset is controlled within ±1°, the compensation energy consumption is controlled to below 80% of the device's rated power consumption, achieving a balance between transmission quality and energy consumption.

[0061] In the waveguide interface coupling efficiency optimization step, a tunable interface matcher with an elastic contact layer is used. The interface gap and contact pressure are dynamically adjusted via a piezoelectric ceramic actuator, with an adjustment accuracy of 0.01 mm and a contact pressure controlled between 5 N and 15 N. A vector network analyzer monitors the reflection coefficient, transmission coefficient, and VSWR at the interface in real time. When the absolute value of the reflection coefficient is greater than 0.1, the matching parameters are automatically optimized. The integrated ultrasonic cleaning module is activated every 24 hours to remove the oxide layer and micro-dust impurities from the interface surface, while simultaneously monitoring the interface temperature and adjusting the contact parameters through a thermal expansion compensation mechanism.

[0062] In the multi-mode transmission collaborative compensation step, the field distribution characteristics of TE10 mode, TM11 mode and mixed mode are identified by a high-precision mode separator. When the loss of TE10 mode is dominant, the width of the waveguide cross section is finely adjusted by 0.5mm to 1mm, and the polarization direction of the coupler is aligned with the electric field direction. When the loss of TM11 mode is prominent, a filling medium with a gradient distribution of dielectric constant is set in the radial direction of the waveguide. When mixed modes coexist, a mode isolation filter is activated to suppress mode conversion, and the two modes are compensated independently by a dual-channel compensation device.

[0063] In the real-time feedback and fine-tuning process, the wideband power sensor and phase detector at the output end collect the compensated signal data every 100ms and compare it with the preset standard value to calculate the compensation deviation. When the deviation is greater than 5%, the gain of the low-noise amplifier is fine-tuned in steps of 0.05dB. When the phase deviation exceeds 0.5°, the phase shifter parameters are adjusted in steps of 0.05° to ensure stable signal transmission quality.

[0064] In the closed-loop iterative optimization process, an incremental learning algorithm is adopted. Every 24 hours, monitoring data and compensation effect feedback are accumulated, and the loss-compensation mapping model parameters are updated in batches. Transfer learning data from cross-base station scenarios are incorporated to improve the model's generalization ability. Four evaluation indicators are set: compensation accuracy, signal stability, energy consumption control effect, and response speed. When any indicator falls below a preset threshold three times consecutively, the model reconstruction process is triggered.

[0065] In the fault self-diagnosis and emergency compensation process, by monitoring the abrupt changes in loss data and the adjustment frequency of compensation parameters, an abnormal fault is identified when the abrupt change in amplitude exceeds 10 dB / second or the parameter adjustment frequency exceeds 10 times / second, and the fault location is accurately pinpointed. For minor faults, the compensation intensity in the corresponding area is increased; for severe faults, the system immediately switches to the backup compensation channel, activates preset emergency compensation parameters, maintains the signal transmission rate at no less than 70% of the rated value, and simultaneously issues audible and visual alarms and remotely pushes fault information.

[0066] In the cross-band adaptive compensation extension step, a loss type-compensation parameter-device adaptation association library is established for each frequency band from 24GHz to 40GHz. When the signal frequency band switches from 28GHz to 39GHz, the initial compensation parameters of the 39GHz band are automatically called, and fine-tuning is completed within 1 second based on real-time loss data. High-frequency bands feature enhanced waveguide shielding structure and radiation suppression compensation, while low-frequency bands optimize conductor surface treatment and coupling enhancement compensation. For newly added frequency bands, initial compensation parameters are automatically generated through feature matching, and efficient compensation can be achieved after 10 iterations of optimization.

[0067] Table 1: Comparison of Waveguide Transmission Loss Compensation Effects in 5G Millimeter Wave Communication Base Stations

[0068]

[0069] Table 1 clearly demonstrates the advantages of this invention in 5G millimeter-wave communication base station scenarios. Traditional solutions employ passive compensation with fixed parameters, resulting in low transmission loss suppression rates, poor signal phase stability, and difficulty in adapting to multiple frequency bands and complex environments, lacking effective emergency protection in case of failure. This invention, through multi-dimensional monitoring and precise analysis, achieves rapid identification of loss types, targeted matching of compensation strategies, significantly improved multi-modal and cross-frequency band compensation capabilities, and significantly enhanced environmental adaptability. The fault self-diagnosis and emergency compensation mechanism ensures the continuity of signal transmission, with transmission loss suppression rates and signal stability significantly superior to traditional solutions, perfectly meeting the high bandwidth and low latency communication requirements of 5G base stations.

[0070] Example 2: Application of Vehicle-Mounted Millimeter-Wave Radar Waveguide Transmission System

[0071] This embodiment applies to the waveguide transmission system of an on-board millimeter-wave radar. The operating frequency band is 77GHz, the waveguide transmission distance is 5 meters, and it is installed on the inside of the front bumper of the vehicle. It faces extreme temperature changes from -40℃ to 85℃, vibration and impact during vehicle operation, and humidity fluctuations. The transmission process is mainly in TE mode with a small amount of mode conversion. It is necessary to ensure the radar detection accuracy and distance through fast response loss compensation to meet the safety requirements of autonomous driving.

[0072] In the multi-dimensional transmission loss real-time monitoring step, a miniaturized broadband power sensor and a phase detector are deployed at both the waveguide input and output ends. A frequency analyzer is deployed at a key node in the middle to collect signal amplitude attenuation, phase shift, and frequency shift data. The sampling frequency is fixed at 2kHz to adapt to the dynamic driving scenario of the vehicle. Two sets of miniature distributed temperature sensors, humidity sensors, and strain sensors are deployed on the surface of the waveguide cavity to collect cavity temperature, internal wall humidity, and structural deformation parameters caused by vibration. The sensors adopt a shock-resistant packaging design, and the data is transmitted through a high-speed bus with the time synchronization error controlled within 0.5ms.

[0073] In the loss feature extraction and classification steps, wavelet transform is used to perform a three-level multi-scale decomposition of the acquired amplitude attenuation and phase shift data, extracting six feature parameters: time-domain peak value, rising edge slope, frequency-domain spectral peak width, and phase jitter value. These are then fused with waveguide material conductivity, surface roughness, and vehicle vibration frequency data to construct a nine-dimensional comprehensive feature vector. A support vector machine algorithm is used for classification training, and the loss feature database is updated every 50 new valid data sets. The classification accuracy remains stable above 92%, quickly identifying conductor loss, dielectric loss, and mixed loss types.

[0074] In the loss mechanism hierarchical analysis step, based on the classification results, conductor loss is associated with the conductivity of the waveguide aluminum alloy material and the 0.05-micron-level surface roughness of the inner wall; dielectric loss is combined with the dielectric constant of the ceramic filling medium and the loss tangent; radiation loss is matched with the sealing state of the waveguide interface and the gap change caused by vehicle vibration. The proportion of each loss is determined by weight allocation, and when the proportion of dielectric loss exceeds 40%, it is determined to be the dominant loss factor.

[0075] In the waveguide environment adaptive calibration step, an environment-loss correlation model is established based on the collected temperature, humidity, structural deformation, and vibration data. A long short-term memory neural network is used to predict environmental changes within the next 10 minutes. When the predicted temperature is below -20℃, the coupling strength of the adjustable coupler is increased by 15% in advance; when the humidity exceeds 80%, a micro-dehumidification module is activated and the dielectric loss compensation parameters are optimized; when the structural deformation caused by vibration exceeds 0.3mm, the deformation effect is offset by an elastic buffer mechanism, and the interface matching parameters are adjusted synchronously.

[0076] In the adaptive compensation strategy matching step, when conductor loss is dominant, a micro-electric adjustable coupler is activated; when dielectric loss is prominent, the state of the ceramic dielectric is optimized through the dielectric characteristic control module; when radiation loss is significant, the interface seal is tightened; and in mixed loss scenarios, a collaborative compensation mode of coupling enhancement and phase calibration is adopted to adapt to the space constraints of automotive scenarios.

[0077] In the dynamic compensation parameter optimization step, the coupling gap of the miniature electric adjustable coupler is adjusted from 0.05mm to 0.5mm, the coupling length is adjusted from 3mm to 10mm, the phase shifter phase calibration accuracy is 0.2°, and the low-noise amplifier gain is adjusted from 8dB to 20dB. Based on a multi-objective optimization algorithm, while ensuring that the signal transmission coefficient meets the standard and the phase offset is controlled within ±2°, the compensation energy consumption is controlled below 5W, adapting to the power limitations of the vehicle power supply.

[0078] In the waveguide interface coupling efficiency optimization step, a tunable interface matcher reinforced with elastic conductive rubber is used. An electromagnetic actuator dynamically adjusts the interface gap and contact pressure, with an adjustment accuracy of 0.02mm and contact pressure controlled between 3N and 10N. A miniaturized vector network analyzer monitors the interface reflection coefficient and transmission coefficient in real time. When the absolute value of the reflection coefficient exceeds 0.15, parameters are automatically optimized. An integrated micro-cleaning brush activates every 100 hours to remove impurities from the interface surface. For poor contact caused by vehicle vibration, a vibration compensation algorithm dynamically adjusts the contact pressure.

[0079] In the multimode transmission collaborative compensation step, the TE01 mode and a small number of converted TM01 modes are identified by a compact mode separator. When the loss of the TE01 mode is dominant, the waveguide cross-sectional size and coupler polarization direction are optimized. When the proportion of the TM01 mode exceeds 10%, a miniature mode filter is activated to suppress mode conversion. The collaborative compensation of the two modes is achieved by a single-channel dual-parameter compensation device.

[0080] In the real-time feedback and fine-tuning process, the output sensor collects the compensated signal data every 50ms and compares it with the preset standard value to calculate the compensation deviation. When the deviation is greater than 3%, the gain of the low-noise amplifier is fine-tuned in steps of 0.03dB. When the phase deviation exceeds 1°, the phase shifter parameters are adjusted in steps of 0.1° to ensure that the signal transmission quality can quickly adapt to the dynamic driving state of the vehicle.

[0081] In the closed-loop iterative optimization process, an incremental learning algorithm is used. Every 12 hours of accumulated monitoring data and compensation effect feedback, the parameters of the loss-compensation mapping model are updated in batches, incorporating transfer learning data under different road conditions. Four evaluation indicators are set: compensation accuracy, signal stability, energy consumption control effect, and response speed. When any indicator falls below a preset threshold twice consecutively, the model reconstruction process is triggered.

[0082] In the fault self-diagnosis and emergency compensation process, by monitoring sudden changes in loss data and the adjustment frequency of compensation parameters, a fault is identified and its location is determined when the amplitude of the sudden change exceeds 5 dB / second or the parameter adjustment frequency exceeds 15 times / second. For minor faults, the compensation intensity is increased; for severe faults, the system switches to the backup compensation channel, activates emergency compensation parameters, maintains the radar detection range at no less than 60% of the rated value, and simultaneously sends a fault alarm signal to the vehicle control system.

[0083] In the cross-band adaptive compensation extension step, a related library for the 76GHz to 79GHz frequency bands is established. When the signal frequency band is fine-tuned from 77GHz to 78GHz, the corresponding initial compensation parameters for the frequency band are automatically called, and real-time fine-tuning is completed within 1 second. For the frequency band characteristics of automotive scenarios, the compensation optimization for the impact of temperature on losses is strengthened to ensure compensation effectiveness under extreme temperatures. Initial parameters for newly added frequency bands are automatically generated through feature matching, and after 5 iterations of optimization, they can meet the usage requirements.

[0084] Table 2: Comparison of Waveguide Transmission Loss Compensation Effects for Vehicle-Mounted Millimeter-Wave Radar

[0085]

[0086] Table 2 highlights the application value of this invention in automotive millimeter-wave radar scenarios. Traditional solutions suffer from slow response speeds, poor stability under extreme environments and vibrations, insufficient detection accuracy, and difficulty in meeting the low-power requirements of automotive applications. This invention employs miniaturized sensors and fast-response compensation devices, significantly improving loss compensation response speed. Environmental adaptive calibration and vibration compensation mechanisms enhance adaptability to extreme environments and vibrations. The low-power optimized design meets automotive power constraints, multi-modal and cross-frequency band compensation capabilities ensure radar detection accuracy, and a fault emergency protection mechanism improves the safety of autonomous driving. All performance indicators are superior to traditional solutions, perfectly meeting the operational requirements of automotive millimeter-wave radar.

[0087] Referring to Figure 2: This figure visually demonstrates the core advantages of this invention in loss compensation across the entire millimeter-wave frequency band, directly addressing the pain points of traditional solutions, such as poor frequency band adaptability and low loss suppression rate. Traditional solutions employ passive compensation with fixed parameters, lacking targeted optimization for the loss characteristics of different frequency bands. As the frequency increases, radiation loss and conductor loss become increasingly significant, leading to a continuous decline in suppression rate, reaching only 35.7% in the 100GHz band, which is insufficient to meet the needs of high-frequency applications. This invention establishes a multi-dimensional correlation library of frequency band, loss, and compensation parameters, specifically optimizing for the dominant loss types in different frequency bands. High-frequency bands feature enhanced shielding structures and radiation suppression compensation, while low-frequency bands optimize conductor surface treatment and coupling enhancement compensation. The loss suppression rate remains stable above 88% across all frequency bands, reaching as high as 92.5% in the 24GHz band. The cross-frequency band adaptive extension function enables rapid fine-tuning during frequency band switching, significantly improving full-band adaptability and perfectly meeting the needs of multi-frequency band applications such as 5G communication and radar detection.

[0088] Referring to Figure 3: This figure clearly illustrates the transmission stability advantages of this invention in complex and extreme environments, solving the problems of poor environmental adaptability and large signal fluctuations in traditional solutions. Traditional solutions lack a calibration mechanism that correlates environment and loss. Device performance degrades significantly under extreme low and high temperatures, strong vibration environments easily cause poor interface contact, and high humidity environments exacerbate dielectric loss. Signal stability is generally below 70%, and even less than 60% under low temperature and strong vibration conditions, making it unsuitable for complex scenarios such as vehicle-mounted and outdoor base stations. This invention establishes a multi-parameter coupled environment-loss correlation model to predict environmental change trends in advance and initiate pre-compensation measures. It also incorporates auxiliary modules such as shock-resistant packaging and ultrasonic dehumidification. Signal stability remains above 95% under extreme environments and approaches 98% under normal temperature conditions. Environmental adaptive calibration reduces loss fluctuations from the source, significantly improving the system's anti-interference capability and environmental adaptability, providing a solid guarantee for stable millimeter-wave transmission in complex scenarios.

[0089] Referring to Figure 4: This figure visually demonstrates the compensation advantages of this invention in multimodal transmission scenarios, solving the problems of traditional solutions failing to suppress additional losses during mode conversion and resulting in poor signal quality. Traditional solutions lack mode identification and dedicated compensation capabilities, designing compensation schemes only for a single mainstream mode, rendering them ineffective for losses in other modes and mixed modes. Mode conversion also induces additional losses, and the signal-to-noise ratio after compensation is generally below 30dB, with mixed modes reaching only 20.5dB, severely impacting signal transmission quality and detection accuracy. This invention identifies the field distribution characteristics of each mode using a high-precision mode separator, matching dedicated compensation strategies for different modes. In mixed modes, a mode isolation filter is used to suppress conversion losses, while multi-channel compensation devices achieve independent and coordinated compensation. After compensation, the signal-to-noise ratio for all modes is stable above 40dB, with the TE10 mode reaching as high as 45.2dB. This significantly improves signal quality in multimodal transmission scenarios, avoids performance degradation caused by inter-mode interference, and provides technical support for the large-scale application of multimodal millimeter-wave systems.

[0090] Referring to Figure 5: This figure clearly reveals the compensation stability advantage of this invention in long-term operation scenarios, overcoming the pain points of traditional solutions being unable to adapt to equipment aging and continuously declining compensation accuracy. Traditional solutions lack a closed-loop iterative optimization mechanism. Factors such as waveguide material aging, interface oxidation, and device performance degradation continuously reduce the compensation effect. After 500 hours of operation, the compensation accuracy retention rate is only 51.7%, requiring frequent manual maintenance and calibration, significantly increasing operation and maintenance costs. This invention, through a closed-loop iterative optimization step, uses an incremental learning algorithm to update the loss-compensation mapping model in batches, incorporating data such as waveguide aging and device attenuation, eliminating the need to retrain the entire model and achieving high iterative efficiency. Simultaneously, the model is periodically comprehensively evaluated, triggering a reconstruction process when indicators fail to meet standards. After 500 hours of operation, the compensation accuracy retention rate remains as high as 96.9%, consistently above 96%. This significantly reduces operation and maintenance costs, extends system lifespan, and provides a guarantee for the long-term stable operation of millimeter-wave equipment, demonstrating outstanding engineering application value.

[0091] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for compensating transmission loss in millimeter-wave waveguides, characterized in that, Includes the following steps: Multi-dimensional transmission loss real-time monitoring steps: Deploy various sensors at the input end, output end and intermediate key nodes of the millimeter waveguide to collect signal loss data and environmental parameters, and dynamically adjust the sampling frequency; Loss mechanism hierarchical analysis steps: Based on the multi-dimensional collected data, distinguish the proportion of various types of loss, extract the characteristic parameters of different loss types, and associate them with the corresponding influencing factors. Adaptive compensation strategy matching steps: Select the corresponding compensation method according to the type and degree of loss. When a single loss dominates, a dedicated compensation method is used. When multiple losses coexist, a collaborative compensation mode is adopted. Dynamic compensation parameter optimization steps: Based on the loss analysis results, adjust the operating parameters of the compensation device and combine them with the waveguide transmission frequency characteristics. Real-time feedback and fine-tuning steps: Collect the compensated signal data through the output monitoring device, compare it with the preset standard value to calculate the compensation deviation, and dynamically fine-tune the compensation parameters; Closed-loop iterative optimization steps: Based on historical data, loss analysis results and compensation effect, construct a loss and compensation mapping model, use machine learning algorithms to optimize the compensation strategy and parameter adjustment rules, update the model regularly, and form a closed loop for the entire process; In the hierarchical analysis step of the loss mechanism, a multi-factor coupling model is used to calculate the comprehensive loss coefficient, and the calculation expression is as follows: in To account for the overall transmission loss factor, 、 、 These are the weighting coefficients for conductor loss, dielectric loss, and radiation loss, respectively. This is the baseline value for conductor loss. This is the basic value for dielectric loss. This is the baseline value for radiation loss. This is the temperature influence coefficient. This is the difference between the actual temperature and the standard temperature. The surface roughness variation coefficient is... This is the difference between the actual surface roughness and the standard value. Humidity influence coefficient This represents the difference between the actual humidity and the standard humidity. The dielectric constant variation coefficient, It is the difference between the actual dielectric constant and the standard value. This is the structural deformation influence coefficient. This is the difference between the actual deformation and the standard state. The sealing condition influence coefficient is... The weighting coefficient is the difference between the actual sealing degree and the standard value, and is dynamically updated based on the real-time loss ratio. In the dynamic compensation parameter optimization step, a multi-objective optimization algorithm is used to solve for the optimal combination of compensation parameters, and the calculation expression is: in This is the optimal compensation parameter vector. To compensate for the parameter vector, 、 、 These are the signal amplitude compensation weight, phase compensation weight, and energy consumption compensation weight, respectively. For the target transmission coefficient, The transmission coefficient under the current parameters. For the target phase value, This represents the phase value under the current parameters. To compensate for energy consumption under the current parameters, set compensation parameter constraint boundaries, limiting the amplifier gain to not exceed the device's rated value and the coupling length to be within the mechanical travel range.

2. The millimeter-wave waveguide transmission loss compensation method according to claim 1, characterized in that, It also includes loss feature extraction and classification steps. The acquired amplitude attenuation and phase shift data are decomposed into multi-scale values ​​through wavelet transform to extract multi-dimensional feature parameters. The waveguide structure parameters and environmental sensing data are fused to construct a comprehensive feature vector. The support vector machine algorithm is used to automatically classify loss types and establish a dynamically updated loss feature library. Loss feature samples under new scenarios are continuously incorporated through an online self-learning mechanism.

3. The millimeter-wave waveguide transmission loss compensation method according to claim 1, characterized in that, It also includes a waveguide environment adaptive calibration step, which establishes a multi-parameter coupled environment and loss correlation model based on the collected temperature, humidity, structural deformation and air pressure data, and predicts the environmental change trend and loss increment through a long short-term memory neural network.

4. The millimeter-wave waveguide transmission loss compensation method according to claim 1, characterized in that, It also includes a multi-mode transmission collaborative compensation step. For TE mode, TM mode and hybrid mode in millimeter waveguide, a high-precision mode separator is used to identify the field distribution characteristics and loss characteristics of each mode. When TE mode loss is dominant, the waveguide cross-sectional size and coupler polarization direction are optimized. When TM mode loss is prominent, the radial distribution state and dielectric constant gradient of the filling medium are adjusted. When hybrid modes coexist, a mode isolation filter is used to suppress mode conversion. At the same time, independent compensation and collaborative optimization of each mode are achieved through multi-channel compensation devices.

5. The millimeter-wave waveguide transmission loss compensation method according to claim 1, characterized in that, It also includes a waveguide interface coupling efficiency optimization step. For the contact loss and reflection loss at the waveguide connection, a tunable interface matcher with an elastic contact layer is used to dynamically adjust the interface gap, contact pressure and equivalent impedance. The reflection coefficient, transmission coefficient and standing wave ratio at the interface are monitored in real time by a vector network analyzer. The matching parameters are optimized based on the coupling efficiency model. An integrated ultrasonic cleaning module is used to periodically remove the oxide layer and micro-dust impurities on the interface surface. The interface temperature change is monitored simultaneously, and the contact parameters are adjusted through a thermal expansion compensation mechanism.

6. The millimeter-wave waveguide transmission loss compensation method according to claim 1, characterized in that, It also includes fault self-diagnosis and emergency compensation steps. By continuously monitoring the abrupt change characteristics of loss data, the adjustment range of compensation parameters, and the operating current and voltage of devices, combined with the difference analysis of multi-node monitoring data, the specific location and severity of the fault can be located. When a minor fault is detected, a local enhanced compensation strategy is activated to increase the compensation intensity of the corresponding area. When a serious fault is detected, the system immediately switches to the backup compensation channel, activates the preset emergency compensation parameters, and sends out fault information through audible and visual alarms and remote push notifications.

7. The millimeter-wave waveguide transmission loss compensation method according to claim 1, characterized in that, In the closed-loop iterative optimization step, an incremental learning algorithm is used to update the loss and compensation mapping model. The model parameters are adjusted in batches using newly added monitoring data, environmental change data, and compensation effect feedback information. A cross-scenario transfer learning mechanism is incorporated to transfer and train loss compensation data from different application scenarios and different waveguide models. A model evaluation index system is set up, and the model is comprehensively evaluated regularly. When any index falls below the preset threshold multiple times in a row, the model reconstruction process is triggered. The model structure and parameter mapping relationship are optimized by combining the latest waveguide characteristics, environmental change patterns, and device performance degradation trends.

8. The millimeter-wave waveguide transmission loss compensation method according to claim 1, characterized in that, It also includes a cross-band adaptive compensation extension step. To address the differences in transmission loss characteristics across different frequency bands, a multi-dimensional association library is established, linking frequency bands, loss types, compensation parameters, and device adaptation. When the transmission signal frequency band is switched, the optimal compensation strategy and initial parameters for the corresponding frequency band are automatically invoked. Based on real-time loss data, rapid fine-tuning is performed, and a smooth transition algorithm is adopted. Special compensation optimization is performed for the characteristics of prominent radiation loss in high-frequency bands and high conductor loss in low-frequency bands. High-frequency bands strengthen waveguide shielding structure and radiation suppression compensation, while low-frequency bands optimize conductor surface treatment and coupling enhancement compensation. It supports dynamic frequency band expansion, and newly added frequency bands can automatically generate initial compensation parameters through feature matching.

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