Anti-interference dynamic optimization system and method for radio and television microwave transmission quality

Abstract

The invention discloses an anti-interference dynamic optimization system and method for radio and television microwave transmission quality. The system comprises a multi-dimensional interference sensing module, a multi-index quality evaluation module, a coupling relation decoupling modeling module, a dynamic optimization decision module and a closed-loop feedback reconstruction module. According to the method, multi-dimensional interference characteristic parameters are collected, a multi-dimensional transmission quality index system is constructed, a comprehensive evaluation value is calculated, a nonlinear coupling relation model is established to recognize dominant interference factors, an optimization objective function is solved to generate a parameter optimization scheme, and self-adaptive optimization is achieved through online reconstruction and closed-loop feedback. According to the invention, accurate sensing and distinguishing of multi-source interference are realized, transmission link parameters are optimized in a targeted manner, and the stability and reliability of broadcast television microwave transmission quality in a complex electromagnetic environment are improved.

Description

Technical Field

[0001] This invention relates to the field of microwave transmission technology for broadcast television, and specifically to a dynamic optimization system and method for improving the quality of microwave transmission of broadcast television with anti-interference capabilities. Background Technology

[0002] Microwave transmission systems for broadcast television are widely used in scenarios such as broadcast television program transmission, emergency broadcasting, and signal coverage in remote areas due to their advantages of short construction cycles, wide coverage, and minimal terrain limitations. However, in actual operation, transmission links are susceptible to multi-source interference such as co-channel or adjacent-channel wireless communication interference, external electromagnetic radiation interference, channel fading, and polarization mismatch, leading to decreased signal-to-noise ratio, increased bit error rate, and even service interruption. Existing technologies mostly employ fixed parameter configurations or single threshold adjustment strategies, which cannot distinguish interference types or quantify the impact of different interferences. This can easily result in parameter adjustments that are delayed, excessive, or ineffective, making it difficult to adapt to complex electromagnetic environments and insufficiently ensuring service continuity. Therefore, there is an urgent need for a technical solution that can accurately sense multi-source interference and dynamically optimize transmission parameters. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic optimization system and method for broadcast television microwave transmission quality to combat interference, thereby solving the problem that existing technologies with fixed parameter configurations or single threshold adjustment strategies cannot distinguish interference types or quantify the degree of impact of different interferences.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: An anti-interference dynamic optimization system for microwave transmission quality of broadcast television includes: a multi-dimensional interference sensing module, which collects multi-dimensional interference characteristic parameters of the microwave transmission link of broadcast television and outputs a set of interference characteristic parameters; a multi-index quality assessment module, connected to the multi-dimensional interference sensing module, which receives the set of interference characteristic parameters, constructs a multi-dimensional transmission quality index system, and calculates a comprehensive transmission quality assessment value; a coupling relationship decoupling modeling module, connected to the multi-index quality assessment module, which receives the comprehensive transmission quality assessment value and the set of interference characteristic parameters, establishes a nonlinear coupling relationship model between the interference characteristic parameters and the transmission quality index, calculates the contribution of each interference characteristic parameter to the transmission quality, and identifies the dominant interference factor; a dynamic optimization decision module, connected to the coupling relationship decoupling modeling module, which receives the dominant interference factor, constructs a parameter optimization objective function and constraints, and generates a transmission link parameter optimization scheme; and a closed-loop feedback reconstruction module, connected to the dynamic optimization decision module and the multi-dimensional interference sensing module, which receives the parameter optimization scheme and sends it to the microwave transmission link of broadcast television for parameter reconstruction, collects the interference characteristic parameters of the reconstructed link in real time and feeds them back to the multi-index quality assessment module to form a closed-loop adaptive optimization. A collaborative architecture is constructed throughout the entire process, encompassing "interference perception, quality assessment, factor identification, parameter optimization, and closed-loop feedback." Each module interacts with data to form a complete link, achieving comprehensive interference capture while ensuring continuous optimization effectiveness through a closed-loop mechanism. This aligns with the adaptive control logic of "perception-decision-execution-verification." It addresses the shortcomings of existing technologies where fixed parameter configurations or single threshold adjustments cannot handle multi-source interference. Through multi-module collaboration, it achieves accurate interference perception and targeted optimization, avoiding issues of lag, over-adjustment, or ineffectiveness, thereby improving the stability and reliability of transmission quality in complex electromagnetic environments.

[0005] Further solution: The multi-dimensional transmission quality indicator system includes characterization quality indicators, stability indicators, and service-aware quality indicators. The comprehensive transmission quality evaluation value is calculated using the following formula: First, the original indicators under each dimension are normalized: for original indicators where larger values ​​are better... The normalization formula is: For the original index, smaller is better The normalization formula is: in, For the kth original index value, The minimum value of the k-th indicator. The maximum value of the k-th indicator. Let be the normalized value of the k-th indicator, k∈{1,2,...,M}, where M is the total number of original indicators; Calculate the quality index Q1, stability index Q2, and business perception quality index Q3: in, To characterize the number of quality sub-indicators The number of stability sub-indicators. = - - The number of sub-indicators for perceived quality in business operations. The weighting coefficients for characterizing the quality sub-indicators; satisfy and , The weighting coefficients for the stability sub-indicators; satisfy and >0, Weighting coefficients for business-perceived quality sub-indicators; satisfy ; Calculate the overall transmission quality evaluation value Q: in, , , These are the weighting coefficients for the characterization quality index, stability index, and business perception quality index, respectively, satisfying... ; First, normalization is used to eliminate differences in the dimensions and value ranges of different indicators, unifying indicators that are "better the larger, better" and "better the smaller, better" into comparable parameters. Then, weighted summation is used to integrate the three types of sub-indicators separately. Finally, the three types of indicators are merged to obtain a comprehensive evaluation value, reflecting both the differences in importance of each sub-indicator and achieving a comprehensive quantification of transmission quality. This avoids the one-sidedness of single-indicator evaluation, solves the problem of a lack of systematic quality assessment in existing technologies, and makes the transmission quality assessment results more objective and comprehensive, providing accurate quality basis for subsequent interference factor identification and parameter optimization.

[0006] Further solution: The construction of the nonlinear coupling relationship model, the calculation of the contribution of interference feature parameters, and the identification of the dominant interference factor are achieved through the following formula: Let the interference feature parameter vector be... ,in For the first There are N interference feature parameters, where N is the number of interference feature parameters. ∈{1,2,...,N}; Establish a nonlinear coupling model between interference characteristic parameters and transmission quality indicators: in, For transmission quality index vector, It is a multivariate nonlinear mapping function; Let K be the model parameter vector, and K be the number of model parameters. Calculate the first One interference characteristic parameter For the first Transmission quality indicators Impact Contribution : Where j∈{1,2,3}, Calculate the partial derivatives; The total influence contribution of each interference characteristic parameter : Among them; dominant interference factor satisfy in ; A quantitative mapping relationship between interference and quality is established by using a nonlinear coupling model, overcoming the limitations of linear relationships; the contribution is calculated by combining partial derivatives with the ratio of parameters to indicators, quantifying the relative impact of a single interference on quality; and the dominant interference factor is located by ranking the total contribution, achieving hierarchical analysis of composite interference.

[0007] The corresponding benefits are: it solves the problem that existing technologies cannot distinguish the type of interference or quantify the degree of interference impact, accurately identifies the core factors that cause transmission quality degradation, provides a clear target for subsequent differentiated optimization, and avoids aimless parameter adjustments.

[0008] Further solution: The parameter optimization scheme is generated by constructing and solving the following objective function and constraints: Let the transmission link parameter vector be... There are 1, 2, ..., T transmission link parameters, where T is the number of transmission link parameters, and t∈{1,2,...,T}. Optimize the objective function: in, Let P be the transmission link parameter and the dominant interference factor be... Comprehensive evaluation value of transmission quality at that time; Constraints: in, Let be the minimum value of the t-th parameter. Let G(P) be the maximum value of the t-th parameter, G(P) be the parametric equality constraint function, and H(P) be the parametric inequality constraint function. With maximizing transmission quality under the dominant interference factor as the core objective, and considering constraints such as equipment hardware performance and transmission standards, the optimization scheme aims to achieve the best results within feasible limits, conforming to the optimization logic of "goal-oriented + controllable constraints." It avoids parameter optimization exceeding equipment capabilities or violating transmission rules, ensuring the feasibility and effectiveness of the optimization scheme, solving the problems of over-adjustment or ineffective parameter adjustments in existing technologies, achieving precise response to dominant interference, and improving optimization efficiency.

[0009] Further proposed solution: The multidimensional interference characteristic parameters include microwave signal-to-noise ratio, bit error rate, packet loss rate, spectrum occupancy characteristic parameters, sidelobe interference energy parameters, polarization mismatch characteristic parameters, and time-domain fluctuation characteristic parameters; Interference features are comprehensively collected from various dimensions, including physical layer transmission performance (signal-to-noise ratio, bit error rate, etc.), frequency domain interference status (spectrum occupancy, sidelobe energy, etc.), spatial polarization characteristics (polarization mismatch), and temporal interference patterns (temporal fluctuations), ensuring coverage of all interference types that may affect transmission quality. This avoids omissions and misjudgments due to incomplete interference feature collection, providing complete and comprehensive data support for subsequent coupled modeling and factor identification, and ensuring accurate perception of multi-source composite interference.

[0010] Further details: The transmission link parameters include modulation parameters, coding redundancy parameters, transmit power allocation parameters, channel bandwidth configuration parameters, polarization parameters, and equalization and error correction parameters; It covers key parameters of the core transmission link, including modulation, coding, power, bandwidth, polarization, equalization, and error correction. Each parameter corresponds to the needs of dealing with different types of interference, forming a multi-dimensional and comprehensive adjustment system. It provides targeted adjustment methods for different types of dominant interference. For example, polarization mismatch interference can be dealt with by adjusting polarization parameters, and co-channel interference can be dealt with by adjusting channel bandwidth or coding redundancy, ensuring that an effective optimization path can be found for all types of interference.

[0011] Further solution: The closed-loop feedback reconstruction module does not interrupt the broadcast television service when performing parameter reconstruction. After reconstruction, the comprehensive evaluation value of transmission quality is verified by the multi-index quality evaluation module. When the evaluation value is lower than the preset threshold, the dynamic optimization decision module is triggered to generate a parameter optimization scheme again. The parameter reconstruction employs a seamless switching mechanism to ensure service continuity. Real-time feedback is used to evaluate and verify the optimization effect; if the target is not met, secondary optimization is triggered, forming a closed-loop cycle of "execution-verification-re-optimization," consistent with the continuous optimization logic of adaptive control. This solves the problem of service interruption caused by parameter adjustment in existing technologies, ensuring the continuity of broadcast television services. Simultaneously, it avoids the shortcomings of incomplete single optimization, ensuring that transmission quality continuously meets preset requirements and adapts to dynamic changes in complex electromagnetic environments.

[0012] An anti-interference dynamic optimization method for microwave transmission quality of broadcast television includes the following steps: Step 1: Collect multi-dimensional interference characteristic parameters of the broadcast television microwave transmission link and construct an interference characteristic parameter vector. ; Step 2: Construct a multi-dimensional transmission quality index system and calculate the quality indicators. Stability indicators Business perception quality indicators and the overall transmission quality assessment value Q; Step 3: Establish a nonlinear coupling relationship model Y=F(X;Θ), and calculate the total influence contribution of each interference characteristic parameter. Identify the dominant interference factor ; Step 4: Based on the objective function and constraints, solve for the transmission link parameter vector. This leads to the formation of a parameter optimization scheme; Step 5: Send the parameter optimization plan to the broadcast television microwave transmission link and perform parameter reconstruction; Step 6: Collect multi-dimensional interference characteristic parameters of the reconstructed transmission link, and repeat Step 2 to calculate the comprehensive evaluation value of the reconstructed transmission quality. ,like < Then return to steps 3 through 5 until... ≥ ,in This is a preset transmission quality threshold; The process proceeds step by step according to the sequence of "data acquisition - quality assessment - factor identification - scheme generation - parameter reconstruction - feedback verification". The output of each step serves as the input for the next step. If the target is not met, a loop iteration is triggered to form a complete closed-loop adaptive process, ensuring the continuity and effectiveness of optimization.

[0013] The corresponding benefits are: the process logic is rigorous and progressive, avoiding fragmentation of the optimization process; through iterative iteration, it achieves real-time response to dynamic interference, solving the problem of optimization lag in existing technologies and ensuring that the transmission quality remains stable in complex electromagnetic environments.

[0014] Further solution: The multidimensional interference feature parameters collected in step 1 are obtained through frequency domain sensing, time domain sensing, and spatial and polarization sensing. The frequency domain sensing obtains the spectrum occupancy status and co-frequency and adjacent-frequency interference energy distribution parameters. The time domain sensing obtains the time duration characteristics, burst characteristics, and periodic fluctuation parameters of the interference signal. The spatial and polarization sensing obtains polarization mismatch information and spatial directional interference parameters. Dedicated sensing methods are designed for interference features of different dimensions: frequency domain sensing focuses on frequency-dimensional interference, time domain sensing focuses on time-dimensional interference, and spatial and polarization sensing focuses on spatial polarization-dimensional interference, achieving accurate acquisition of interference features. This improves the targeting and accuracy of interference feature acquisition, avoids confusion between interference features of different dimensions, and ensures that the acquired interference parameters truly reflect the nature of the interference, providing high-quality data support for subsequent model construction and factor identification.

[0015] Further solution: In step 4, the objective function is solved using a numerical optimization algorithm, which includes any one of gradient descent, genetic algorithm, or particle swarm optimization algorithm. Mature and efficient numerical optimization algorithms are selected to solve the objective function. Different algorithms are adapted to the solution requirements of different scenarios, ensuring that the optimal parameter vector is found quickly under constraints. This improves the efficiency of parameter optimization solutions, avoids optimization lag caused by excessively long solution times, ensures the system can quickly respond to changes in interference, generate optimization solutions in a timely manner, and guarantee real-time stability of transmission quality.

[0016] The present invention has the following beneficial effects: This invention comprehensively captures the interference characteristics of the transmission link through a multi-dimensional interference sensing module, accurately reflects the transmission quality status through a multi-index quality assessment module, identifies the dominant interference factors through a coupling relationship decoupling modeling module, generates targeted parameter optimization schemes through a dynamic optimization decision-making module, and ensures that the optimization effect continuously meets the standards through a closed-loop feedback reconstruction module. The entire technical solution realizes a closed-loop adaptive process for interference sensing, quality assessment, factor identification, parameter optimization, and feedback verification. Logically, it solves the problems of existing technologies being unable to distinguish interference types and lacking targeted optimization adjustments, enabling transmission link parameters to be dynamically adjusted according to the interference status. This, in turn, ensures the stability and reliability of microwave transmission for broadcast television in complex electromagnetic environments and improves service continuity. Attached Figure Description

[0017] Figure 1 This is a logic block diagram of the system of the present invention.

[0018] Figure 2 The diagram illustrates the specific steps of the method of the present invention. Detailed Implementation

[0019] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0020] I. Overall Structure Implementation The anti-interference broadcast television microwave transmission quality dynamic optimization system provided in this embodiment has the same overall structure as described in the technical solution. Each module interacts with signals and data through a data interface, forming a complete closed-loop adaptive optimization system. This system can be deployed at the transmitting and receiving ends of broadcast television microwave transmission. Through real-time data acquisition, analysis, decision-making, and execution, it achieves dynamic optimization of transmission quality without affecting the normal broadcasting of broadcast television services.

[0021] II. Detailed Implementation of Each Module and Method Step (I) Multidimensional interference sensing module and parameter acquisition The multi-dimensional interference sensing module acquires multi-dimensional interference characteristic parameters through a frequency domain sensing unit, a time domain sensing unit, and a spatial and polarization sensing unit. Specifically, the frequency domain sensing unit uses a spectrum analyzer to acquire the spectrum occupancy status of the microwave transmission signal and the energy distribution of co-channel and adjacent-channel interference, outputting parameters such as spectrum occupancy rate, co-channel interference power, and adjacent-channel interference power. The time domain sensing unit uses an oscilloscope and a data acquisition card to acquire parameters such as the duration of the interference signal, burst interval, and periodic fluctuation period. The spatial and polarization sensing unit uses a polarization tester and a directional antenna to acquire parameters such as the polarization mismatch angle and the incident direction of the spatial interference signal.

[0022] The collected multidimensional interference characteristic parameters specifically include microwave signal-to-noise ratio, bit error rate, packet loss rate, spectrum occupancy rate, co-channel interference power, adjacent channel interference power, sidelobe interference energy, polarization mismatch angle, interference signal duration, burst interval, periodic fluctuation period, and spatial incident direction. These parameters are then processed to construct an interference characteristic parameter vector. ,in For signal-to-noise ratio, For bit error rate, is the spectrum occupancy rate, and the remaining parameters correspond to the interference features collected above, with N being the total number of interference feature parameters collected.

[0023] (II) Multi-indicator quality assessment module and calculation of comprehensive assessment value The multi-dimensional transmission quality indicator system includes three categories: characterization quality indicators, stability indicators, and service-aware quality indicators. Among them, the sub-indicators of characterization quality indicators include signal-to-noise ratio, bit error rate, and packet loss rate, corresponding to... =3; Sub-indicators of stability include transmission quality fluctuation variance and the number of index mutations, corresponding to =2; Sub-indicators of business perception quality include the percentage of time with screen stuttering and audio distortion, corresponding to =2, Total number of original indicators = + + =7.

[0024] The weighting coefficients are determined using the analytic hierarchy process (AHP), where the weighting coefficients of the quality sub-indicators are... ,satisfy Weighting coefficients of stability sub-indicators ,satisfy Weighting coefficients of business perception quality sub-indicators ,satisfy Weighting coefficients of the three types of indicators ,satisfy + + =1.

[0025] With signal-to-noise ratio (SNR) (higher is better) and bit error rate ( Normalization is performed using the example of (the smaller the better): assuming the minimum signal-to-noise ratio... =10, Maximum value =40, and the signal-to-noise ratio z1=30 at a certain moment, then its normalized value is 40. Assume the minimum value of the bit error rate. = Maximum value The bit error rate collected at a certain moment Then its normalized value .

[0026] Calculate the quality indicators according to the formula. ; Stability Indicators ; Business-perceived quality indicators ; Then through The overall transmission quality assessment value Q is calculated.

[0027] (III) Decoupling Modeling Module and Identification of Dominant Interference Factors Multivariate nonlinear mapping function in nonlinear coupling relationship model Implemented using a BP neural network, model parameters This includes the weight matrix and bias vector of the neural network. The training data for the BP neural network comes from the measured data of historical interference feature parameters and corresponding transmission quality indicators. The training process minimizes the mean square error between the predicted and measured values ​​using the gradient descent method until the model converges.

[0028] Partial derivatives of interference characteristic parameters with respect to transmission quality indices The interference is calculated through backpropagation of a BP neural network, that is, by analyzing the sensitivity of the neural network's output to the input, the influence of each interference feature parameter on each transmission quality index is obtained. The i-th interference feature parameter is taken as the co-channel interference power (…). ), No. =1 (characterizing quality indicators) For example, by calculation... At some point =5、 =0.8, then this interference characteristic parameter is... Impact Contribution .

[0029] According to the formula Calculate the total influence contribution of each interference characteristic parameter. All By comparing the parameters, the interference characteristic parameter with the largest total impact contribution was selected as the dominant interference factor. .

[0030] (iv) Dynamic optimization decision-making module and parameter optimization scheme generation Transmission link parameter vector The parameters specifically include: modulation method parameters. (Optional values ​​are QPSK, 16QAM, and 64QAM), coding redundancy parameter (Optional values ​​are 1 / 2, 2 / 3, 3 / 4) Transmit power allocation parameters Channel bandwidth configuration parameters Polarization parameters (Optional values ​​are horizontal polarization, vertical polarization, and circular polarization), equalization and error correction parameters (Optional values ​​are convolutional code, Turbo code, and LDPC code), T=6.

[0031] Optimize objective function That is, to find a set of transmission link parameters P such that, under the current dominant interference factor Under this effect, the overall transmission quality evaluation value Q reaches its maximum. Among the constraints, and The parameters are determined based on the equipment hardware performance and transmission standards, such as the transmit power allocation parameters. of =10W =50W; the equality constraint function G(P) is the channel bandwidth configuration parameter. With modulation parameters The matching relationship, i.e., the modulation efficiency, where k is a coefficient. The data rate is represented by the business data rate; the inequality constraint function H(P) is the coding redundancy parameter. With Equilibrium and Error Correction Parameters The combination of constraints, namely the preset threshold for error correction capability.

[0032] The above optimization problem is solved using a genetic algorithm. Through operations such as population initialization, selection, crossover, and mutation, the algorithm iteratively searches for the transmission link parameter vector that satisfies the constraints and maximizes the objective function. This leads to the formation of a parameter optimization scheme.

[0033] (v) Closed-loop feedback reconstruction module and parameter reconstruction and feedback verification The closed-loop feedback reconstruction module sends the parameter optimization scheme to the transmitting and receiving devices of the broadcast television microwave transmission link through a remote configuration interface. After receiving the parameters, the devices complete the parameter reconstruction through a seamless switching mechanism. The entire process does not interrupt the broadcast television service.

[0034] After parameter reconstruction is completed, the multi-dimensional interference sensing module continuously collects multi-dimensional interference characteristic parameters of the reconstructed link at a preset period (e.g., 1 second / time), and the multi-index quality assessment module calculates the comprehensive transmission quality assessment value in real time. .Will With preset transmission quality threshold If a comparison is made, < This triggers the decoupling modeling module to recalculate the contribution of interference feature parameters and identify the dominant interference factor; the dynamic optimization decision module to regenerate the parameter optimization scheme; and the closed-loop feedback reconstruction module to execute parameter reconstruction again, until... ≥ Complete closed-loop adaptive optimization.

[0035] III. Clarification of Formulas and Terminology Normalization formula: used to convert original indicators with different dimensions and different value ranges into normalized values ​​in the interval [0,1], eliminating the influence of dimensions and making each indicator comparable; among them, "the larger the better" indicators refer to indicators with larger values ​​and better transmission quality (such as signal-to-noise ratio), and "the smaller the better" indicators refer to indicators with smaller values ​​and better transmission quality (such as bit error rate).

[0036] Transmission quality indicators and comprehensive evaluation value formula: By using a weighted summation method, the normalized values ​​of each sub-indicator are integrated into a single-dimensional evaluation value. The weight coefficient reflects the importance of each indicator in the transmission quality evaluation.

[0037] Nonlinear coupling relationship model: Establish the mapping relationship between interference characteristic parameters and transmission quality indicators to achieve a quantitative description from interference characteristics to quality status.

[0038] Influence contribution formula: The ratio of partial derivatives to parameter values ​​and index values ​​quantifies the relative influence of a single interference characteristic parameter on transmission quality indicators. The total influence contribution comprehensively reflects the impact of a single interference characteristic parameter on the overall transmission quality.

[0039] Optimize the objective function and constraints: The objective function clarifies the direction of parameter optimization, and the constraints ensure that the optimized parameters meet the requirements of equipment performance and transmission standards, and avoid parameters exceeding the reasonable range, which could lead to transmission abnormalities.

[0040] The core working principle of this system and method revolves around a full-process adaptive mechanism of "interference perception - quality assessment - factor identification - parameter optimization - closed-loop feedback". First, a multi-dimensional interference sensing module comprehensively collects interference characteristic parameters of the transmission link from the frequency domain, time domain, spatial domain, and polarization dimensions, constructing a complete interference characteristic dataset to provide a foundation for subsequent analysis. Second, based on a multi-dimensional transmission quality index system, interference characteristics are correlated with physical layer performance, transmission stability, and service perceived quality. A comprehensive evaluation result is obtained through quantitative calculation, accurately reflecting the transmission quality status. Next, a nonlinear coupling relationship model is used to establish a mapping relationship between interference characteristics and transmission quality. The influence weights of different interferences are separated through contribution calculation, identifying the dominant interference factor and solving the problem that traditional solutions cannot distinguish interference types. Subsequently, a constrained optimization objective function is constructed based on the characteristics of the dominant interference factor, solving for the transmission link parameter combination that matches the current interference state, achieving differentiated and targeted parameter optimization. Finally, a closed-loop feedback reconstruction module completes online parameter reconstruction while continuously collecting interference and quality data of the reconstructed link, repeatedly verifying the optimization effect until the transmission quality meets the preset requirements, forming a closed loop of "perception-decision-execution-verification." This ensures that the system continuously and dynamically adjusts parameters in complex electromagnetic environments, maintaining the stability and reliability of broadcast television microwave transmission.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An anti-interference dynamic optimization system for microwave transmission quality in broadcast television, characterized in that, include: The multi-dimensional interference sensing module collects multi-dimensional interference characteristic parameters of the broadcast television microwave transmission link and outputs a set of interference characteristic parameters. The multi-index quality assessment module is connected to the multi-dimensional interference sensing module, receives a set of interference characteristic parameters, constructs a multi-dimensional transmission quality index system, and calculates a comprehensive transmission quality assessment value. The coupling relationship decoupling modeling module is connected to the multi-index quality assessment module. It receives the comprehensive transmission quality assessment value and the set of interference characteristic parameters, establishes a nonlinear coupling relationship model between interference characteristic parameters and transmission quality indicators, calculates the contribution of each interference characteristic parameter to the transmission quality, and identifies the dominant interference factor. The dynamic optimization decision module is connected to the coupling relationship decoupling modeling module, receives the dominant interference factor, constructs the parameter optimization objective function and constraints, and generates a transmission link parameter optimization scheme. The closed-loop feedback reconstruction module is connected to the dynamic optimization decision module and the multi-dimensional interference perception module. It receives the parameter optimization scheme and sends it to the broadcast television microwave transmission link to perform parameter reconstruction. It collects the interference characteristic parameters of the reconstructed link in real time and feeds them back to the multi-index quality evaluation module to form a closed-loop adaptive optimization.

2. The anti-interference broadcast television microwave transmission quality dynamic optimization system according to claim 1, characterized in that, The multi-dimensional transmission quality index system includes characterization quality indexes, stability indexes, and service perception quality indexes.

3. The anti-interference broadcast television microwave transmission quality dynamic optimization system according to claim 1, characterized in that, The construction of the nonlinear coupling relationship model, the calculation of the contribution of interference feature parameters, and the identification of the dominant interference factor are achieved through the following formula: Let the interference feature parameter vector be... ,in For the first There are N interference feature parameters, where N is the number of interference feature parameters. ∈{1,2,...,N}; Establish a nonlinear coupling model between interference characteristic parameters and transmission quality indicators: in, For transmission quality index vector, It is a multivariate nonlinear mapping function; Let K be the model parameter vector, and K be the number of model parameters. Calculate the first One interference characteristic parameter For the first Transmission quality indicators Impact Contribution : 。 4. The anti-interference broadcast television microwave transmission quality dynamic optimization system according to claim 1, characterized in that, The parameter optimization scheme is generated by constructing and solving the following objective function and constraints: Let the transmission link parameter vector be... There are 1, 2, ..., T transmission link parameters, where T is the number of transmission link parameters, and t∈{1,2,...,T}. Optimize the objective function: in, Let P be the transmission link parameter and the dominant interference factor be... Comprehensive evaluation value of transmission quality at that time; Constraints: .

5. The anti-interference broadcast television microwave transmission quality dynamic optimization system according to claim 1, characterized in that, The multidimensional interference characteristic parameters include microwave signal-to-noise ratio, bit error rate, packet loss rate, spectrum occupancy characteristic parameters, sidelobe interference energy parameters, polarization mismatch characteristic parameters, and time-domain fluctuation characteristic parameters.

6. The anti-interference broadcast television microwave transmission quality dynamic optimization system according to claim 4, characterized in that, The transmission link parameters include modulation parameters, coding redundancy parameters, transmit power allocation parameters, channel bandwidth configuration parameters, polarization parameters, and equalization and error correction parameters.

7. The anti-interference broadcast television microwave transmission quality dynamic optimization system according to claim 1, characterized in that, The closed-loop feedback reconstruction module does not interrupt the broadcast television service when performing parameter reconstruction. After reconstruction, the comprehensive evaluation value of transmission quality is verified by the multi-index quality evaluation module. When the evaluation value is lower than the preset threshold, the dynamic optimization decision module is triggered to generate a parameter optimization scheme again.

8. A method for dynamic optimization of microwave transmission quality for broadcast television with anti-interference capabilities, characterized in that, Includes the following steps: Step 1: Collect multi-dimensional interference characteristic parameters of the broadcast television microwave transmission link and construct an interference characteristic parameter vector. ; Step 2: Construct a multi-dimensional transmission quality index system and calculate the quality indicators. Stability indicators Business perception quality indicators and the overall transmission quality assessment value Q; Step 3: Establish a nonlinear coupling relationship model Y=F(X;Θ), and calculate the total influence contribution of each interference characteristic parameter. Identify the dominant interference factor ; Step 4: Based on the objective function and constraints, solve for the transmission link parameter vector. This leads to the formation of a parameter optimization scheme; Step 5: Send the parameter optimization plan to the broadcast television microwave transmission link and perform parameter reconstruction; Step 6: Collect multi-dimensional interference characteristic parameters of the reconstructed transmission link, and repeat Step 2 to calculate the comprehensive evaluation value of the reconstructed transmission quality. ,like < Then return to steps 3 through 5 until... ≥ ,in This is a preset transmission quality threshold.

9. The method for dynamic optimization of radio and television microwave transmission quality against interference according to claim 8, characterized in that, The multidimensional interference feature parameters collected in step 1 are obtained through frequency domain sensing, time domain sensing, and spatial and polarization sensing. The frequency domain sensing obtains the spectrum occupancy status and the energy distribution parameters of co-frequency and adjacent-frequency interference. The time domain sensing obtains the time duration characteristics, burst characteristics, and periodic fluctuation parameters of the interference signal. The spatial and polarization sensing obtains polarization mismatch information and spatial directional interference parameters.

10. The method for dynamic optimization of radio and television microwave transmission quality against interference according to claim 8, characterized in that, In step 4, the objective function is solved using a numerical optimization algorithm, which includes any one of gradient descent, genetic algorithm, or particle swarm optimization algorithm.

Images