Wireless communication spectrum optimization method and system based on artificial intelligence

Abstract

The invention relates to the technical field of communication spectrum optimization, in particular to a wireless communication spectrum optimization method and system based on artificial intelligence, and the method comprises the following steps: collecting network spectrum usage data, carrying out the unified integration of data formats, extracting network loads and user demand indexes, and evaluating the spectrum utilization rate and the user satisfaction degree. And constructing an initial spectrum configuration scheme. According to the invention, dynamic configuration of spectrum resources is realized through integration of multi-source network spectrum usage data, especially spectrum allocation is automatically adjusted through real-time data analysis in a high-demand period, the spectrum utilization efficiency is significantly improved, adaptive management is introduced, and the spectrum utilization efficiency is improved. The spectrum allocation is allowed to quickly respond according to the change of the real-time network load and the user demand, the quality and the response speed of the network service are greatly improved, the effectiveness of the spectrum allocation after adjustment is further verified through the simulation test in the actual network environment, the network can be ensured to maintain excellent performance under different conditions, and the user experience is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of communication spectrum optimization, and in particular to a method and system for wireless communication spectrum optimization based on artificial intelligence. Background Art

[0002] Communication spectrum optimization is a key technology area that involves the use of algorithms and system methods to enhance the efficiency of wireless communication spectrum utilization. With the widespread use of wireless devices and the sharp increase in mobile communication data demand, the effective management and optimization of limited spectrum resources has become a core issue in the development of communication technology. This technology area includes but is not limited to spectrum allocation, spectrum sharing, spectrum reuse, and interference management. By achieving higher data transmission rates and better service quality, communication spectrum optimization can support more users and more complex communication services. It integrates a variety of technologies such as machine learning, signal processing, cognitive radio, and network planning to improve spectrum utilization, reduce interference, and optimize network performance.

[0003] Among them, the wireless communication spectrum optimization method refers to adjusting and optimizing the use of spectrum in wireless communication systems through various technical means to improve the efficiency of spectrum use and network performance. It usually revolves around aspects such as dynamic allocation of spectrum, real-time monitoring and adaptive spectrum management. Its main purpose is to maximize the efficiency of spectrum resource use while ensuring communication quality and user experience. By optimizing spectrum allocation and utilization strategies, interference can be reduced, transmission speed and capacity can be increased, thereby supporting more users and services. It is widely used in mobile network operators, military communications, satellite communications and other occasions, and is of great significance in dealing with the problem of increasingly tight spectrum resources.

[0004] Existing technologies usually rely on preset allocation modes and limited adaptive adjustments in spectrum optimization, which makes it difficult to quickly respond to immediate changes in network load and user behavior. The static adjustment mechanism makes it impossible to effectively manage spectrum resources in situations with dense users or heavy data traffic. The common result is spectrum congestion or underutilization of resources, which affects the continuity and quality of services. In addition, due to the lack of efficient data analysis support, traditional spectrum management methods perform poorly in predicting and handling spectrum interference, especially in dynamic and complex communication environments, which limits the scalability and reliability of the network and slows down the pace of technological innovation and service optimization. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a wireless communication spectrum optimization method based on artificial intelligence.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a wireless communication spectrum optimization method based on artificial intelligence, comprising the following steps:

[0007] S1: Collect network spectrum usage data, integrate the data in a unified format, extract network load and user demand indicators, and build an initial spectrum configuration plan by evaluating spectrum utilization and user satisfaction;

[0008] S2: adopting the initial spectrum configuration scheme, iteratively updating the initial spectrum allocation scheme, evaluating the performance of the difference spectrum allocation schemes, comprehensively analyzing the performance indicators of all schemes according to the evaluation results, and outputting an improved spectrum configuration;

[0009] S3: Based on the improved spectrum configuration, real-time data flow and network usage status are monitored, network load and user behavior changes are tracked, and spectrum allocation is automatically adjusted to match corresponding changes according to the tracking results of network load and user behavior changes, and a dynamic spectrum adjustment result is obtained through cyclic optimization;

[0010] S4: Based on the dynamic spectrum adjustment results, an actual network environment simulation test is performed to verify the effectiveness of the spectrum allocation plan through measured data, confirm the final performance of the plan, and obtain a verified spectrum optimization configuration.

[0011] As a further solution of the present invention, the step of acquiring the initial spectrum configuration solution is:

[0012] S111: collect original spectrum usage data of network base stations, record frequency usage rate, signal strength and timestamp, perform data cleaning and format standardization, and generate a standardized network spectrum data set;

[0013] S112: Based on the standardized network spectrum data set, the formula is adopted:

[0014]

[0015] Calculate the adjusted signal strength difference index , and obtain the performance indicator data set, where It is The signal strength of each data point, is the average signal strength of all data points, is the corresponding frequency utilization rate, is the maximum value of all frequency utilizations, is the total number of data points;

[0016] S113: Based on the performance indicator data set, combined with network load and user satisfaction, the formula is adopted:

[0017]

[0018] Calculate the composite score , get the initial spectrum configuration scheme, where Representative The spectrum occupancy of the data points, Representative data points of user satisfaction, is the total number of data points.

[0019] As a further solution of the present invention, the step of evaluating the performance of the differential spectrum allocation scheme is:

[0020] S211: Based on the initial spectrum configuration scheme, adjust the existing configuration, optimize the spectrum configuration by loop iteration, and obtain an adjusted spectrum configuration scheme;

[0021] S212: Based on the adjusted spectrum configuration scheme, a high-fidelity network simulator is used to simulate performance under different network conditions to obtain a simulated data set;

[0022] S213: Analyze the simulation data set, evaluate the performance difference between the adjusted spectrum configuration scheme and the initial spectrum configuration scheme, and obtain a performance evaluation record.

[0023] As a further solution of the present invention, the step of obtaining the improved spectrum configuration is:

[0024] S221: Based on the performance evaluation record, statistically analyze the signal-to-interference ratio, transmission delay, and data throughput, and generate a performance indicator data set by calculating the standard deviation and the average value;

[0025] S222: Analyze the performance indicator data set and perform multi-attribute decision analysis using the formula:

[0026]

[0027] Calculate and output the comprehensive spectrum configuration score of the solution ,in, represents the signal-to-interference ratio, represents the transmission delay, Represents data throughput;

[0028] S223: According to the comprehensive spectrum configuration score of the scheme, the spectrum configuration scheme is readjusted to match the current network demand and technology development trend, and an improved spectrum configuration scheme is output.

[0029] As a further solution of the present invention, the steps of tracking the changes in network load and user behavior are:

[0030] S311: adopting the improved spectrum configuration, activating data flow monitoring, synchronously performing communication network status analysis, analyzing real-time transmitted data flow and communication network status, and generating real-time communication network monitoring records;

[0031] S312: Based on the real-time communication network monitoring record, extract network congestion and traffic anomaly data using the formula:

[0032]

[0033] Calculate communication network congestion index , generate the communication network load anomaly analysis results, where, Representative The data flow of network nodes, Represents the number of links to the corresponding node, represents the variance of network traffic;

[0034] S313: Based on the communication network load anomaly analysis results, user behavior monitoring is performed to track key user behavior changes, analyze the impact on the communication network status, and obtain a user behavior analysis report.

[0035] As a further solution of the present invention, the step of obtaining the dynamically adjusted spectrum result is:

[0036] S321: extracting key data points based on the communication network load anomaly analysis result and the user behavior analysis report, dynamically adjusting the spectrum configuration, and outputting a preliminary dynamic spectrum adjustment plan;

[0037] S322: Apply the preliminary dynamic spectrum adjustment scheme to monitor the impact on network performance in real time, iteratively optimize the preliminary dynamic spectrum adjustment scheme by analyzing actual traffic data after adjustment and user satisfaction feedback, and generate an optimized dynamic spectrum adjustment scheme;

[0038] S323: Based on the optimized dynamic spectrum adjustment scheme, combined with the current network demand and future demand forecast, the network load and user behavior changes are automatically matched to obtain a dynamic spectrum adjustment result.

[0039] As a further solution of the present invention, the step of obtaining the verified spectrum optimization configuration is:

[0040] S411: Based on the dynamic spectrum adjustment result, configure a simulation test platform of an actual network environment, start a spectrum experiment, and generate a network environment simulation test plan;

[0041] S412: Execute the network environment simulation test solution, monitor and record the data flow and network delay during the test, using the formula:

[0042]

[0043] Calculate the average load efficiency for each data point , evaluate the comprehensive performance of network load and delay, and obtain the measured data analysis results, among which, Representative The network traffic of each data point, that is, the size of each data packet in the simulated network environment, Represents the corresponding delay, that is, the time it takes for a data packet to be sent and received. is the total number of test data points;

[0044] S413: Using the measured data analysis results, evaluate the actual effectiveness and performance of the spectrum allocation scheme, adjust the spectrum configuration parameters to reveal the optimal network performance, and generate a verified spectrum optimization configuration.

[0045] A wireless communication spectrum optimization system based on artificial intelligence, comprising:

[0046] The data preprocessing module collects the original spectrum usage data of network base stations, performs data cleaning and format standardization, calculates the adjusted signal strength difference index, and obtains the initial spectrum configuration plan based on network load and user satisfaction;

[0047] The iterative optimization module optimizes the spectrum configuration by cyclic iteration based on the initial spectrum configuration scheme, simulates the performance under different network conditions, evaluates the performance difference, and obtains the performance evaluation record;

[0048] The configuration scoring module performs statistical analysis based on the performance evaluation records, calculates the comprehensive spectrum configuration score of the scheme, readjusts the spectrum configuration scheme to match the current network requirements and technology development trends, and outputs an improved spectrum configuration scheme;

[0049] The traffic monitoring module adopts the improved spectrum configuration to analyze the real-time transmitted data traffic and communication network status, extract network congestion and traffic anomaly data, calculate communication network congestion indicators, track key user behavior changes, analyze the impact on the communication network status, and obtain a user behavior analysis report;

[0050] The dynamic adjustment module extracts key data points based on the user behavior analysis report, performs dynamic adjustment of spectrum configuration, analyzes actual traffic data after adjustment and user satisfaction feedback, performs iterative optimization, matches network load and user behavior changes, and obtains dynamic spectrum adjustment results;

[0051] The performance verification module configures a simulation test platform and starts a spectrum experiment based on the dynamic spectrum adjustment results, monitors and records data flow and network delay during the test, calculates the average load efficiency of each data point, evaluates the actual effectiveness and performance of the spectrum allocation plan, and generates a verified spectrum optimization configuration.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are:

[0053] In the present invention, by integrating multi-source network spectrum usage data, dynamic configuration of spectrum resources is achieved, especially by automatically adjusting spectrum allocation through real-time data analysis during high-demand periods, significantly improving spectrum utilization efficiency, introducing adaptive management, allowing spectrum allocation to respond quickly to changes in real-time network load and user demand, greatly improving the quality and response speed of network services, and further verifying the effectiveness of the adjusted spectrum allocation through simulation tests in actual network environments, ensuring that the network can maintain excellent performance under different conditions and enhance user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a main step flow chart of the present invention;

[0055] Figure 2 A flowchart of obtaining the initial spectrum configuration scheme of the present invention;

[0056] Figure 3 A flow chart for evaluating the performance of the differential spectrum allocation scheme of the present invention;

[0057] Figure 4 A flowchart for obtaining the improved spectrum configuration of the present invention;

[0058] Figure 5 A flow chart for tracking changes in network load and user behavior of the present invention;

[0059] Figure 6 The present invention is a flowchart for dynamically adjusting the spectrum results;

[0060] Figure 7 This is a flow chart for obtaining the spectrum optimization configuration verified by the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0063] See also Figure 1 , a wireless communication spectrum optimization method based on artificial intelligence, comprising the following steps:

[0064] S1: Collect network spectrum usage data, integrate the data in a unified format, extract network load and user demand indicators, and build an initial spectrum configuration plan by evaluating spectrum utilization and user satisfaction;

[0065] S2: adopt the initial spectrum configuration scheme, iteratively update the initial spectrum allocation scheme, evaluate the performance of the different spectrum allocation schemes, comprehensively analyze the performance indicators of all schemes based on the evaluation results, and output the improved spectrum configuration;

[0066] S3: Based on improved spectrum configuration, real-time data flow and network usage status are monitored, network load and user behavior changes are tracked, and spectrum allocation is automatically adjusted to match the corresponding changes according to the tracking results of network load and user behavior changes. Through cyclic optimization, the spectrum adjustment results are obtained dynamically.

[0067] S4: Based on the results of the dynamic spectrum adjustment, conduct a simulation test of the actual network environment, verify the effectiveness of the spectrum allocation plan through measured data, confirm the final performance of the plan, and obtain the verified spectrum optimization configuration.

[0068] The initial spectrum configuration plan includes network load indicators, user demand indicators and spectrum utilization; the improved spectrum configuration includes performance difference analysis results, comprehensive performance indicators and selected spectrum allocation plans; the dynamic spectrum adjustment results include spectrum allocation dynamic records, network usage response results and behavior change adaptability analysis results; the verified spectrum optimization configuration includes network environment adaptability analysis records, configuration efficiency evaluation results and performance stability analysis results.

[0069] See also Figure 2 , the steps to obtain the initial spectrum configuration plan are:

[0070] S111: collect original spectrum usage data of network base stations, record frequency usage rate, signal strength and timestamp, perform data cleaning and format standardization, and generate a standardized network spectrum data set;

[0071] The original spectrum usage data of each network base station is collected, including frequency utilization rate, signal strength and timestamp. In the process of data cleaning and format standardization of the original data, the outliers and missing values ​​in the data must be identified and processed first, such as replacing missing data points with the median or mean. For the processing of extreme values, it is necessary to select appropriate methods according to the distribution of the data, such as trimming or conversion. Subsequently, the data is formatted to ensure that all data meet the requirements of subsequent processing, such as converting the timestamp into a standard format and adjusting the frequency utilization rate to a unified percentage for further analysis and processing to generate a standardized network spectrum data set.

[0072] S112: Based on the standardized network spectrum dataset, the formula is:

[0073]

[0074] Calculate the adjusted signal strength difference index , and obtain the performance indicator data set, where It is The signal strength of each data point, is the average signal strength of all data points, is the corresponding frequency utilization rate, is the maximum value of all frequency utilizations, is the total number of data points;

[0075] There is a set of actual collected signal strength data , average signal strength , the corresponding frequency utilization rate , maximum frequency utilization The specific calculation process is as follows:

[0076]

[0077]

[0078]

[0079]

[0080] The results show that the adjusted signal strength difference index obtained by the weighted average method is 1.988, reflecting the comprehensive effect between signal strength and frequency utilization rate. The value of 1.988 is closer to 2, indicating that in each base station of the network, the change of signal strength has a certain correlation with the density of frequency use, but the fluctuation range is not extreme. It reflects the stability of signal strength and the balance of the network under the current frequency utilization rate, which can help network operators identify which areas have large changes in signal strength and need to adjust spectrum allocation or take signal optimization measures.

[0081] S113: Based on the performance indicator data set, combined with network load and user satisfaction, the formula is used:

[0082]

[0083] Calculate the composite score , get the initial spectrum configuration scheme, where Representative The spectrum occupancy of the data points, Representative data points of user satisfaction, is the total number of data points;

[0084] Collect spectrum occupancy data and user satisfaction data , the total number of data points The specific calculation process is as follows:

[0085]

[0086]

[0087]

[0088] The results show that the overall score is 0.8537. The score of 0.8537 indicates that there are areas with slight load pressure or user dissatisfaction, but overall the network performs well and the user experience is stable. Targeted spectrum optimization or resource reallocation can be carried out based on this data.

[0089] See also Figure 3 , the evaluation steps of the performance of the differential spectrum allocation scheme are:

[0090] S211: Based on the initial spectrum configuration plan, adjust the existing configuration, optimize the spectrum configuration by loop iteration, and obtain an adjusted spectrum configuration plan;

[0091] Starting from the initial spectrum configuration plan, adjust the existing configuration according to the current business needs and frequency conflicts. By iteratively adjusting the spectrum configuration, ensure the optimal use of spectrum resources. The application effect of the adjusted spectrum configuration plan in network operation is the key. To this end, it is necessary to monitor the spectrum utilization in real time and adjust the iterative algorithm to cope with the changing network conditions and needs. For example, by adjusting the parameters in the algorithm, such as the minimum interference limit and priority rules, balance business needs and spectrum efficiency to ensure that all users and services can access spectrum resources fairly and effectively. The dynamic adjustment strategy needs to be adjusted based on actual data to achieve the best spectrum configuration.

[0092] S212: Based on the adjusted spectrum configuration scheme, a high-fidelity network simulator is used to simulate the performance under different network conditions to obtain a simulated data set;

[0093] The adjusted spectrum configuration scheme is applied to a high-fidelity network simulator to evaluate its performance in various environments by simulating different network loads and interference situations, such as network congestion and spectrum sharing. The high-fidelity network simulator can accurately reflect the various complex situations in the actual network environment, thereby providing predictive performance data about different configuration schemes before actual deployment. Data is the key to evaluating the efficiency of different spectrum configuration schemes, which can help operators optimize their network configuration and ensure that network performance is not affected during high-demand periods or when spectrum resources are limited. The simulation test results will be used to compare and evaluate the performance of different configuration schemes to select the spectrum configuration that best suits current and future needs.

[0094] S213: Analyze the simulation data set, evaluate the performance difference between the adjusted spectrum configuration scheme and the initial spectrum configuration scheme, and obtain a performance evaluation record;

[0095] The collected signal-to-interference ratio (SIR), transmission delay (TD) and data throughput (TP) are the performance test results obtained from the network simulator. Each parameter reflects different aspects of network performance, using the following formula:

[0096]

[0097] Calculate comprehensive performance indicators; signal-to-interference ratio is an indicator to measure the degree of network interference, transmission delay is the time required for data to be transmitted from one point to another, and data throughput is the amount of data that the network can handle per unit time. The value of the parameter depends on the actual conditions of the simulation test. For example, if the simulated network load is the data traffic during peak hours, then the parameter will reflect the network performance under high load conditions. The results show the pros and cons of the overall performance of the network and are used for the overall performance evaluation of the solution.

[0098] See also Figure 4, the steps for obtaining the improved spectrum configuration are:

[0099] S221: Based on the performance evaluation records, statistical analysis is performed on the signal-to-interference ratio, transmission delay, and data throughput, and a performance indicator data set is generated by calculating the standard deviation and the average value;

[0100] Key performance data, including signal-to-interference ratio, transmission delay, and data throughput, are collected from performance evaluation reports and processed in detail using statistical analysis methods. First, the mean and standard deviation of each indicator are calculated, and then statistical data are used to identify any outliers or trends. This not only provides accurate performance data, but also enhances the explanatory power of the data set, thereby ensuring the accuracy and depth of the analysis. As the basis for comprehensive performance evaluation, it provides solid data support for subsequent analysis. Through detailed data processing, it is possible to more accurately identify which configuration schemes may cause performance bottlenecks or provide optimization space, thereby guiding the formulation of more effective spectrum configuration strategies.

[0101] S222: Analyze the performance indicator data set and conduct multi-attribute decision analysis using the formula:

[0102]

[0103] Calculate and output the comprehensive spectrum configuration score of the solution ,in, represents the signal-to-interference ratio, represents the transmission delay, Represents data throughput;

[0104] Represents the square value of the signal-to-interference ratio, which measures the network's ability to maintain communication quality under co-channel interference. Represents the square value of the transmission delay, which indicates the speed at which data is transmitted from the source to the destination. Represents the square value of data throughput, reflecting the efficiency of the network in processing data per unit time. Considering that the square root of the parameters can balance the influence of various indicators and thus provide a fair comparison benchmark, this formula provides a comprehensive spectrum configuration score for a scheme by taking the square root of the sum of the squares of the three parameters and dividing it by 3. It is used to compare the performance of different configuration schemes. Assume , , ,but Calculated as The score represents the overall network performance of the configuration, with a score between 0 and 100 (standardized range), and 67.49 shows that the configuration has a good performance level, but there is still room for improvement. This value indicates that the configuration has reached a certain standard in maintaining communication quality, minimizing latency, and maximizing data processing capacity, but it is not optimal.

[0105] S223: Readjust the spectrum configuration plan according to the comprehensive spectrum configuration score of the plan to match the current network requirements and technology development trends, and output an improved spectrum configuration plan;

[0106] Based on the comprehensive spectrum configuration score of the plan, specific adjustments to the spectrum configuration are made. The process involves in-depth analysis and redesign of the existing spectrum allocation plan. Taking into account the diversity of network needs and the latest trends in technological development, adjustment strategies include increasing or decreasing channel bandwidth, changing channel allocation priorities, and introducing new spectrum sharing technologies. The adjustments are aimed at improving the overall performance of the network and enhancing resource utilization efficiency. The implementation of the improvement plan takes into account the user's service quality requirements and the network's operational efficiency. Each adjustment is based on the results of the comprehensive score and performance test to ensure that the improvement measures can actually solve existing problems and optimize the network's performance output. Through policy adjustments, network managers can ensure that the communication spectrum structure is more in line with current and expected business needs, thereby improving user satisfaction.

[0107] See also Figure 5 ,The steps to track changes in network load and user behavior are:

[0108] S311: adopt improved spectrum configuration, activate data flow monitoring, synchronously perform communication network status analysis, analyze real-time transmitted data flow and communication network status, and generate real-time communication network monitoring records;

[0109] The monitoring equipment is deployed with improved spectrum configuration. The equipment can capture and analyze the data traffic flowing through the communication network and the current network status in real time. After the equipment is deployed, the monitoring of real-time data flow and the analysis of communication network status are integrated into a real-time network monitoring report, which includes not only the peaks and troughs of data traffic, but also the monitoring of data packet size, speed and its path from source to destination. Through this comprehensive data collection, any abnormal behavior or potential network congestion points in the communication network can be observed in real time, which is crucial for preventing communication network failures and optimizing network performance. After generating a real-time communication network monitoring report, network administrators can quickly respond to changes in network conditions and take appropriate measures to adjust and optimize the communication network spectrum configuration.

[0110] S312: Based on the real-time communication network monitoring records, extract network congestion and traffic anomaly data using the formula:

[0111]

[0112] Calculate communication network congestion indicators , generate the communication network load anomaly analysis results, where, Representative The data flow of network nodes Represents the number of links to the corresponding node, represents the variance of network traffic;

[0113] Assume that there are 5 nodes in the communication network, and the data flow of each node is 100, 150, 200, 250, and 300 MB / s respectively; Represents the number of connections of the corresponding node, assuming that there are 2, 3, 4, 5, and 6 connections respectively; Represents the variance of network traffic. The variance value calculated in actual monitoring is 4000MB^2 / s^2.

[0114] Calculate each node value:

[0115] For the first node:

[0116]

[0117] For the second node:

[0118]

[0119] For the third node:

[0120]

[0121] For the fourth node:

[0122]

[0123] For the fifth node:

[0124]

[0125] The sum will be Next, calculate the denominator Values:

[0126]

[0127] Calculate the final network congestion index :

[0128]

[0129] The results show that the current network congestion level is low, network traffic is evenly distributed among nodes, and there is no need to urgently adjust the spectrum configuration. If it is below a certain critical value, for example, less than 5 means there is no obvious congestion, indicating that the current network condition is good, the balance between traffic and number of connections is appropriate, and network resources are being used effectively without excessive concentration or idleness of resources, allowing network administrators to monitor continuously without immediate adjustment. This metric can serve as a basis for evaluating whether spectrum configuration needs to be adjusted, thereby improving user experience and network performance.

[0130] S313: Comprehensive communication network load anomaly analysis results, perform user behavior monitoring, track key user behavior changes, analyze the impact on the communication network status, and obtain a user behavior analysis report;

[0131] After analyzing the abnormal communication network load, user behavior monitoring is started. By tracking the behavior changes of key users, their specific impact on the network status is analyzed. The monitoring involves detailed records of user request types, access frequencies, and data upload and download volumes. Through data analysis models, future user behavior patterns and changes in their demand for network resources can be analyzed. The analysis results can help network administrators make more scientific decisions in resource allocation and network expansion plans. Based on the user behavior analysis report obtained through analysis, the communication spectrum configuration can be further optimized, thereby improving user experience.

[0132] See also Figure 6 , the steps to obtain the dynamic spectrum adjustment results are:

[0133] S321: extract key data points based on the communication network load anomaly analysis results and user behavior analysis report, dynamically adjust the spectrum configuration, and output a preliminary dynamic spectrum adjustment plan;

[0134] From the results of network load anomaly analysis and user behavior analysis reports, key data including user online time, data upload and download frequency and size, and communication volume between network nodes are accurately extracted. Through detailed data, the demand differences of various parts of the communication network are analyzed, including the identification of high-traffic demand areas and low-traffic demand areas. Based on the demand differences, a preliminary spectrum reconfiguration plan is dynamically generated, and necessary adjustments are made according to the actual network usage conditions to optimize the data flow demand satisfaction during peak hours and the spectrum utilization efficiency during off-peak hours, so as to carry out field testing and application.

[0135] S322: Apply the preliminary dynamic spectrum adjustment plan, monitor the impact on network performance in real time, iteratively optimize the preliminary dynamic spectrum adjustment plan by analyzing actual traffic data after adjustment and user satisfaction feedback, and generate an optimized dynamic spectrum adjustment plan;

[0136] When executing the initial dynamic spectrum adjustment plan, high-precision monitoring equipment is used to monitor the impact of the plan on network performance in real time. The monitoring data includes but is not limited to key performance indicators such as network latency, data transmission speed, and user connection stability. This monitoring activity aims to capture the immediate effects of the plan after implementation. By analyzing the actual traffic data after adjustment and the feedback obtained through user satisfaction surveys, machine learning and adaptive learning algorithms are used to continuously iterate and optimize spectrum allocation to ensure that each adjustment is based on the latest network usage data and user feedback. The optimized dynamic spectrum adjustment plan records the effect of each adjustment in detail in order to evaluate its impact on the overall network performance.

[0137] S323: Based on the optimized dynamic spectrum adjustment scheme, combined with the current network demand and future demand forecast, the network load and user behavior changes are automatically matched to obtain a dynamic spectrum adjustment result;

[0138] Before the optimized spectrum adjustment plan is fixed, the system will integrate the current network demand and future forecasts based on historical data and trend prediction models, consider the actual network usage and forecast data, and automatically adjust the spectrum configuration to adapt to possible changes in network load and user behavior in the future, ensuring that the adjustment plan can flexibly respond to upcoming changes, thereby maintaining maximum utilization and continuous optimization of network resources. The final dynamically adjusted spectrum results are set as the new standard for network management and implemented to improve the overall efficiency of the network and user experience, while ensuring that when future needs change, they can respond quickly and accurately to optimize network performance and service quality.

[0139] See also Figure 7 , the steps to obtain the verified spectrum optimization configuration are:

[0140] S411: Based on the result of the dynamic spectrum adjustment, a simulation test platform of the actual network environment is configured, a spectrum experiment is started, and a network environment simulation test plan is generated;

[0141] Based on the results of dynamically adjusting the spectrum, a simulation test platform for the actual network environment is configured. First, by selecting appropriate network simulator software and setting network parameters such as bandwidth limit, network delay, packet loss rate and error rate, the simulator creates a network model close to the real environment based on these parameters. Then, the results of dynamically adjusting the spectrum are used to guide the simulator's spectrum configuration, which includes spectrum allocation and priority setting to reflect the network traffic requirements of different data types, continuously monitoring the traffic and delay in the simulated network, and automatically adjusting the spectrum settings to respond to changes in network status. Through this series of settings and adjustments, a simulation test plan that comprehensively covers various network scenarios is finally generated.

[0142] S412: Execute the network environment simulation test plan, monitor and record the data flow and network delay during the test, using the formula:

[0143]

[0144] Calculate the average load efficiency for each data point , evaluate the comprehensive performance of network load and delay, and obtain the measured data analysis results, among which, Representative The network traffic of each data point, that is, the size of each data packet in the simulated network environment, Represents the corresponding delay, that is, the time it takes for a data packet to be sent and received. is the total number of test data points;

[0145] Indicates The network traffic of a data point, for example, a data point is the size of a network packet, assuming it is 100KB; Indicates the corresponding delay, assuming it is 50ms; is the total number of test data points, assuming there are 10 data points. By inserting the values, the calculation formula becomes ,The results show that after considering the factors of traffic and delay, the average load efficiency of each data point of the network system is 22.36. ,The numerical value helps network engineers understand the performance level under the current spectrum network configuration, and further adjust the spectrum configuration accordingly.

[0146] S413: using the measured data analysis results, evaluating the actual effectiveness and performance of the spectrum allocation scheme, adjusting the spectrum configuration parameters to reveal the optimal network performance, and generating a verified spectrum optimization configuration;

[0147] The actual effectiveness and performance of the spectrum allocation scheme are evaluated using the results of measured data analysis. In this process, a detailed statistical analysis is first performed on the data collected in the network simulation test, including the calculation of key performance indicators such as the packet transmission success rate, the rate of change of network delay, the total network throughput, and spectrum utilization. Analysis of these data helps identify the advantages and disadvantages in spectrum allocation. Based on these analysis results, the spectrum configuration parameters are further adjusted, such as adjusting the bandwidth of a specific frequency band or changing the spectrum allocation strategy, in order to optimize the efficiency of spectrum use. Finally, the adjusted configuration will be reapplied to the network environment to ensure the optimal network performance. The generated spectrum optimization configuration will guide actual network operations to ensure stable and efficient network operation.

[0148] A wireless communication spectrum optimization system based on artificial intelligence, comprising:

[0149] The data preprocessing module collects the original spectrum usage data of network base stations, performs data cleaning and format standardization, calculates the adjusted signal strength difference index, and obtains the initial spectrum configuration plan based on network load and user satisfaction;

[0150] The iterative optimization module optimizes the spectrum configuration based on the initial spectrum configuration plan through cyclic iteration, simulates the performance under different network conditions, evaluates the performance difference, and obtains the performance evaluation record;

[0151] The configuration scoring module performs statistical analysis based on performance evaluation records, calculates the comprehensive spectrum configuration score of the solution, readjusts the spectrum configuration solution to match the current network requirements and technology development trends, and outputs an improved spectrum configuration solution;

[0152] The traffic monitoring module adopts improved spectrum configuration to analyze the real-time data traffic and communication network status, extract network congestion and traffic anomaly data, calculate communication network congestion indicators, track key user behavior changes, analyze the impact on the communication network status, and obtain user behavior analysis reports;

[0153] The dynamic adjustment module extracts key data points based on user behavior analysis reports, dynamically adjusts spectrum configuration, analyzes actual traffic data and user satisfaction feedback after adjustment, performs iterative optimization, matches network load and user behavior changes, and obtains dynamic spectrum adjustment results.

[0154] The performance verification module dynamically adjusts the spectrum results, configures the simulation test platform and starts the spectrum experiment, monitors and records the data flow and network delay during the test, calculates the average load efficiency of each data point, evaluates the actual effectiveness and performance of the spectrum allocation plan, and generates a verified spectrum optimization configuration.

[0155] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A wireless communication spectrum optimization method based on artificial intelligence, characterized in that: The following steps are involved: Collect network spectrum usage data, integrate the data in a unified format, extract network load and user demand indicators, and build an initial spectrum configuration plan by evaluating spectrum utilization and user satisfaction; Adopting the initial spectrum configuration scheme, iteratively updating the initial spectrum allocation scheme, evaluating the performance of the difference spectrum allocation schemes, comprehensively analyzing the performance indicators of all schemes according to the evaluation results, and outputting an improved spectrum configuration; Based on the improved spectrum configuration, real-time data flow and network usage status are monitored, network load and user behavior changes are tracked, and spectrum allocation is automatically adjusted to match corresponding changes according to the tracking results of network load and user behavior changes, and a dynamically adjusted spectrum result is obtained through cyclic optimization; Based on the dynamic spectrum adjustment results, an actual network environment simulation test is performed to verify the effectiveness of the spectrum allocation plan through measured data, confirm the final performance of the plan, and obtain a verified spectrum optimization configuration.

2. The method for optimizing wireless communication spectrum based on artificial intelligence according to claim 1, characterized in that: The steps of obtaining the initial spectrum configuration scheme are: Collect the original spectrum usage data of network base stations, record the frequency usage rate, signal strength and timestamp, perform data cleaning and format standardization, and generate a standardized network spectrum data set; Based on the standardized network spectrum dataset, the formula is adopted: Calculate the adjusted signal strength difference index , and obtain the performance indicator data set, where It is The signal strength of each data point, is the average signal strength of all data points, is the corresponding frequency utilization rate, is the maximum value of all frequency utilizations, is the total number of data points; According to the performance indicator data set, combined with network load and user satisfaction, the formula is adopted: Calculate the composite score , get the initial spectrum configuration scheme, where Representative The spectrum occupancy of the data points, Representative data points of user satisfaction, is the total number of data points.

3. The method for optimizing wireless communication spectrum based on artificial intelligence according to claim 2, characterized in that: The evaluation steps of the performance of the differential spectrum allocation scheme are as follows: Based on the initial spectrum configuration scheme, adjusting the existing configuration, optimizing the spectrum configuration by cyclic iteration, and obtaining an adjusted spectrum configuration scheme; Based on the adjusted spectrum configuration scheme, a high-fidelity network simulator is used to simulate the performance under different network conditions to obtain a simulated data set; The simulated data set is analyzed to evaluate the performance difference between the adjusted spectrum configuration scheme and the initial spectrum configuration scheme, and a performance evaluation record is obtained.

4. The method for optimizing wireless communication spectrum based on artificial intelligence according to claim 3, characterized in that: The steps of obtaining the improved spectrum configuration are: Based on the performance evaluation records, statistically analyze the signal-to-interference ratio, transmission delay, and data throughput, and generate a performance indicator data set by calculating standard deviation and average value; Analyze the performance indicator data set and conduct multi-attribute decision analysis using the formula: Calculate and output the comprehensive spectrum configuration score of the solution ,in, represents the signal-to-interference ratio, represents the transmission delay, Represents data throughput; According to the comprehensive spectrum configuration score of the plan, the spectrum configuration plan is readjusted to match the current network requirements and technology development trends, and an improved spectrum configuration plan is output.

5. The method for optimizing wireless communication spectrum based on artificial intelligence according to claim 4, characterized in that: The steps for tracking changes in network load and user behavior are: Adopting the improved spectrum configuration, activating data flow monitoring, synchronously performing communication network status analysis, analyzing real-time transmitted data flow and communication network status, and generating real-time communication network monitoring records; Based on the real-time communication network monitoring records, network congestion and traffic anomaly data are extracted using the formula: Calculate communication network congestion indicators , generate the communication network load anomaly analysis results, where, Representative The data flow of network nodes Represents the number of links to the corresponding node, represents the variance of network traffic; Based on the communication network load anomaly analysis results, user behavior monitoring is performed, key user behavior changes are tracked, the impact on the communication network status is analyzed, and a user behavior analysis report is obtained.

6. The method for optimizing wireless communication spectrum based on artificial intelligence according to claim 5, characterized in that: The steps for obtaining the dynamic spectrum adjustment result are: Based on the communication network load anomaly analysis result and the user behavior analysis report, extract key data points, perform dynamic spectrum configuration adjustment, and output a preliminary dynamic spectrum adjustment plan; Apply the preliminary dynamic spectrum adjustment scheme, monitor the impact on network performance in real time, iteratively optimize the preliminary dynamic spectrum adjustment scheme by analyzing actual traffic data after adjustment and user satisfaction feedback, and generate an optimized dynamic spectrum adjustment scheme; Based on the optimized dynamic spectrum adjustment scheme, combined with the current network demand and future demand forecast, the network load and user behavior changes are automatically matched to obtain a dynamic spectrum adjustment result.

7. The method for optimizing wireless communication spectrum based on artificial intelligence according to claim 6, characterized in that: The steps for obtaining the verified spectrum optimization configuration are: Based on the dynamic spectrum adjustment result, configuring a simulation test platform of an actual network environment, starting a spectrum experiment, and generating a network environment simulation test plan; Execute the network environment simulation test plan, monitor and record the data flow and network delay during the test, and use the formula: Calculate the average load efficiency for each data point , evaluate the comprehensive performance of network load and delay, and obtain the measured data analysis results, among which, Representative The network traffic of data points, Represents the corresponding delay, is the total number of test data points; The measured data analysis results are used to evaluate the actual effectiveness and performance of the spectrum allocation scheme, adjust the spectrum configuration parameters to reveal the optimal network performance, and generate a verified spectrum optimization configuration.

8. A wireless communication spectrum optimization system based on artificial intelligence, characterized in that: The system is used to execute the wireless communication spectrum optimization method based on artificial intelligence according to any one of claims 1 to 7, comprising: The data preprocessing module collects the original spectrum usage data of network base stations, performs data cleaning and format standardization, calculates the adjusted signal strength difference index, and obtains the initial spectrum configuration plan based on network load and user satisfaction; The iterative optimization module optimizes the spectrum configuration by cyclic iteration based on the initial spectrum configuration scheme, simulates the performance under different network conditions, evaluates the performance difference, and obtains the performance evaluation record; The configuration scoring module performs statistical analysis based on the performance evaluation records, calculates the comprehensive spectrum configuration score of the scheme, readjusts the spectrum configuration scheme to match the current network requirements and technology development trends, and outputs an improved spectrum configuration scheme; The traffic monitoring module adopts the improved spectrum configuration to analyze the real-time transmitted data traffic and communication network status, extract network congestion and traffic anomaly data, calculate communication network congestion indicators, track key user behavior changes, analyze the impact on the communication network status, and obtain a user behavior analysis report; The dynamic adjustment module extracts key data points based on the user behavior analysis report, performs dynamic adjustment of spectrum configuration, analyzes actual traffic data after adjustment and user satisfaction feedback, performs iterative optimization, matches network load and user behavior changes, and obtains dynamic spectrum adjustment results; The performance verification module configures a simulation test platform and starts a spectrum experiment based on the dynamic spectrum adjustment results, monitors and records data flow and network delay during the test, calculates the average load efficiency of each data point, evaluates the actual effectiveness and performance of the spectrum allocation plan, and generates a verified spectrum optimization configuration.

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