A method and system for optimizing RedCap terminal access to 5G network

Abstract

The present invention relates to the field of wireless communication optimization technology, specifically a method and system for optimizing RedCap terminals accessing a 5G network, which collects data from RedCap terminals, extracts spectrum status information during the terminal access to the 5G network, divides the channel state into several channel state categories, calculates the transition probability between channel states, and generates a dynamic transition probability matrix. The present invention, by collecting data from RedCap terminals and extracting spectrum status information, innovatively categorizes the channel states, and combines the dynamic transition probability matrix construction method to effectively depict the changing patterns of the channel states. The frequency statistics and co-occurrence relationship analysis of the channel state categories make channel allocation more accurate, and can adjust the channel allocation strategy according to dynamic changes, thereby enhancing the flexibility and efficiency of resource scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication optimization, and in particular to a method and system for optimizing RedCap terminals accessing a 5G network. Background Art

[0002] The field of wireless communication optimization technology involves improving the performance and resource utilization efficiency of wireless communication networks, encompassing aspects such as network planning, signal transmission optimization, spectrum resource management, and device access control. This field aims to enhance wireless network stability, coverage, transmission rates, and user experience through technical means. It is widely used in 4G and 5G networks to meet the growing demand for data transmission and the number of connected devices.

[0003] The optimization method for RedCap terminals accessing 5G networks focuses on improving the access efficiency and communication performance of lightweight terminals (such as RedCap devices) in 5G network environments. This method aims to address issues such as insufficient resource allocation, signal interference, and transmission delays that lightweight terminals may face in high-density network scenarios, thereby enhancing the practical application of RedCap devices in fields such as the Internet of Things and industrial automation.

[0004] Existing technologies struggle to effectively characterize the changing characteristics of channel states in high-density network scenarios. The classification of channel categories and their dynamic transition characteristics have not been thoroughly studied, making it difficult for channel resource allocation to flexibly respond to dynamic changes in signal interference and network load. For example, when signal interference in a network changes frequently, existing technologies often cannot accurately predict the future distribution of channel states, resulting in delayed resource allocation. During resource matching, existing technologies struggle to comprehensively consider signal strength and interference probability, easily assigning incorrect priorities in scenarios with low interference but insufficient signal strength, thus impacting communication quality. Regarding spectrum block and load matching, existing technologies inadequately optimize path allocation and lack an effective balance between path length and resource weight, which can lead to reduced resource utilization efficiency. For example, in multi-task scenarios, existing methods may select long paths with low resource weights, resulting in wasted resources. In task access management, existing technologies have limited real-time channel adjustment capabilities and are unable to dynamically optimize the resource distribution of primary and secondary channels in the face of uneven task distribution. This can easily lead to irrational bandwidth allocation and increased fluctuations in task processing latency. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides an optimization method and system for RedCap terminals to access 5G networks. The technical solution is as follows:

[0006] A method for optimizing RedCap terminal access to 5G network, comprising the following steps:

[0007] S1: Collect data from RedCap terminals, extract spectrum status information when the terminals access the 5G network, divide the channel status into several channel state categories, calculate the transition probability between channel states, and generate a dynamic transition probability matrix;

[0008] S2: Based on the data in the dynamic transfer probability matrix, select the channel with the lowest interference probability and the highest signal strength when the RedCap terminal accesses the 5G network as the target channel, and generate a channel priority allocation result;

[0009] S3: Based on the channel priority allocation result, spectrum blocks and load distribution requirements are extracted, the allocation path between the spectrum blocks and the load distribution is optimized, multiple priority queues are divided, and the priority queues are associated with resource information in the allocation path to generate a cross-layer priority matching result;

[0010] S4: Based on the cross-layer priority matching result, monitor the access status of the terminal task, perform real-time analysis on the signal strength and interference probability in the task access status, adjust the task distribution of the primary channel and the auxiliary channel in real time according to the analysis result, and generate a hierarchical access result for the primary and auxiliary channels;

[0011] S5: Based on the hierarchical access results of the primary and secondary channels, the priority segments are classified according to delay sensitivity and throughput requirements, and bandwidth resource allocation of the priority segments is dynamically optimized to generate bandwidth dynamic allocation optimization results.

[0012] The present invention has improvements in that the dynamic transfer probability matrix includes the transfer probability of the channel state category, the signal strength range corresponding to the channel state category, and the interference probability range corresponding to the channel state category; the channel priority allocation result includes the target channel number, channel priority ranking, and the available time window corresponding to the channel; the cross-layer priority matching result includes priority queue classification, resource allocation path optimization parameters, and load distribution correlation information for the channel; the main control and auxiliary channel layered access results include the task distribution status of the main control channel, the task distribution status of the auxiliary channel, and the real-time channel load balancing parameters; the bandwidth dynamic allocation optimization result includes the priority segment bandwidth allocation ratio, bandwidth dynamic adjustment parameters, and priority segment resource allocation status information.

[0013] The improvements of the present invention include collecting data from RedCap terminals, extracting spectrum status information during terminal access to the 5G network, dividing the channel status into several channel status categories, calculating the transition probability between channel states, and generating a dynamic transition probability matrix. The specific steps are as follows:

[0014] S101: Based on the RedCap terminal signal strength, interference probability, and occupancy data, according to the channel state information collection standard, the signal strength data is grouped and labeled with strength levels, and the interference probability and occupancy data are divided into data ranges to generate a channel state classification data set;

[0015] S102: Based on the channel state classification data set, statistically analyze the occurrence frequency of each channel state category, sort the category frequencies according to their proportions, and statistically analyze the co-occurrence relationships between each channel state category to generate a channel state category statistical list;

[0016] S103: Calculate the transition probability of each channel state category to other categories in the statistical list of channel state categories in time series order, dynamically adjust through the joint weight of signal strength, interference probability and occupancy, and generate a dynamic transition probability matrix.

[0017] The present invention is improved in that the transition probability from each channel state category to other categories is calculated using the formula:

[0018] ;

[0019] Get the transition probability from category i to category j ;

[0020] in, is the actual number of times category i transfers to category j, is the total number of times category i transfers to all categories, is the normalized value of the signal strength of category i, calculated as: , is the signal strength of category i, and It is the maximum and minimum signal strength of all categories.

[0021] The present invention is improved in that, based on the data in the dynamic transfer probability matrix, the channel with the lowest interference probability and the highest signal strength when the RedCap terminal accesses the 5G network is selected as the target channel, and the specific steps of generating the channel priority allocation result are as follows:

[0022] S201: Based on the dynamic transition probability matrix and the current channel state data, the channel state distribution probability corresponding to each future time step is calculated, the channel state probability of each time step is extracted and recorded, and a channel state prediction distribution list is generated;

[0023] S202: Based on the channel state prediction distribution list, filter the channel states of future time steps according to the conditions that the signal strength value is within an upper limit range and the interference probability value is within a lower limit range to generate a channel state preferred distribution list;

[0024] S203: Based on the channel status preferred distribution list and in combination with the resource allocation requirements of the RedCap terminal, a corresponding relationship is established according to the priority order of the channels and the terminal requirements to generate a channel priority allocation result.

[0025] The present invention has the following improvements: for calculating the channel state distribution probability corresponding to each future time step, the formula is used:

[0026] ;

[0027] Get the next moment of the time step Channel Status The probability distribution of ;

[0028] in, is the time step Channel Status The probability distribution of is an element in the dynamic transition probability matrix, representing the channel state Transition to the next channel state The probability of is the total number of channel states.

[0029] The present invention has the following improvements: based on the channel priority allocation result, spectrum blocks and load distribution requirements are extracted therefrom, the allocation path between the spectrum blocks and the load distribution is optimized, multiple priority queues are divided, and the priority queues are associated with resource information in the allocation path. The specific steps of generating a cross-layer priority matching result are as follows:

[0030] S301: Based on the channel priority allocation result, extract the distribution information of the spectrum blocks and the load demand information and analyze their adaptability, set the edge weights between the two according to the adaptability value, and generate a spectrum block and load edge weight class list;

[0031] S302: Based on the spectrum block and load edge weight class list, perform a combined analysis on all spectrum block allocation paths, select the allocation path with the shortest path length and the highest total weight value, and generate the optimal resource allocation path;

[0032] S303: Based on the optimal resource allocation path, multiple priority queues are divided according to the correspondence between spectrum blocks and loads in the allocation path, and the priority queues are associated with resource allocation information in the path to generate a cross-layer priority matching result.

[0033] The present invention is improved in that, based on the cross-layer priority matching result, the access status of the terminal task is monitored, the signal strength and interference probability in the task access status are analyzed in real time, and the task distribution of the primary channel and the secondary channel is adjusted in real time according to the analysis result. The specific steps of generating the hierarchical access results of the primary and secondary channels are as follows:

[0034] S401: Based on the cross-layer priority matching result, extract resource allocation information for the terminal to access the 5G network, monitor the access status of the terminal task in real time, analyze the signal strength and interference probability of the access task, determine whether it exceeds a preset signal strength range, and generate a task access status analysis result;

[0035] S402: Based on the task access status analysis result, reallocate tasks with signal strengths below a preset signal strength range or with interference probability above a preset signal strength range to the auxiliary channel, maintain tasks within the preset signal strength range on the primary channel, record the task distribution of the primary and secondary channels, and generate a primary and secondary channel distribution table;

[0036] S403: Based on the primary and secondary channel distribution table, the real-time load conditions of the primary and secondary channels are adjusted, hierarchical access is re-established, and initial allocation of corresponding resources is performed to generate a primary and secondary channel hierarchical access result.

[0037] The present invention is improved in that, based on the hierarchical access results of the primary and secondary channels, priority segments are classified according to delay sensitivity and throughput requirements, and bandwidth resource allocation of the priority segments is dynamically optimized. The specific steps of generating the dynamic bandwidth allocation optimization results are as follows:

[0038] S501: Based on the hierarchical access results of the primary and secondary channels, extract priority segment data of the hierarchical access RedCap terminal, classify the priority segments according to the numerical ranges of delay sensitivity and throughput requirements, and generate priority segment classification results;

[0039] S502: Based on the priority segment classification result, the actual values ​​of the delay sensitivity and throughput requirement of the priority segment are compared with the preset delay threshold range and throughput requirement standard, and the corresponding bandwidth resources are refined and adjusted according to the comparison result to generate a priority segment bandwidth allocation list;

[0040] S503: Based on the priority segment bandwidth allocation list and in combination with real-time network load monitoring data, dynamically adjust the segment allocation parameters of each priority segment according to the load situation, optimize the allocation ratio and growth rate of bandwidth resources, and generate a bandwidth dynamic allocation optimization result.

[0041] A system for optimizing RedCap terminals for accessing 5G networks, the system comprising:

[0042] The channel state analysis module extracts channel state information based on RedCap terminal spectrum state data, classifies channel state categories, counts category frequencies and transition relationships, calculates channel state transition probabilities, and generates a dynamic transition probability matrix.

[0043] The channel optimization and allocation module selects the target channel with the lowest interference probability and the highest signal strength based on the dynamic transition probability matrix, calculates the compatibility of the spectrum block and the load distribution, optimizes the allocation path, divides the priority queues and associates the resource information, and generates a cross-layer priority matching result;

[0044] The task access optimization module monitors the task access status, analyzes the signal strength and interference probability based on the cross-layer priority matching results, adjusts the tasks that exceed the range to the auxiliary channel, optimizes the distribution of tasks between the main control and auxiliary channels, and generates the hierarchical access results of the main control and auxiliary channels;

[0045] The bandwidth dynamic allocation module classifies the priority segments according to the delay sensitivity and throughput requirements based on the hierarchical access results of the main control and auxiliary channels, dynamically adjusts the bandwidth allocation strategy, and optimizes the segment allocation parameters in combination with the real-time load to generate the bandwidth dynamic allocation optimization result.

[0046] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0047] By collecting data from RedCap terminals and extracting spectrum status information, the system innovatively categorizes channel states and, combined with a dynamic transition probability matrix construction method, effectively characterizes the changing patterns of channel states. Frequency statistics and co-occurrence analysis of channel state categories enable more precise channel allocation and can adjust channel allocation strategies based on dynamic changes, thereby enhancing the flexibility and efficiency of resource scheduling. During channel resource allocation, the system utilizes upper and lower bounds on signal strength and interference probability, combined with dynamic probability predictions to generate an optimal distribution of future channel states. This prioritized allocation strategy is then matched to terminal demand, significantly improving channel resource utilization and mitigating the impact of interference. In spectrum block and load allocation optimization, the system uses fitness analysis and path optimization to identify the optimal resource allocation path, further enhancing resource allocation efficiency and load balancing capabilities. In task access management, the system dynamically adjusts the task distribution of primary and secondary channels based on real-time monitoring of signal strength and interference probability. Bandwidth allocation is optimized based on latency sensitivity and throughput requirements, enabling dynamic adjustment and hierarchical management of bandwidth resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 This is a detailed flow chart of step S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of step S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of step S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of step S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of step S5 of the present invention;

[0055] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] See also Figure 1 , an embodiment of the present invention provides an optimization method for a RedCap terminal to access a 5G network, comprising the following steps:

[0061] S1: Collect data from RedCap terminals, extract spectrum status information when the terminals access the 5G network, divide the channel status into several channel state categories, calculate the transition probability between channel states, and generate a dynamic transition probability matrix;

[0062] S2: Based on the data in the dynamic transition probability matrix, the channel with the lowest interference probability and the highest signal strength when the RedCap terminal accesses the 5G network is selected as the target channel, and the channel priority allocation result is generated;

[0063] S3: Based on the channel priority allocation results, spectrum blocks and load distribution requirements are extracted, the allocation path between the spectrum blocks and load distribution is optimized, multiple priority queues are divided, and the priority queues are associated with the resource information in the allocation path to generate cross-layer priority matching results.

[0064] S4: Based on the cross-layer priority matching results, monitor the access status of terminal tasks, perform real-time analysis of the signal strength and interference probability of the task access status, adjust the task distribution of the primary and secondary channels in real time based on the analysis results, and generate the hierarchical access results of the primary and secondary channels;

[0065] S5: Based on the hierarchical access results of the primary and secondary channels, the priority segments are classified according to delay sensitivity and throughput requirements. By dynamically optimizing the bandwidth resource allocation of the priority segments, the bandwidth dynamic allocation optimization results are generated.

[0066] The dynamic transfer probability matrix includes the transfer probability of the channel state category, the signal strength range corresponding to the channel state category, and the interference probability range corresponding to the channel state category. The channel priority allocation result includes the target channel number, channel priority ranking, and the available time window corresponding to the channel. The cross-layer priority matching result includes priority queue classification, resource allocation path optimization parameters, and load distribution correlation information for the channel. The layered access results of the main control and auxiliary channels include the task distribution status of the main control channel, the task distribution status of the auxiliary channel, and the real-time channel load balancing parameters. The bandwidth dynamic allocation optimization results include the priority segment bandwidth allocation ratio, bandwidth dynamic adjustment parameters, and priority segment resource allocation status information.

[0067] See also Figure 2 ,collect data from RedCap terminals, extract spectrum status information when terminals access 5G networks, classify channel states into several channel state categories, calculate the transition probabilities between channel states, and generate a dynamic transition probability matrix. The specific steps are as follows:

[0068] S101: Based on the RedCap terminal signal strength, interference probability, and occupancy data, according to the channel state information collection standard, the signal strength data is grouped and labeled with strength levels, and the interference probability and occupancy data are divided into data ranges to generate a channel state classification data set;

[0069] First, signal strength sample data is collected. Based on actual testing, multiple signal strength sample values ​​are obtained. For example, in a test environment, the signal strength measured when the terminal is connected is 40, 60, 80, 90, and so on. Then, the distribution range of the signal strength samples is calculated, and the intervals are divided using distribution statistics. For example, the quantile method is used to divide the signal strength into weak signal, medium signal, and strong signal, corresponding to the intervals of 0-50, 50-75, and 75-100, respectively. Then, the interference probability data is divided. By monitoring the environmental interference situation, the probability value of the interference event is measured. For example, the probability of interference occurrence is 10%, 30%, and 60%, respectively, and the corresponding interference level is divided into low, medium, and high. Finally, the occupancy data is processed. By monitoring the channel occupancy rate when the terminal is connected, the occupancy rate interval is obtained. For example, the occupancy rate during the statistical time period is 20%, 50%, and 80%, respectively, which is divided into low occupancy, medium occupancy, and high occupancy. Combined with the above classification method, a channel status classification dataset containing signal strength groups, interference probability levels, and occupancy levels is generated.

[0070] S102: Based on the channel state classification data set, statistically analyze the occurrence frequency of each channel state category, sort the category frequencies according to their proportions, and statistically analyze the co-occurrence relationships between each channel state category to generate a channel state category statistical list;

[0071] The names and frequencies of all channel state categories are recorded as basic data. By parsing the channel state data in the time series, the frequency statistics method is used to obtain the number of occurrences of each category. For example, within a one-hour sampling period, the number of occurrences of channel states 1, 2, and 3 is 30, 20, and 10, respectively. The occurrence frequencies are then converted into percentages. For example, the occurrence frequency of category 1 is 50%, category 2 is 33.3%, and category 3 is 16.7%. They are sorted from high to low according to frequency. The co-occurrence relationship between channel state categories is then calculated, and the channel state data is correlated using the co-occurrence matrix. The co-occurrence matrix is ​​constructed by recording the frequency of consecutive occurrences of any two channel states in the time series. For example, the number of consecutive co-occurrences of category 1 and category 2 is 10, and the number of co-occurrences of category 1 and category 3 is 5. Finally, a frequency and co-occurrence relationship list of channel state categories is generated based on these statistical results.

[0072] S103: Calculate the transition probability of each channel state category to other categories in the statistical list of channel state categories in time series order, dynamically adjust the probability by the joint weight of signal strength, interference probability and occupancy, and generate a dynamic transition probability matrix;

[0073] To calculate the transition probability from each channel state category to other categories, the formula is used:

[0074] ;

[0075] Get the transition probability from category i to category j ;

[0076] in, is the actual number of times category i transfers to category j, based on time series data statistics. The specific statistical method is to traverse adjacent data pairs in the time series. For example, if the time series is [1,2,1,3], the number of times category 1 transfers to category 2 is 1, and the number of times category 2 transfers to category 1 is 0. is the total number of times category i transfers to all categories, which is obtained by summing the frequencies of category i transferring to other categories in the statistical time series. For example, the total number of times category 1 transfers to categories 2 and 3 is , is the normalized value of the signal strength of category i, reflecting the relative strength of the signal strength corresponding to category i and the signal strength of all categories. The normalization processing method is: , is the signal strength of category i, which is obtained by measuring the signal power when the monitoring device is connected to the RedCap terminal. For example, the signal strength of category 1 measured by the signal receiver is 70dBm, category 2 is 50dBm, and category 3 is 90dBm. and are the maximum and minimum values ​​of signal intensity in all categories, respectively, obtained through statistical analysis of time series data.

[0077] Collect channel state category time series data. For example, if the time series is [1, 2, 1, 3, 2, 1], record the number of transitions from category 1 to 2, 1 to 3, 2 to 1, and 2 to 3, respectively. According to the sequence statistics, we can get: , and record the signal strength range corresponding to each category. For example, the signal strength of category 1 is 70dBm, the signal strength of category 2 is 50dBm, and the signal strength of category 3 is 90dBm. The maximum signal strength is 90dBm and the minimum is 50dBm. Calculate the normalized signal strength value of each category according to the formula:

[0078] ;

[0079] Substitute the normalized signal strength value into the improved transition probability formula. For example, the transition probability from category 1 to category 2 is:

[0080] ;

[0081] The transition probability from category 1 to category 3 is:

[0082] ;

[0083] Finally, all transition probabilities are organized into a dynamic transition probability matrix, for example:

[0084] ;

[0085] The results show that each value in the matrix represents the probability relationship of a certain category transferring to other categories. For example, 0.666 in the matrix means that the probability of category 1 transferring to category 2 is 0.666. The accuracy of the calculation results can be verified by combining historical signal strength data. For example, comparing the actual signal strength measurements of category 1 and category 2, the results show that the transfer probability of the category with stronger signal is relatively larger. This dynamic transfer probability matrix can dynamically adjust and optimize the calculation results in combination with the standardized value of signal strength, improve the channel resource allocation efficiency of RedCap terminals accessing 5G networks, enhance access stability, and thus provide a reliable reference basis for network optimization.

[0086] See also Figure 3 Based on the data in the dynamic transfer probability matrix, the channel with the lowest interference probability and the highest signal strength when the RedCap terminal accesses the 5G network is selected as the target channel. The specific steps for generating the channel priority allocation result are as follows:

[0087] S201: Based on the dynamic transition probability matrix and the current channel state data, the channel state distribution probability corresponding to each future time step is calculated, the channel state probability of each time step is extracted and recorded, and a channel state prediction distribution list is generated;

[0088] To calculate the channel state distribution probability corresponding to each future time step, the formula is used:

[0089] ;

[0090] Get the next moment of the time step Channel Status The probability distribution of ;

[0091] in, is the time step Channel Status The probability distribution of the channel state at the current moment The probability value is directly collected based on the current channel state statistics. For example, it is obtained by recording the number of times the channel state appears in a time window and normalizing it. Assuming that the time window is 10 seconds and the number of times states 1, 2, and 3 appear is 6, 3, and 1, the probability distribution is , is an element in the dynamic transition probability matrix, representing the channel state Transition to the next channel state The probability of is the total number of channel states, representing the full range of channel states.

[0092] Dynamic transition probability matrix Based on the above statistical analysis, for example, the matrix is: ;

[0093] Current moment The channel state probability distribution For vectors, for example: ;

[0094] Calculate time steps state The distribution probability of:

[0095] ;

[0096] Enter the value:

[0097] ;

[0098] Calculate time steps state The distribution probability of:

[0099] ;

[0100] Enter the value:

[0101] ;

[0102] Calculate time steps state The distribution probability of:

[0103] ;

[0104] Enter the value:

[0105] ;

[0106] Finally, the time step The channel state probability distribution vector is: ;

[0107] The results show that by calculating the dynamic transition probability matrix, the distribution probability of the channel state at future moments can be predicted. For example, the predicted probability of state 1 is 0.45. Comparing it with the historical state distribution can verify the accuracy of the transfer matrix. The prediction result provides an important basis for subsequent channel allocation optimization.

[0108] S202: Based on the channel state prediction distribution list, the channel state of the future time step is screened according to the conditions that the signal strength value is within the upper limit range and the interference probability value is within the lower limit range to generate a channel state preferred distribution list;

[0109] The signal strength values ​​and interference probability values ​​corresponding to each future time step are extracted from the channel state prediction distribution list. The specific operation includes extracting the signal strength and interference probability parameters of each channel state from the predicted distribution data. For example, the signal strength data at the future time is recorded as 60dBm, 80dBm and 95dBm, and the interference probabilities are 20%, 5% and 10%, respectively. The channel states are compared and screened item by item according to the set conditions, and the channel states with signal strength higher than 75dBm and interference probability lower than 10% are marked as preferred channels. The occupancy data is further checked to verify the availability of the channel state. For example, channel states with channel occupancy rates higher than 80% are eliminated, and channel states with signal strength within the range, interference probability within the range, and occupancy rates that meet the conditions are screened out. Finally, a preferred channel state distribution list is generated.

[0110] S203: Based on the channel status preferred distribution list and the resource allocation requirements of the RedCap terminal, a corresponding relationship is established according to the priority order of the channels and the terminal requirements to generate a channel priority allocation result;

[0111] Combined with the resource allocation requirements of the RedCap terminal, the parameter data of the terminal resource requirements are extracted, such as bandwidth requirements, delay requirements and access signal strength. The specific operation is to compare the actual values ​​of the RedCap terminal resource requirements with the preferred distribution of channel status one by one. For example, the terminal requires a bandwidth of 50Mbps and a delay requirement of less than 20ms. The bandwidth and delay data of the preferred channel are obtained as 60Mbps and 15ms respectively. These channel status parameters are aligned with the terminal requirements and sorted according to the channel priority order. For example, the channel status that meets the requirements is prioritized and sorted from high to low according to the performance parameters of bandwidth and delay. The correspondence between terminal requirements and channel status is established in turn, and finally the channel priority allocation result is formed and the relevant parameters are recorded for subsequent verification and optimization.

[0112] See also Figure 4Based on the channel priority allocation results, spectrum blocks and load distribution requirements are extracted, the allocation path between spectrum blocks and load distribution is optimized, and multiple priority queues are divided. The priority queues are associated with the resource information in the allocation path to generate the cross-layer priority matching results. The specific steps are as follows:

[0113] S301: Based on the channel priority allocation result, extract the distribution information of the spectrum block and the load demand information and analyze their adaptability. Set the edge weight between the two according to the adaptability value, and generate a spectrum block and load edge weight class list;

[0114] The monitoring equipment records information such as the center frequency, bandwidth, and occupancy duration of each spectrum block. For example, the center frequency of the spectrum block is 2.4 GHz, the bandwidth is 20 MHz, and the occupancy duration is 5 ms. At the same time, information about the load requirements is obtained, including bandwidth requirements, data transmission delay, and payload size. For example, the payload bandwidth requirement is 15 MHz, the data transmission delay is 3 ms, and the payload size is 5 MB. An adaptability analysis is performed on the two sets of information. The adaptability parameter is obtained by comparing the difference between the bandwidth of the spectrum block and the bandwidth requirement of the payload, and the degree of overlap between the occupancy duration and the payload transmission duration. The edge weight between the spectrum block and the payload is set according to the adaptability value. The edge weight values ​​of each spectrum block and the payload are organized into an edge weight category list. The adaptation information and weight values ​​are recorded for subsequent path analysis.

[0115] S302: Based on the spectrum block and load edge weight class list, perform a combined analysis on all spectrum block allocation paths, select the allocation path with the shortest path length and the highest total weight value, and generate the optimal resource allocation path;

[0116] Take the spectrum block numbers and their corresponding edge weights from the list and combine all spectrum blocks using graph theory path analysis methods. For example, use the shortest path analysis method to traverse the paths of different spectrum block combinations. Record the length and total weight of each path. Define the path length as the number of spectrum blocks in the path, and the total weight as the sum of all edge weights in the path. For example, path 1 includes spectrum blocks 1, 3, and 5, has a path length of 3, and a total weight of 8.5. Path 2 includes spectrum blocks 2 and 4, has a path length of 2, and a total weight of 7.2. Sort all paths by shortest path length and highest total weight. Path 2 is selected as the optimal resource allocation path. Record the paths as a spectrum block number sequence for subsequent operations.

[0117] S303: Based on the optimal resource allocation path, multiple priority queues are divided according to the correspondence between spectrum blocks and loads in the allocation path, and the priority queues are associated with the resource allocation information in the path to generate a cross-layer priority matching result;

[0118] Based on the correspondence between spectrum blocks and loads in the path, the weight, bandwidth, latency, and other parameters of each spectrum block are obtained. The weights are sorted by size and divided into multiple priority queues. For example, the high-priority queue contains spectrum blocks with weights of 6.5 and 5.8, and the low-priority queue contains spectrum blocks with weights of 3.5 and 2.8. The spectrum blocks in the priority queues are associated with the load requirements. By screening the bandwidth requirements, latency requirements, and size of the loads, the high-priority queues are allocated to loads with high bandwidth requirements and low latency requirements, while the low-priority queues are allocated to loads with low bandwidth requirements and latency requirements. Finally, the priority queues are associated with the spectrum blocks of the resource allocation path to form a cross-layer priority matching result, which is used for subsequent spectrum resource allocation and optimization operations.

[0119] See also Figure 5 Based on the cross-layer priority matching results, the access status of the terminal tasks is monitored, the signal strength and interference probability in the task access status are analyzed in real time, and the task distribution of the primary and secondary channels is adjusted in real time according to the analysis results. The specific steps for generating the hierarchical access results of the primary and secondary channels are as follows:

[0120] S401: Based on the cross-layer priority matching results, the resource allocation information for the terminal to access the 5G network is extracted, the access status of the terminal task is monitored in real time, the signal strength and interference probability of the access task are analyzed, and whether it exceeds the preset signal strength range is determined, and the task access status analysis result is generated;

[0121] Extract resource allocation information for terminal access to the 5G network, monitor the access status of terminal tasks in real time, and analyze the signal strength and interference probability of access tasks. By recording the real-time signal strength and interference probability data of terminal access, for example, the signal strength is -75dBm and the interference probability is 20%, the signal strength is compared with the preset signal range (for example, -80dBm to -50dBm), and the interference probability is analyzed to see whether it exceeds the set threshold (for example, 10%). Determine whether the task access status is within the normal range, record the data points of signal strength and interference probability, and analyze the reasons for exceeding the range. For example, count the number of tasks that exceed the range, the extent of the exceedance, and its impact on the task access quality. Generate task access status analysis results, including the status classification of the access task (normal or abnormal) and its signal interference characteristics.

[0122] S402: Based on the task access status analysis results, tasks with signal strengths below a preset signal strength range or with interference probability above a preset signal strength range are reallocated to the secondary channel, tasks within the preset signal strength range are maintained on the primary channel, the task distribution between the primary and secondary channels is recorded, and a primary and secondary channel distribution table is generated;

[0123] Tasks with signal strengths lower than the preset signal strength range or interference probability higher than the preset threshold are reallocated to the auxiliary channel, and tasks within the preset signal strength range are maintained on the primary channel. Channel allocation information is recorded through a real-time allocation table. First, tasks that meet the signal strength and interference probability requirements are screened out and assigned to the primary channel. For example, task A with a signal strength of -65dBm and an interference probability of 8% is assigned to the primary channel. At the same time, tasks with signal strengths lower than the range or interference probability higher than the threshold are screened out, such as task B with a signal strength of -85dBm or an interference probability of 15%, and assigned to the auxiliary channel. The task distribution information of the primary and secondary channels is recorded, including task number, signal strength, interference probability, and assigned channel number, to generate a task distribution table for the primary and secondary channels, which serves as the basic data for subsequent channel resource allocation.

[0124] S403: Based on the primary and secondary channel distribution table, adjust the real-time load of the primary and secondary channels, re-establish layered access, initialize the allocation of corresponding resources, and generate a primary and secondary channel layered access result;

[0125] Adjust the real-time load of the main control channel and auxiliary channel, combine the task distribution information and channel load statistics, re-establish the channel architecture of hierarchical access, and divide the channel load information into three levels: high, medium and low. High-load channels correspond to channels with more than 80% of tasks and more than 75% of bandwidth utilization; medium-load channels correspond to channels with 50%-80% of tasks and 50%-75% of bandwidth utilization; low-load channels correspond to channels with less than 50% of tasks and less than 50% of bandwidth utilization. Adjust the auxiliary channel load to balance the task distribution, and initialize the allocation of channel resources according to the principle of hierarchical access. For example, high-load channels are allocated more bandwidth resources, medium-load channels are allocated medium bandwidth resources, and low-load channels are allocated the least bandwidth resources. Record the load tasks and resource allocation information of each access channel to generate the hierarchical access results of the main control and auxiliary channels.

[0126] See also Figure 6 Based on the hierarchical access results of the primary and secondary channels, priority segments are classified according to delay sensitivity and throughput requirements. By dynamically optimizing bandwidth resource allocation for priority segments, the specific steps for generating dynamic bandwidth allocation optimization results are as follows:

[0127] S501: Based on the hierarchical access results of the primary and secondary channels, priority segment data of the hierarchical access RedCap terminals is extracted, and the priority segments are classified according to the numerical ranges of delay sensitivity and throughput requirements to generate priority segment classification results;

[0128] Extract priority segment data from layered access RedCap terminals, analyze the delay sensitivity and throughput requirements in the priority segments, and record the actual delay value and throughput requirement value for each priority segment. For example, the delay values ​​are 5ms, 15ms, and 50ms, and the throughput requirements are 100Mbps, 50Mbps, and 10Mbps, respectively. Divide delay sensitivity into three intervals: high, medium, and low. High delay sensitivity corresponds to a delay value less than 10ms, medium delay sensitivity corresponds to a delay value between 10ms and 30ms, and low delay sensitivity corresponds to a delay value greater than 30ms. At the same time, divide throughput requirements into three intervals: high, medium, and low. High throughput requirements correspond to a throughput greater than 75Mbps, medium throughput requirements correspond to a throughput between 25Mbps and 75Mbps, and low throughput requirements correspond to a throughput less than 25Mbps. Classify the priority segment data according to the above interval standards and record the classification results for subsequent resource allocation optimization analysis to generate priority segment classification results.

[0129] S502: Based on the priority segment classification results, the actual values ​​of the delay sensitivity and throughput requirements of the priority segment are compared with the preset delay threshold range and throughput requirement standards. Based on the comparison results, the corresponding bandwidth resources are refined and adjusted to generate a priority segment bandwidth allocation list;

[0130] The actual latency sensitivity and throughput requirements of each priority segment are compared with the preset latency threshold range and throughput requirement standard. The actual latency and throughput requirement values ​​of each priority segment are extracted. For example, a priority segment with a latency of 8ms and a throughput requirement of 90Mbps is recorded and compared with the preset high latency sensitivity threshold (less than 10ms) and high throughput requirement standard (greater than 75Mbps). This priority segment is confirmed to meet the high priority classification and bandwidth resources are further allocated. Resources are allocated according to bandwidth adjustment rules. For example, priority segments with higher latency sensitivity are allocated more bandwidth resources, such as 50MHz bandwidth for high priority segments, 30MHz bandwidth for medium priority segments, and 20MHz bandwidth for low priority segments. A bandwidth allocation list for priority segments is generated for dynamic resource optimization and adjustment.

[0131] S503: Based on the priority segment bandwidth allocation list and in combination with real-time network load monitoring data, dynamically adjust the segment allocation parameters of each priority segment according to the load situation, optimize the allocation ratio and growth rate of bandwidth resources, and generate a dynamic bandwidth allocation optimization result;

[0132] Combined with real-time network load monitoring data, the current load parameters of the network are extracted. For example, if the network load rate is 70%, the number of tasks in the priority segments are 10 tasks in the high-priority segment, 20 tasks in the medium-priority segment, and 15 tasks in the low-priority segment. The segment allocation parameters of each priority segment are dynamically adjusted according to the load situation. For example, under high load conditions, the bandwidth allocation of the low-priority segment is reduced to 10MHz, while the bandwidth allocation of the high-priority segment is increased to 60MHz. The bandwidth allocation ratio of each priority segment is adjusted according to real-time load changes, and the bandwidth allocation growth rate of each priority segment is recorded. For example, the bandwidth allocation growth rate of the high-priority segment is 10Mbps / s, the bandwidth allocation growth rate of the medium-priority segment is 5Mbps / s, and the bandwidth allocation growth rate of the low-priority segment is 0Mbps / s. Finally, the dynamic bandwidth allocation optimization results are generated for subsequent network resource management and bandwidth allocation adjustment.

[0133] See also Figure 7 , an optimization system for RedCap terminals to access 5G networks, the system comprising:

[0134] The channel state analysis module extracts channel state information based on RedCap terminal spectrum state data, classifies channel state categories, counts category frequencies and transition relationships, calculates channel state transition probabilities, and generates a dynamic transition probability matrix.

[0135] The channel optimization and allocation module selects the target channel with the lowest interference probability and the highest signal strength based on the dynamic transition probability matrix, calculates the compatibility of spectrum blocks and load distribution, optimizes allocation paths, divides priority queues, associates resource information, and generates cross-layer priority matching results.

[0136] The task access optimization module monitors the task access status based on the cross-layer priority matching results, analyzes the signal strength and interference probability, adjusts out-of-range tasks to auxiliary channels, optimizes the distribution of tasks between the main and auxiliary channels, and generates hierarchical access results for the main and auxiliary channels;

[0137] The bandwidth dynamic allocation module classifies the priority segments according to delay sensitivity and throughput requirements based on the hierarchical access results of the main control and auxiliary channels, dynamically adjusts the bandwidth allocation strategy, and combines the real-time load optimization segment allocation parameters to generate the bandwidth dynamic allocation optimization results.

[0138] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0139] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0140] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0141] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0142] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0145] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0146] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0147] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for optimizing RedCap terminal access to 5G network, characterized in that: The following steps are involved: Collect data from RedCap terminals, extract spectrum status information when the terminals access the 5G network, divide the channel status into several channel state categories, calculate the transition probability between channel states, and generate a dynamic transition probability matrix; Based on the data in the dynamic transfer probability matrix, the channel with the lowest interference probability and the highest signal strength when the RedCap terminal accesses the 5G network is selected as the target channel, and a channel priority allocation result is generated; Based on the channel priority allocation result, spectrum blocks and load distribution requirements are extracted, an allocation path between the spectrum blocks and the load distribution is optimized, multiple priority queues are divided, and the priority queues are associated with resource information in the allocation path to generate a cross-layer priority matching result; Based on the cross-layer priority matching result, the access status of the terminal task is monitored, the signal strength and interference probability in the task access status are analyzed in real time, and the task distribution of the primary channel and the auxiliary channel is adjusted in real time according to the analysis result to generate the hierarchical access result of the primary and auxiliary channels; Based on the hierarchical access results of the primary and secondary channels, priority segments are classified according to delay sensitivity and throughput requirements, and bandwidth resource allocation of the priority segments is dynamically optimized to generate bandwidth dynamic allocation optimization results.

2. The optimization method for RedCap terminal accessing 5G network according to claim 1, characterized in that: The dynamic transfer probability matrix includes the transfer probability of the channel state category, the signal strength range corresponding to the channel state category, and the interference probability range corresponding to the channel state category. The channel priority allocation result includes the target channel number, channel priority ranking, and the available time window corresponding to the channel. The cross-layer priority matching result includes priority queue classification, resource allocation path optimization parameters, and load distribution correlation information for the channel. The master and auxiliary channel layered access results include the task distribution status of the master channel, the task distribution status of the auxiliary channel, and the real-time channel load balancing parameters. The bandwidth dynamic allocation optimization result includes the priority segment bandwidth allocation ratio, bandwidth dynamic adjustment parameters, and priority segment resource allocation status information.

3. The optimization method for RedCap terminal accessing 5G network according to claim 1, characterized in that: The specific steps for collecting data from RedCap terminals, extracting spectrum status information when the terminals access the 5G network, dividing the channel status into several channel status categories, and calculating the transition probabilities between channel states to generate a dynamic transition probability matrix are as follows: Based on the RedCap terminal signal strength, interference probability, and occupancy data, according to the channel state information collection standard, the signal strength data is grouped and labeled with strength levels, and the interference probability and occupancy data are divided into data ranges to generate a channel state classification data set; Based on the channel state classification data set, statistically analyzing the occurrence frequency of each channel state category, sorting the category frequencies according to their proportions, and statistically analyzing the co-occurrence relationships between each channel state category to generate a channel state category statistical list; The channel state category statistical list is arranged in a time series order, and the transition probability of each channel state category to other categories is calculated. Dynamic adjustment is performed through the joint weight of signal strength, interference probability and occupancy to generate a dynamic transition probability matrix.

4. The optimization method for RedCap terminal accessing 5G network according to claim 3, characterized in that: To calculate the transition probability from each channel state category to other categories, the formula is used: ； Get the transition probability from category i to category j ; in, is the actual number of times category i transfers to category j, is the total number of times category i transfers to all categories, is the normalized value of the signal strength of category i, calculated as: , is the signal strength of category i, and It is the maximum and minimum signal strength of all categories.

5. The optimization method for RedCap terminal accessing 5G network according to claim 1, characterized in that: Based on the data in the dynamic transfer probability matrix, the channel with the lowest interference probability and the highest signal strength when the RedCap terminal accesses the 5G network is selected as the target channel. The specific steps for generating the channel priority allocation result are as follows: Based on the dynamic transition probability matrix and the current channel state data, the channel state distribution probability corresponding to each future time step is calculated, the channel state probability of each time step is extracted and recorded, and a channel state prediction distribution list is generated; Based on the channel state prediction distribution list, the channel states of future time steps are screened according to the conditions that the signal strength value is within an upper limit range and the interference probability value is within a lower limit range to generate a channel state preferred distribution list; Based on the channel status preferred distribution list, combined with the resource allocation requirements of the RedCap terminal, a corresponding relationship is established according to the priority order of the channels and the terminal requirements to generate a channel priority allocation result.

6. The optimization method for RedCap terminal accessing 5G network according to claim 5, characterized in that: To calculate the channel state distribution probability corresponding to each future time step, the formula is used: ； Get the next moment of the time step Channel Status The probability distribution of ; in, is the time step Channel Status The probability distribution of is an element in the dynamic transition probability matrix, representing the channel state Transition to the next channel state The probability of is the total number of channel states.

7. The optimization method for RedCap terminal accessing 5G network according to claim 1, characterized in that: Based on the channel priority allocation result, spectrum blocks and load distribution requirements are extracted, the allocation path between the spectrum blocks and the load distribution is optimized, multiple priority queues are divided, and the priority queues are associated with resource information in the allocation path to generate a cross-layer priority matching result. The specific steps are as follows: Based on the channel priority allocation result, extracting the distribution information of the spectrum blocks and the load demand information and analyzing their adaptability, setting the edge weights between the two according to the value of the adaptability, and generating a spectrum block and load edge weight class list; Based on the spectrum block and load edge weight class list, performing a combined analysis on all spectrum block allocation paths, selecting the allocation path with the shortest path length and the highest total weight value, and generating the optimal resource allocation path; Based on the optimal resource allocation path, multiple priority queues are divided according to the correspondence between spectrum blocks and loads in the allocation path, and the priority queues are associated with resource allocation information in the path to generate a cross-layer priority matching result.

8. The optimization method for RedCap terminal accessing 5G network according to claim 1, characterized in that: Based on the cross-layer priority matching result, the access status of the terminal task is monitored, the signal strength and interference probability in the task access status are analyzed in real time, and the task distribution of the primary channel and the auxiliary channel is adjusted in real time according to the analysis result. The specific steps for generating the hierarchical access result of the primary and auxiliary channels are as follows: Based on the cross-layer priority matching result, the resource allocation information for the terminal to access the 5G network is extracted, the access status of the terminal task is monitored in real time, the signal strength and interference probability of the access task are analyzed, and it is determined whether it exceeds the preset signal strength range, and the task access status analysis result is generated; Based on the task access status analysis results, reallocate tasks with signal strengths below a preset signal strength range or with interference probability above a preset signal strength range to the auxiliary channel, maintain tasks within the preset signal strength range on the primary channel, record the task distribution of the primary channel and the auxiliary channel, and generate a primary and auxiliary channel distribution table; Based on the primary and secondary channel distribution table, the real-time load conditions of the primary and secondary channels are adjusted, hierarchical access is re-established, and initial allocation of corresponding resources is performed to generate a primary and secondary channel hierarchical access result.

9. The optimization method for RedCap terminal accessing 5G network according to claim 1, characterized in that: Based on the hierarchical access results of the primary and secondary channels, priority segments are classified according to delay sensitivity and throughput requirements, and bandwidth resource allocation for the priority segments is dynamically optimized to generate dynamic bandwidth allocation optimization results. Specific steps are as follows: Based on the hierarchical access results of the master and auxiliary channels, extracting priority segment data of the hierarchical access RedCap terminal, classifying the priority segments according to the numerical ranges of delay sensitivity and throughput requirements, and generating priority segment classification results; Based on the priority segment classification results, the actual values ​​of the delay sensitivity and throughput requirements of the priority segments are compared with the preset delay threshold range and throughput requirement standards, and the corresponding bandwidth resources are refined and adjusted according to the comparison results to generate a priority segment bandwidth allocation list; Based on the priority segment bandwidth allocation list and combined with real-time network load monitoring data, the segment allocation parameters of each priority segment are dynamically adjusted according to the load situation, the allocation ratio and growth rate of bandwidth resources are optimized, and a bandwidth dynamic allocation optimization result is generated.

10. An optimization system for RedCap terminals to access 5G networks, characterized in that: The method for optimizing RedCap terminal access to a 5G network according to any one of claims 1 to 9 is executed, wherein the system comprises: The channel state analysis module extracts channel state information based on RedCap terminal spectrum state data, classifies channel state categories, counts category frequencies and transition relationships, calculates channel state transition probabilities, and generates a dynamic transition probability matrix. The channel optimization and allocation module selects the target channel with the lowest interference probability and the highest signal strength based on the dynamic transition probability matrix, calculates the compatibility of the spectrum block and the load distribution, optimizes the allocation path, divides the priority queues and associates the resource information, and generates a cross-layer priority matching result; The task access optimization module monitors the task access status, analyzes the signal strength and interference probability based on the cross-layer priority matching results, adjusts the tasks that exceed the range to the auxiliary channel, optimizes the distribution of tasks between the main control and auxiliary channels, and generates the hierarchical access results of the main control and auxiliary channels; The bandwidth dynamic allocation module classifies the priority segments according to the delay sensitivity and throughput requirements based on the hierarchical access results of the main control and auxiliary channels, dynamically adjusts the bandwidth allocation strategy, and optimizes the segment allocation parameters in combination with the real-time load to generate the bandwidth dynamic allocation optimization result.

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