A 5g network slice dynamic access control method

Abstract

The application provides a 5G network slice dynamic access control method, which measures the connection density of each slice from the analysis of the number of connections in the sub-slice; calculates the drop rate and access delay of each slice according to the drop rate and access delay of each service in each sub-slice; normalizes the key service indicators such as the connection density, drop rate and access delay of each slice, and calculates the target function of all slices; according to the target consistency principle, the target to-be-accessed sub-slice of each slice is screened out and is included in the qualified set; according to whether the qualified set is empty, the target slice of the new service access is differentiated to realize the dynamic access adjustment control of the new service.

Description

Technical Field

[0001] This invention relates to the field of 5G communication technology, and more specifically to a dynamic access control method for 5G network slicing. Background Technology

[0002] With the massive access of machine-type communication (MTC) devices in 5G networks, the surge in the number of M2M service terminals has led to increasingly prominent problems such as access network overload and signaling congestion, specifically manifested as increased service access latency, higher data packet loss rates, and even service interruptions. In existing technologies, He Duyi proposed a wireless access strategy based on terminal quantity control (Wireless Access Control Strategy for M2M Mega-Connections in 5G Scenarios, Guangdong Communication Technology [J], 2022(1):22-29), which alleviates congestion by limiting the number of access requests. However, this method relies on sufficient bandwidth and only performs data aggregation after the connection is established, resulting in reduced network utilization. In addition, with the increase in 5G network slicing load, the risk of service collisions has increased significantly, and traditional methods are difficult to effectively balance resource utilization and collision suppression.

[0003] Therefore, there is an urgent need for a control method that can dynamically evaluate network slice quality and intelligently select access targets to solve the performance degradation problem caused by static policies in existing technologies. Summary of the Invention

[0004] To address the network congestion caused by massive M2M device access, this invention proposes a dynamic access control method for 5G network slicing. By dynamically selecting the optimal sub-slice in real time the moment a new service arrives, the method significantly reduces the probability of service collisions while ensuring resource utilization.

[0005] A further objective of this invention is to achieve a balance between load balancing and business stability quickly through secondary screening.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a 5G network slicing dynamic access control method, comprising the following steps:

[0007] S1, obtain the connection density of each sub-slice in each network slice, and filter the sub-slices with high load balancing based on the connection density;

[0008] S2, obtain the average disconnection rate and average access latency of each sub-slice service, and filter the sub-slices with high service stability;

[0009] S3, include the sub-slices in each network slice that simultaneously satisfy high load balancing and high service stability into the qualified set;

[0010] S4. Based on the status of the qualified set, differentiate the target slice for access control of new services. If the qualified set is not empty, select the preferred slice from the qualified set based on load balancing and service stability; otherwise, select the globally preferred slice.

[0011] In this technical solution, a comprehensive weighted evaluation system is constructed by dynamically evaluating multi-dimensional indicators such as connection density, service drop rate, and access latency of sub-slices. A two-level screening mechanism of qualified sets is adopted to realize intelligent access control of network slices, which significantly reduces service access latency and collision frequency, while improving resource utilization. This effectively solves the technical challenge of balancing resource allocation and service quality in high-density 5G scenarios.

[0012] Preferably, in step S4, selecting the globally preferred slice includes: comparing the service stability of all sub-slices of all network slices, selecting the sub-slice with high service stability as the target access sub-slice for the new service, and the network slice where the sub-slice is located as the access control target slice for the new service.

[0013] Preferably, the selection of the preferred slice from the qualified set based on load balancing and service stability includes: if there is only one sub-slice in the qualified set, then the slice containing that sub-slice is selected as the target slice for access control of the new service; otherwise, sub-slices with low connection density are selected first, and if the service stability of the sub-slice is poor, its selection priority is reduced to obtain the preferred slice from the qualified set.

[0014] Preferably, the selection of the preferred slice from the qualified set includes: using the connection density of the sub-slice with high load balancing as the benchmark for the comprehensive score, using the service stability index as the correction item for the comprehensive score, introducing a loss factor as the coefficient of the service stability index to reduce it, and selecting the slice with the smallest comprehensive score from the qualified set as the target slice for access control of the new service.

[0015] Preferably, the method for selecting sub-slices with high load balancing includes: selecting sub-slices with low connection density as sub-slices with high load balancing.

[0016] Preferably, the method for selecting sub-slices with high service stability includes: setting a first weight as the weight coefficient of the average call drop rate, linearly weighting the average call drop rate and the average access latency, and obtaining sub-slices with smaller weight values ​​as sub-slices with high service stability.

[0017] Preferably, the first weight is dynamically adjusted according to the network congestion level, and the first weight is negatively correlated with the network congestion level.

[0018] Preferably, the loss factor ranges from 0 to 0.5 and is adjusted according to the network congestion level and service type priority.

[0019] Preferably, the connection density of the sub-slice is obtained by the ratio of the area of ​​the sub-slice to the number of connections.

[0020] Preferably, the comprehensive score of a network slice is obtained by subtracting the product of the service stability index and the loss factor of the sub-slice with high service stability in the network slice from the normalized value of the connection density of the sub-slice with high load balancing in the network slice.

[0021] The beneficial effects of this invention are:

[0022] 1) By verifying both low connection density and low weighting, the selected sub-slices are ensured to have both low load and high stability;

[0023] 2) The weight balance between connection density and stability is dynamically adjusted through the loss factor, with stability taking priority during burst traffic.

[0024] 3) The screening results are optimized through a two-level screening mechanism of qualified sets, and parameters are adjusted through real-time network status feedback to enhance robustness;

[0025] 4) It can implement differentiated access control for new services to ensure that the services are always in a good slice environment, providing real-time assurance for improving customer experience. Attached Figure Description

[0026] Figure 1 This is a flowchart of a 5G network slicing dynamic access control method according to the present invention.

[0027] Figure 2 This is the algorithm flowchart of Embodiment 2 of the present invention.

[0028] Figure 3 This is a comparison chart of average service access latency in Embodiment 2 of the present invention.

[0029] Figure 4 This is a comparison chart of the number of service collisions in Embodiment 2 of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely preferred embodiments of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0031] Example 1

[0032] This embodiment provides a dynamic access control method for 5G network slicing, such as... Figure 1 As shown, it includes the following steps.

[0033] Step S1: Filter sub-slices with high load balancing.

[0034] Specifically, in this embodiment, there are m network slices, denoted by the i-th slice, where the value of i ranges from 1 to m. That is, the m network slices are the 1st slice, the 2nd slice, ..., the m-th slice.

[0035] Each network slice is divided into several sub-slices. The number of sub-slices in each network slice can be different, that is, the i-th slice is divided into N sub-slices. i The k-th sub-slice of the i-th slice is represented by the k-th sub-slice of the i-th slice, where k ranges from 1 to N. i Each network slice has a certain area and number of connections.

[0036] Step S1 also includes the following sub-steps.

[0037] Step S11: For any sub-slice, calculate its connection density by dividing its number of connections by its area.

[0038] By connecting density quantization of the load level of sub-slices, basic data support is provided for subsequent screening.

[0039] Step S12: Normalize the connection density data to obtain the normalized connection density value of any sub-slice.

[0040] Eliminate dimensional differences between different slices to achieve comparability of load levels across multiple slices.

[0041] Step S13: For any slice, search for the sub-slice with the minimum connection density normalization value to obtain the sub-slice with high load balancing.

[0042] Prioritize selecting the lightest sub-slice to reduce congestion risk from the source.

[0043] In step S1, a benchmark evaluation system for network load is established by quantifying the connection density of sub-slices. Compared with the method of only counting the number of connections, the introduction of area parameters can eliminate the evaluation bias caused by the difference in coverage and achieve comparability of load levels across slices.

[0044] Step S2: Select sub-slices with high business stability.

[0045] Each slice already contains a certain number of services, and the i-th slice already contains n services. i There are 1 business segment, each represented by the j-th business segment of the i-th slice, where j ranges from 1 to n. i The disconnection rate and access latency of each service in the sub-slice are statistically significant.

[0046] Step S2 also includes the following sub-steps.

[0047] Step S21: For any service existing in any sub-slice, calculate the expected value of the drop rate of all services in the sub-slice to obtain the average drop rate, and calculate the expected value of the access latency of all services in the sub-slice to obtain the average access latency.

[0048] By eliminating single-service fluctuation interference through probability analysis, the stability of sub-slices can be accurately reflected.

[0049] Step S22: Normalize the average call drop rate and average access latency to obtain normalized values ​​for the call drop rate and access latency.

[0050] The two heterogeneous metrics, the percentage of disconnection rate and the millisecond latency, are unified into dimensionless values ​​for easier comprehensive evaluation.

[0051] Step S23: Obtain the service stability index based on the normalized value of the disconnection rate and the normalized value of the access latency.

[0052] The first weight is set as the weight of the drop rate indicator, and its value ranges from 0 to 1. The linear weighted value of the drop rate normalization value and the access latency normalization value of any sub-slice is calculated. Specifically, the first weight and the complement of the first weight are used as the weight coefficients of the drop rate normalization value and the access latency normalization value, respectively, and the two are weighted and summed to obtain the service stability indicator.

[0053] The first weight is negatively correlated with the network congestion level and can be dynamically adjusted according to the network congestion level, highlighting the priority of service interruption risk through weight allocation.

[0054] Step S24: For any slice, find the sub-slice with the smallest weighted value to obtain the sub-slice with high business stability.

[0055] Select sub-slices with better stability to avoid high collision risks.

[0056] In step S2, the expected value is used to calculate the call drop rate and access latency, which can effectively smooth out sudden fluctuations in single services, such as instantaneous traffic surges, and reduce the interference of occasional abnormal data on the evaluation. In the weighted value calculation, the sensitivity of service interruption and latency indicators is balanced by the first weight, and differentiating and adapting to different service requirements.

[0057] Step S3: Generate a qualified set.

[0058] Step S3 also includes the following sub-steps.

[0059] Step S31: For any network slice, if the selected sub-slice with high load balancing and the sub-slice with high service stability are the same sub-slice, then lock the sub-slice as the target sub-slice to be accessed for the network slice; otherwise, set the target sub-slice to be accessed for the network slice to be empty.

[0060] Dual verification ensures that the selected sub-slices meet both low load and high stability requirements.

[0061] Step S32: Include all target sub-slices to be accessed in the qualified set.

[0062] A dynamic admission mechanism is constructed to include target sub-slices to be accessed into the qualified set, allowing only high-quality sub-slices to enter the candidate set.

[0063] In step S3, the dual verification mechanism requires sub-slices to simultaneously meet the criteria of "light load" and "good stability," which is more stringent than single-dimensional screening. The emptying mechanism avoids the forced allocation of inferior resources.

[0064] Step S4: Differentiatedly select the access control target slice for new services.

[0065] Step S4 also includes the following sub-steps.

[0066] Step S41: If the number of sub-slices in the qualified set meets the condition of 1, then the slice containing the sub-slice is taken as the access control target slice for the new service.

[0067] When there is only one sub-slice in the qualified set, the decision-making process is simplified and the unique optimal target is quickly identified.

[0068] Step S42: If the qualified set is not empty and the number of sub-slices in the qualified set is not 1, set a loss factor. The value range of the loss factor is 0 to 0.5. Use connection density as the benchmark item for comprehensive scoring, use the service stability index as the correction item for comprehensive scoring, use the loss factor as the coefficient of the service stability index to reduce it, calculate the comprehensive score of any slice, and select the slice with the smallest comprehensive score as the target slice for access control of the new service.

[0069] Specifically, the overall score of any network slice is equal to the normalized connection density value of the sub-slice with high load balancing in the network slice minus the product of the service stability index (i.e., the linear weighted value of the normalized sub-slice disconnection rate and the normalized access latency value) and the loss factor of the sub-slice with high service stability in the network slice.

[0070] The loss factor is adjusted based on network congestion level and service type priority.

[0071] Step S43: If the number of sub-slices in the qualified set is 0, then the globally preferred slice is selected, that is, the service stability of all sub-slices of all network slices is compared, and the sub-slice with high service stability is selected as the target access sub-slice for the new service. The network slice where the sub-slice is located is used as the access control target slice for the new service.

[0072] It dynamically adapts to changes in network conditions and prioritizes stability during sudden traffic surges.

[0073] In step S4, a dynamic adjustment window is constructed using the loss factor to automatically increase the stability weight when network congestion occurs. Global optimization is triggered when the qualified set is empty to ensure basic performance under extreme scenarios.

[0074] Example 2

[0075] This embodiment uses the number of network slices m as an example to specifically illustrate the 5G network slice dynamic access control method provided in Embodiment 1. Each 5G network slice is divided into 3 sub-slices, and the corresponding indicators and service conditions are shown in Tables 1-3:

[0076] Table 1. Services stored in the first subslice of each 5G network

[0077]

[0078] Table 2. Services stored in the second subslice of each 5G network

[0079]

[0080] Table 3. Services Existing in the 3rd Subslice of Each 5G Network

[0081]

[0082] The basic data is shown in Table 4:

[0083] Table 4 Basic Data

[0084] project data Operating frequency (GHz) 2.6 Operating bandwidth (MHz) 100 <![CDATA[Business disconnection rate index weight Det drp ∈[0,1]]]> 0.6 <![CDATA[Loss factor Ft los ∈[0, 0.5]]]> 0.3

[0085] It should be noted that the embodiments and specific data described in this specification are only for illustrative purposes to illustrate the technical solutions of the present invention and to help understand the implementation process of the method. In practical applications, the method can be automatically executed by a computer program or dedicated hardware, without relying on the manual calculation steps shown in the embodiments. Those skilled in the art can adjust the parameter configuration and implementation, and the parameter configuration and implementation method, according to the actual network environment and business needs, and all such adjustments should be considered to fall within the protection scope of the present invention.

[0086] This example provides a dynamic access control method for 5G network slicing, which includes the following steps, and the algorithm flowchart is shown below. Figure 2 As shown.

[0087] Step S1: Filter sub-slices with high load balancing.

[0088] Step S1 also includes the following sub-steps.

[0089] Step S11: For any sub-slice, calculate its connection density by dividing its number of connections by its area.

[0090] The connection density of each sub-slice is as follows: The connection density of the first sub-slice of the first slice is 2 million / km. 2 The connection density of the second sub-slice of the first slice is 500,000 / km. 2 The connection density of the third sub-slice of the first slice is 1.6 million / km. 2 The connection density of the first sub-slice of the second slice is 650,000 / km. 2 The connection density of the second sub-slice of the second slice is 600,000 / km. 2 The connection density of the third sub-slice of the second slice is 700,000 / km. 2 The connection density of the first sub-slice of the third slice is 1.2 million / km. 2 The connection density of the second sub-slice of the third slice is 1 million / km. 2 The connection density of the third sub-slice of the third slice is 1.1 million / km. 2 .

[0091] Step S12: Normalize the connection density data to obtain the normalized connection density value of any sub-slice.

[0092] The normalized connection density values ​​for each sub-slice are as follows: The normalized connection density value for the first sub-slice of slice 1 is 0.49, the normalized connection density value for the second sub-slice of slice 1 is 0.12, and the normalized connection density value for the third sub-slice of slice 1 is 0.39; The normalized connection density value for the first sub-slice of slice 2 is 0.33, the normalized connection density value for the second sub-slice of slice 2 is 0.31, and the normalized connection density value for the third sub-slice of slice 2 is 0.36; The normalized connection density value for the first sub-slice of slice 3 is 0.36, the normalized connection density value for the second sub-slice of slice 3 is 0.3, and the normalized connection density value for the third sub-slice of slice 3 is 0.33.

[0093] Step S13: For any slice, search for the sub-slice with the minimum connection density normalization value to obtain the sub-slice with high load balancing.

[0094] In this embodiment, the sub-slice of the first slice with the minimum connection density normalization value is the second sub-slice of the first slice, the sub-slice of the second slice with the minimum connection density normalization value is the second sub-slice of the second slice, and the sub-slice of the third slice with the minimum connection density normalization value is the second sub-slice of the third slice.

[0095] Therefore, the sub-slices with high load balancing are the second sub-slice of the first slice, the second sub-slice of the second slice, and the second sub-slice of the third slice.

[0096] Step S2: Select sub-slices with high business stability.

[0097] Each slice already contains a certain number of services, and the i-th slice already contains n services. i There are 1 business segment, each represented by the j-th business segment of the i-th slice, where j ranges from 1 to n. i The disconnection rate and access latency of each service in the sub-slice are statistically significant.

[0098] Step S2 also includes the following sub-steps.

[0099] Step S21: For any service existing in any sub-slice, calculate the expected value of the drop rate of all services in the sub-slice to obtain the average drop rate, and calculate the expected value of the access latency of all services in the sub-slice to obtain the average access latency.

[0100] The average service downtime rates for each sub-slice are as follows: The average service downtime rate for the first sub-slice of slice 1 is 2.75%, the average service downtime rate for the second sub-slice of slice 1 is 1.88%, and the average service downtime rate for the third sub-slice of slice 1 is 2.63%; the average service downtime rate for the first sub-slice of slice 2 is 1.75%, the average service downtime rate for the second sub-slice of slice 2 is 2.98%, and the average service downtime rate for the third sub-slice of slice 2 is 1.48%; the average service downtime rate for the first sub-slice of slice 3 is 1.63%, the average service downtime rate for the second sub-slice of slice 3 is 2.3%, and the average service downtime rate for the third sub-slice of slice 3 is 2.45%.

[0101] The average access latency for each sub-slice is as follows: The average access latency for the first sub-slice of slice 1 is 50 ms, the average access latency for the second sub-slice of slice 1 is 16.25 ms, and the average access latency for the third sub-slice of slice 1 is 23.75 ms; The average access latency for the first sub-slice of slice 2 is 20 ms, the average access latency for the second sub-slice of slice 2 is 15 ms, and the average access latency for the third sub-slice of slice 2 is 12.5 ms; The average access latency for the first sub-slice of slice 3 is 30 ms, the average access latency for the second sub-slice of slice 3 is 30 ms, and the average access latency for the third sub-slice of slice 3 is 17.5 ms.

[0102] Step S22: Normalize the average call drop rate and average access latency to obtain normalized values ​​for the call drop rate and access latency.

[0103] The normalized disconnection rates for each sub-slice are as follows: The normalized disconnection rate for the first sub-slice of the first slice is 0.38, the normalized disconnection rate for the second sub-slice of the first slice is 0.26, and the normalized disconnection rate for the third sub-slice of the first slice is 0.36; the normalized disconnection rate for the first sub-slice of the second slice is 0.42, the normalized disconnection rate for the second sub-slice of the second slice is 0.23, and the normalized disconnection rate for the third sub-slice of the second slice is 0.35; the normalized disconnection rate for the first sub-slice of the third slice is 0.25, the normalized disconnection rate for the second sub-slice of the third slice is 0.36, and the normalized disconnection rate for the third sub-slice of the third slice is 0.38.

[0104] The normalized access latency values ​​for each sub-slice are as follows: The normalized access latency value for the first sub-slice of slice 1 is 0.56, the normalized access latency value for the second sub-slice of slice 1 is 0.18, and the normalized access latency value for the third sub-slice of slice 1 is 0.26; the normalized access latency value for the first sub-slice of slice 2 is 0.42, the normalized access latency value for the second sub-slice of slice 2 is 0.32, and the normalized access latency value for the third sub-slice of slice 2 is 0.26; the normalized access latency value for the first sub-slice of slice 3 is 0.39, the normalized access latency value for the second sub-slice of slice 3 is 0.39, and the normalized access latency value for the third sub-slice of slice 3 is 0.23.

[0105] Step S23: Obtain the service stability index based on the normalized value of the disconnection rate and the normalized value of the access latency.

[0106] The first weight is set as the weight of the drop rate indicator, and its value ranges from 0 to 1. The linear weighted value of the drop rate normalization value and the access latency normalization value of any sub-slice is calculated. Specifically, the first weight and the complement of the first weight are used as the weight coefficients of the drop rate normalization value and the access latency normalization value, respectively, and the two are weighted and summed to obtain the service stability indicator.

[0107] In this embodiment, the weighted values ​​of each sub-slice are as follows: the weighted value of the first sub-slice of the first slice is 0.45, the weighted value of the second sub-slice of the first slice is 0.23, and the weighted value of the third sub-slice of the first slice is 0.32; the weighted value of the first sub-slice of the second slice is 0.42, the weighted value of the second sub-slice of the second slice is 0.27, and the weighted value of the third sub-slice of the second slice is 0.32; the weighted value of the first sub-slice of the third slice is 0.31, the weighted value of the second sub-slice of the third slice is 0.37, and the weighted value of the third sub-slice of the third slice is 0.32.

[0108] Step S24: For any slice, find the sub-slice with the smallest weighted value to obtain the sub-slice with high business stability.

[0109] In this embodiment, the sub-slice with the smallest weighted value of the first slice is the second sub-slice of the first slice, the sub-slice with the smallest weighted value of the second slice is the second sub-slice of the second slice, and the sub-slice with the smallest weighted value of the third slice is the first sub-slice of the third slice.

[0110] Therefore, the sub-slices with high business stability are the second sub-slice of the first slice, the second sub-slice of the second slice, and the first sub-slice of the third slice.

[0111] Step S3: Generate a qualified set.

[0112] Step S3 also includes the following sub-steps.

[0113] Step S31: For any network slice, if the selected sub-slice with high load balancing and the sub-slice with high service stability are the same sub-slice, then lock the sub-slice as the target sub-slice to be accessed for the network slice; otherwise, set the target sub-slice to be accessed for the network slice to be empty.

[0114] Dual verification ensures that the selected sub-slices meet both low load and high stability requirements.

[0115] In this embodiment, for the first slice, both the sub-slice with high load balancing and the sub-slice with high service stability are the second sub-slices of the first slice. Therefore, the second sub-slice of the first slice is locked as the target sub-slice to be accessed in the first slice. For the second slice, both the sub-slice with high load balancing and the sub-slice with high service stability are the second sub-slices of the second slice. Therefore, the second sub-slice of the second slice is locked as the target sub-slice to be accessed in the second slice. For the third slice, the sub-slice with high load balancing is the second sub-slice of the third slice, while the sub-slice with high service stability is the first sub-slice of the third slice. The sub-slice with high load balancing and the sub-slice with high service stability are not the same sub-slice. Therefore, the target sub-slice to be accessed in the third slice is set to null.

[0116] Step S32: Include all target sub-slices to be accessed in the qualified set.

[0117] A dynamic admission mechanism is constructed to include target sub-slices to be accessed into the qualified set, allowing only high-quality sub-slices to enter the candidate set.

[0118] In this embodiment, the only two sub-slices included in the qualified set are the second sub-slice of the first slice and the second sub-slice of the second slice.

[0119] In step S3, the dual verification mechanism requires sub-slices to simultaneously meet the criteria of "light load" and "good stability," which is more stringent than single-dimensional screening. The emptying mechanism avoids the forced allocation of inferior resources.

[0120] Step S4: Differentiatedly select the access control target slice for new services.

[0121] Step S4 also includes the following sub-steps.

[0122] Step S41: If the number of sub-slices in the qualified set meets the condition of 1, then the slice containing the sub-slice is taken as the access control target slice for the new service.

[0123] When there is only one sub-slice in the qualified set, the decision-making process is simplified and the unique optimal target is quickly identified.

[0124] In this embodiment, the number of sub-slices in the qualified set is 2, which does not meet the condition, so this step is not executed.

[0125] Step S42: If the qualified set is not empty and the number of sub-slices in the qualified set is not 1, set a loss factor. The value range of the loss factor is 0 to 0.5. Use connection density as the benchmark item for comprehensive scoring, use the service stability index as the correction item for comprehensive scoring, use the loss factor as the coefficient of the service stability index to reduce it, calculate the comprehensive score of any slice, and select the slice with the smallest comprehensive score as the target slice for access control of the new service.

[0126] Specifically, the overall score of any network slice is equal to the normalized connection density value of the sub-slice with high load balancing in the network slice minus the product of the service stability index (i.e., the linear weighted value of the normalized sub-slice disconnection rate and the normalized access latency value) and the loss factor of the sub-slice with high service stability in the network slice.

[0127] In this embodiment, the loss factor is set to 0.3, resulting in a comprehensive score of 0.05 for the first slice and 0.23 for the second slice. Therefore, the second slice has the lowest comprehensive score, and is selected as the target slice for access control of the new service.

[0128] Step S43: If the number of sub-slices in the qualified set is 0, then the globally preferred slice is selected, that is, the service stability of all sub-slices of all network slices is compared, and the sub-slice with high service stability is selected as the target access sub-slice for the new service. The network slice where the sub-slice is located is used as the access control target slice for the new service.

[0129] It dynamically adapts to changes in network conditions and prioritizes stability during sudden traffic surges.

[0130] In this embodiment, the number of sub-slices in the qualified set is 2, which does not meet the condition, so this step is not executed.

[0131] The results of the technical effect verification of the present invention through simulation experiments are described in detail below.

[0132] The 5G network slicing dynamic access control method of this embodiment, together with the wireless access strategy based on terminal quantity control proposed by He Duyi in the background technology (wireless access control strategy for M2M giant connection in 5G scenario, Guangdong Communication Technology [J], 2022(1):22-29, hereinafter referred to as paper Hedy), and the network slicing access control method based on connection balance in the patent with publication number CN118555612A, are simulated on the MATLAB platform. The network and service configurations are performed according to the above table. The obtained average access latency and number of service collisions are shown in the appendix. Figures 3 to 4 As shown.

[0133] like Figure 3As shown, the OF-NSAC algorithm of this invention consistently achieves lower access latency than the Hedy and LBD-NSAC methods in the paper. The mechanism lies in the fact that OF-NSAC itself evaluates access based on access latency as the core indicator, weighting the drop rate and access latency to select the target slice with the lowest weighting value. When the access latency of the target slices is not significantly different, the sub-slice with the lowest connection density is selected for access based on the objective function, thereby further reducing access latency and avoiding the problem of limiting the number of M2M access devices in the Hedy paper. LBD-NSAC, on the other hand, selects based purely on the connection balance of each sub-slice. While balance and latency are related, they are not tightly coupled. A common feature of the three algorithms is that their average access latency continuously increases as the number of access services increases.

[0134] like Figure 4 As shown, service collisions can lead to network congestion, and in severe cases, packet loss or even service interruption, greatly impacting customer experience. The OF-NSAC algorithm in this invention directly assesses the service drop rate to measure the likelihood of service collisions and improves the sensitivity of network congestion control by dynamically adjusting the weight of the service drop rate indicator. In contrast, the collision avoidance mechanism of the paper Hedy is relatively simple, namely reducing the number of access services. Therefore, when the number of access services increases sharply, the collision suppression capability weakens and lags. LBD-NSAC weakens network congestion by making the collisions across the entire slice more even, but it lacks dynamic management capabilities. This is reflected in the curves: both the paper Hedy and LBD-NSAC algorithms have a higher number of collisions than the OF-NSAC algorithm in this invention when the number of access services is the same.

[0135] In this invention, the first step is to analyze the number of connections in each sub-slice and measure the connection density of each slice. Based on the drop rate and access latency of each service in each sub-slice, the drop rate and access latency of each slice are calculated. Key service indicators such as connection density, drop rate, and access latency of each slice are normalized, and the objective function for all slices is calculated. Based on the principle of objective consistency, target sub-slices to be accessed for each slice are selected and included in the qualified set. Depending on whether the qualified set is empty, the target slice for new service access is determined differently, thereby achieving dynamic access adjustment and control for new services.

Claims

1. A dynamic access control method for 5G network slicing, characterized in that, Includes the following steps: S1, obtain the connection density of each sub-slice in each network slice, and filter the sub-slices with high load balancing based on the connection density; S2, obtain the average disconnection rate and average access latency of each sub-slice service, and filter the sub-slices with high service stability; S3, include the sub-slices in each network slice that simultaneously satisfy high load balancing and high service stability into the qualified set; S4. Based on the status of the qualified set, differentiate the target slice for access control of new services. If the qualified set is not empty, select the preferred slice from the qualified set based on load balancing and service stability. Otherwise, select the globally preferred slice.

2. The 5G network slicing dynamic access control method according to claim 1, characterized in that, In step S4, selecting the globally preferred slice includes: comparing the service stability of all sub-slices of all network slices, selecting the sub-slice with high service stability as the target access sub-slice for the new service, and the network slice where the sub-slice is located as the access control target slice for the new service.

3. The 5G network slicing dynamic access control method according to claim 1, characterized in that, The selection of the preferred slice from the qualified set based on load balancing and service stability includes: if there is only one sub-slice in the qualified set, then the slice containing that sub-slice is selected as the target slice for access control of the new service; otherwise, sub-slices with low connection density are selected first, and if the service stability of the sub-slice is poor, its selection priority is reduced to obtain the preferred slice from the qualified set.

4. A 5G network slicing dynamic access control method according to claim 1 or 3, characterized in that, The selection of the preferred slice from the qualified set includes: using the connection density of the sub-slice with high load balancing as the benchmark for comprehensive scoring, using the service stability index as the correction item for comprehensive scoring, introducing a loss factor as a coefficient to reduce the service stability index, and selecting the slice with the lowest comprehensive score from the qualified set as the target slice for access control of the new service.

5. A 5G network slicing dynamic access control method according to claim 1, 2, or 3, characterized in that, The method for selecting sub-slices with high load balancing includes: selecting sub-slices with low connection density as sub-slices with high load balancing.

6. A 5G network slicing dynamic access control method according to claim 1, 2, or 3, characterized in that, The method for selecting sub-slices with high service stability includes: setting a first weight as the weight coefficient of the average call drop rate, linearly weighting the average call drop rate and the average access latency, and obtaining sub-slices with smaller weight values ​​as sub-slices with high service stability.

7. The 5G network slicing dynamic access control method according to claim 6, characterized in that, The first weight is dynamically adjusted according to the network congestion level, and the first weight is negatively correlated with the network congestion level.

8. The 5G network slicing dynamic access control method according to claim 4, characterized in that, The loss factor ranges from 0 to 0.5 and is adjusted according to the network congestion level and service type priority.

9. A 5G network slicing dynamic access control method according to claim 1 or 3, characterized in that, The connection density of the sub-slice is obtained by the ratio of the area of ​​the sub-slice to the number of connections.

10. A 5G network slicing dynamic access control method according to claim 4, characterized in that, The comprehensive score of a network slice is obtained by subtracting the product of the service stability index and the loss factor of the sub-slice with high service stability in the network slice from the normalized value of the connection density of the sub-slice with high load balancing in the network slice.

Images