Network slice access control method, system and equipment based on regional connection density
By using a network slicing access control method based on regional connection density, the optimal slice is dynamically selected for new service access, which solves the problems of access network overload and signaling congestion in 5G networks and achieves low latency and high-efficiency transmission.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUAXIN CONSULTATING CO LTD
- Filing Date
- 2026-01-26
- Publication Date
- 2026-04-14
AI Technical Summary
In 5G networks, the access network overload and signaling congestion caused by the massive number of M2M service terminals accessing the network cannot be effectively solved by limiting the number of access terminals in existing technologies. This leads to increased service access latency, packet loss, and service interruption.
The network slicing access control method based on regional connection density calculates the connection density, congestion rate, and signal-to-noise ratio of network slices, and uses a composite evaluation weighted value to select the optimal slice for new service access, avoiding invalid access attempts and dynamically adjusting the access strategy.
It achieves low average access latency under high load conditions, reduces the risk of service collisions, improves transmission efficiency and service capacity, adapts to the QoS requirements of different services, and adapts to the connection density and congestion control of different application scenarios.
Smart Images
Figure CN121865374A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of 5G network technology, specifically to a network slicing access control method, system, and device based on regional connection density. Background Technology
[0002] Unified Access Control (UAC) technology alleviates the severe access conflict problem caused by a large number of terminals competing for network access in 5G systems. However, there is no unified standard in the industry regarding the specific algorithm implementation. In actual network environments, the number of terminals accessing the network changes in real time. Generally speaking, this large number of machine-to-machine (M2M) connections mainly come from periodically triggered services between devices and systems, exhibiting strong historical data similarity, chaotic time series with sharp peaks and thick peaks, and fractal distribution characteristics. Therefore, for 5G communication scenarios with massive connections, how to adaptively adjust the number of terminals initiating competitive random access becomes paramount. After all, the massive number of M2M service terminals sending access requests to the base station can easily cause access network overload and signaling congestion, leading to increased service access latency, severe packet loss, and even service interruption.
[0003] A wireless access control strategy with congestion control function in the M2M service access process has been proposed in the prior art (Wireless Access Control Strategy for M2M Mega-Connections in 5G Scenarios, Guangdong Communication Technology [J], 2022(1):22-29, hereinafter referred to as: Traditional Algorithm). This method alleviates the access congestion problem by effectively controlling the number of terminals that initiate access requests to the base station and reduces access latency through the reward function. However, the prerequisite for the implementation of this method is that each MTC (Machine-Type Communication) device must have sufficient bandwidth, and the data aggregation function of the aggregation node is only implemented after the connection is successfully established, resulting in a loss of network utilization. With more and more access, the load of 5G network (including slicing) is getting bigger and bigger, the possibility of service collision is rising sharply, and the decline in network performance is also increasing dramatically. Therefore, this invention proposes a network slicing access control method, system and device based on regional connection density. Summary of the Invention
[0004] The purpose of this invention is to provide a network slice access control method, system, and device based on regional connection density to solve the problems mentioned in the background art.
[0005] According to a first aspect of the present invention, in order to achieve the above objective, the present invention provides the following technical solution: a network slicing access control method based on regional connectivity density, comprising the following steps: Get m network slices and their sub-slice sets, network slices Current signal-to-noise ratio ,load and the congestion rate of existing services within each network slice. Among them, network slices are defined. The connection density of each sub-slice is the region connection density, and the region connection density is... ; Based on region connectivity density Calculate the average connection density of a network slice The maximum region connection density, the minimum region connection density, and the jitter value of the region connection density are obtained, and the mathematical expectation value of the region connection density jitter value is obtained. Based on the congestion rate of existing services within each network slice Calculate the average congestion rate for each slice and obtain the expected value of the congestion rate for each slice. The signal-to-noise ratio of each slice Seci Convert to decimal value and calculate the load of each slice. The expected value of the mathematical expression; Normalize the expected values of regional connection density jitter, congestion rate, and load to obtain regional connection density jitter sub-indices, congestion sub-indices, and load sub-indices. Combine these with the preset regional connection density weights to calculate the composite evaluation weighted value for each slice. Based on the average connectivity density of each slice With preset connection density threshold Based on the relationship, a set of valid slices is selected. In the set of valid slices, a comprehensive evaluation index for new service access control is calculated based on the composite evaluation weighted value and the decimal value of the signal-to-noise ratio of each slice. The slice with the smallest index value is selected as the target slice for accessing new services. If the set of valid slices is empty, new services are rejected.
[0006] Furthermore, based on the region connection density SLKDi, the average connection density LKDi, maximum region connection density, minimum region connection density, and jitter value of the region connection density of the network slice are calculated, and the expected value of the region connection density jitter value is obtained, as follows: (2-1): For any network slice Calculate its connectivity density ; Calculate its maximum regional connectivity density Calculate its minimum region connectivity density Where max(∙) and min(∙) are the maximum and minimum value functions, respectively; (2-2): Calculate slices jitter value of region connectivity density And the mathematical expectation of the region connectivity density jitter value. .
[0007] Furthermore, based on the congestion rate of existing services within each network slice... Calculate the average congestion rate for each slice and obtain the expected value of the congestion rate for each slice, as follows: (3-1): For any slice Calculate all of its services Sum of congestion rates Based on this, the slice is calculated. congestion rate ; (3-2): Calculate the expected value of the congestion rate for all slices. .
[0008] Further, each slice signal-to-noise ratio Convert to decimal value and calculate the load of each slice. The expected value of the mathematical expression is as follows: (4-1): Cut each slice The signal-to-noise ratio is converted to a decimal value. ,in, Represents a power function; (4-2): Calculate the mathematical expectation of the load for all slices. .
[0009] Furthermore, the expected values of the regional connectivity density jitter, congestion rate, and load are normalized to obtain regional connectivity density jitter sub-indices, congestion sub-indices, and load sub-indices. Combined with preset regional connectivity density weights, the composite evaluation weighted value for each slice is calculated, as follows: (5-1): Calculate any slice Regional connection density jitter index Congestion sub-indicators Load sub-indicators ; (5-2): Set the region connectivity density weights ; Calculate any slice Composite evaluation weighted value .
[0010] Furthermore, based on the average connection density of each slice With preset connection density threshold Based on the relationship, a set of valid slices is selected. Within this set, a comprehensive evaluation index for new service access control is calculated using the composite evaluation weighted value and the decimal value of the signal-to-noise ratio for each slice, as detailed below: (6-1): Set the connection density threshold All slices Include in the effective set of slices ;like If empty, the new service will be rejected; if... If there is only one slice, the new service will be directly connected to this slice; (6-2): Calculate the effective set of slices arbitrary slice New Service Access Control Comprehensive Evaluation Indicators Select the slice with the minimum value. New services are implemented and integrated as target slices.
[0011] According to a second aspect of the present invention, the present invention provides a network slice access control system based on regional connectivity density, for implementing the network slice access control method based on regional connectivity density described in the first aspect, comprising: The data acquisition module is used to acquire m network slices and their sub-slice sets, and network slices. Current signal-to-noise ratio ,load and the congestion rate of existing services within each network slice. Among them, network slices are defined. The connection density of each sub-slice is the region connection density, and the region connection density is... ; The slice connectivity density calculation module is used to calculate connectivity density based on region. Calculate the average connection density of a network slice The maximum region connection density, the minimum region connection density, and the jitter value of the region connection density are obtained, and the mathematical expectation value of the region connection density jitter value is obtained. The service congestion rate assessment module is used to assess the congestion rate of existing services within each network slice. Calculate the average congestion rate for each slice and obtain the expected value of the congestion rate for each slice. The slice performance index conversion module is used to convert the performance index of each slice. signal-to-noise ratio Convert to decimal value and calculate the load of each slice. The expected value of the mathematical expression; The composite evaluation weighted value calculation module is used to normalize the expected values of regional connection density jitter, congestion rate, and load to obtain regional connection density jitter sub-indices, congestion sub-indices, and load sub-indices. Combined with the preset regional connection density weights, the composite evaluation weighted value of each slice is calculated. The new service access control module is used to control the connection density based on the average connection density of each slice. With preset connection density threshold Based on the relationship, a set of valid slices is selected. In the set of valid slices, a comprehensive evaluation index for new service access control is calculated based on the composite evaluation weighted value and the decimal value of the signal-to-noise ratio of each slice. The slice with the smallest index value is selected as the target slice for accessing new services. If the set of valid slices is empty, new services are rejected.
[0012] According to a third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor loads and executes the computer program, it employs the network slicing access control method based on regional connection density described in the first aspect.
[0013] According to a fourth aspect of the present invention, the present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the network slice access control method based on regional connectivity density as described in the first aspect.
[0014] According to a fifth aspect of the present invention, the present invention provides a computer program product comprising a computer program, which, when executed by a processor, is used to load and execute the network slice access control method based on regional connectivity density as described in the first aspect.
[0015] The present invention has at least the following beneficial effects: 1. This invention constructs a multi-dimensional evaluation system based on regional connection density jitter, service congestion rate, and slice load. This system can accurately identify the distribution status of network resources and dynamically select the slice with the best overall quality to access new services. Compared to the crude control strategy of simply limiting the number of access terminals in existing technologies, this invention makes access decisions based on the principle of minimizing the weighted value of the composite evaluation. This effectively avoids invalid access attempts under high load and high jitter environments, thereby maintaining a lower average access latency in scenarios where the number of services continues to grow.
[0016] 2. Before access control, this invention actively filters the effective slice set that meets the connection density threshold, and prioritizes the slice with the best channel signal-to-noise ratio and the lowest comprehensive evaluation index within the set, thereby reducing the risk of service collision. This mechanism can smoothly adapt to the dynamic changes in access scale and shows better collision suppression capability and foresight compared with the existing technology.
[0017] 3. This invention, through fine-grained sub-slice region connection density analysis, can identify and prioritize the use of slices with balanced connection density distribution and few resource fragments to carry new services, achieving refined matching of access resources. Simultaneously, the signal-to-noise ratio (SNR), as a key parameter in access decisions, encourages services to migrate to areas with superior channel conditions, improving transmission efficiency and service capacity per unit of resources.
[0018] 4. This invention uses regional connection density jitter as the core evaluation dimension, enabling it to keenly perceive dynamic fluctuations in network topology and ensure that services are accessed in a slice environment with high connection stability. Combined with real-time quantitative evaluation of slice load and congestion status, this method can adaptively differentiate the QoS requirements of different services, achieve differentiated access control, and provide a continuous high-quality network environment guarantee for latency-sensitive and reliability-sensitive services.
[0019] 5. By introducing configurable regional connection density weighting coefficients, this invention can flexibly adapt to the differentiated emphasis on connection density, congestion control, and load balancing requirements of various application scenarios such as smart cities, industrial IoT, and vehicle-to-everything (V2X). Furthermore, the normalization mechanism based on mathematical expectation ensures scale invariance of the evaluation metrics, enhancing the algorithm's generalization ability and robustness in heterogeneous network environments and avoiding the decision-making rigidity problem caused by a single threshold judgment.
[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description
[0021] Figure 1 This is a flowchart illustrating the method described in this invention; Figure 2 This is a comparison chart of the average service access latency between the method described in this invention and traditional algorithms; Figure 3 This is a comparison chart of the number of business collisions between the method described in this invention and traditional algorithms. Detailed Implementation
[0022] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0023] The purpose of this invention is to achieve more intuitive access control for different services through differentiated strategies, given a limited amount of 5G network slice resources. Each network slice contains a suitable number of services, and the arrival of new services is estimated to identify target slices that meet resource requirements. Different access criteria are set according to the differentiated services in different scenarios, and dynamic adjustment control is implemented for services. This ensures the implementation of access across different network slices and meets the performance requirements of different services.
[0024] Example 1: like Figure 1 The illustrated embodiment is a 5G network slicing access control method based on regional connection density, including... Network slices Each includes Individual slices; network slices Current signal-to-noise ratio ,load The connectivity density of each sub-slice is defined as the region connectivity density, and the slice... The connection densities of the sub-slice regions within are respectively ;slice It already contains Business Corresponding to any business Its current congestion rate .
[0025] Please see Figures 1-3 This invention provides a technical solution: a network slice access control method based on regional connectivity density, comprising the following steps: Step 1: Based on region connectivity density Calculate the average connection density of a network slice The maximum region connection density, the minimum region connection density, and the jitter value of the region connection density are calculated, and the expected value of the region connection density jitter value is obtained, as follows: Step 1-1: For any slice Calculate its connection density ; Calculate its maximum regional connectivity density Calculate its minimum region connectivity density Where max(∙) and min(∙) are the maximum and minimum value functions, respectively; Step 1-2: Calculate slices jitter value of region connectivity density And the mathematical expectation of the region connectivity density jitter value. ; Step 2: Based on the congestion rate of existing services within each network slice Calculate the average congestion rate for each slice and obtain the expected value of the congestion rate for each slice, as follows: Step 2-1: For any slice Calculate all of its services Sum of congestion rates Based on this, calculate the slice. congestion rate ; Step 2-2: Calculate the expected value of the congestion rate for all slices. ; Step 3: Calculate the signal-to-noise ratio of each slice Seci Convert to decimal value and calculate the load of each slice. The expected value of the mathematical expression is as follows: Step 3-1: Slice each piece The signal-to-noise ratio is converted to a decimal value. ,in, Represents a power function; Step 3-2: Calculate the expected value of the load for all slices. ; Step 4: Normalize the expected values of the region connectivity density jitter, congestion rate, and load to obtain the region connectivity density jitter sub-indices, congestion sub-indices, and load sub-indices. Combined with the preset region connectivity density weights, calculate the composite evaluation weighted value for each slice, as follows: Step 4-1: Calculate any slice Regional connection density jitter index Congestion sub-indicators Load distribution index ; Step 4-2: Set the region connectivity density weights ; Calculate any slice Composite evaluation weighted value ; Step 5: Based on the average connectivity density of each slice With preset connection density threshold Based on the relationship between the two, a valid slice set is selected. Within this set, a comprehensive evaluation index for new service access control is calculated based on the composite evaluation weighted value and the decimal value of the signal-to-noise ratio for each slice. The slice with the lowest index value is selected as the target slice for new service access. If the valid slice set is empty, new services are rejected, as detailed below: Step 5-1: Set the connection density threshold ; will meet the conditions All slices Include in the effective set of slices ;like If empty, the new service will be rejected; if... If there is only one slice, the new service will be directly connected to that slice; Step 5-2: Calculate the effective set of slices arbitrary slice New Service Access Control Comprehensive Evaluation Indicators Select the slice with the minimum value. New services are integrated as target slices; The technical solution of the present invention will be further described below with reference to specific embodiments: Below, m This embodiment will be explained in detail using an example. The network slice configurations for each 5G network are shown in Table 1: Table 1. Services Existing in Each 5G Network Slice The basic data is shown in Table 2: Table 2 Basic Data This example describes a 5G network slice access control method based on regional connection density, including the following steps: slice connection density calculation, service congestion rate assessment, slice performance index conversion, composite assessment weighted value generation, and new service access control. Step 1: Calculate the slice connectivity density; Step 1-1: For any slice Calculate its connection density ; Calculate its maximum regional connectivity density Calculate its minimum region connectivity density ; Step 1-2: Calculate slices jitter value of region connectivity density And the mathematical expectation of the region connectivity density jitter value. ; Step 2: Business congestion rate assessment; Step 2-1: For any slice Calculate all of its services Sum of congestion rates Based on this, calculate the slice. congestion rate ; Step 2-2: Calculate the expected value of the congestion rate for all slices. ; Step 3: Conversion of slice performance indicators; Step 3-1: Slice each piece The signal-to-noise ratio is converted to a decimal value. ; Step 3-2: Calculate the expected value of the load for all slices. ; Step 4: Generate composite evaluation weighted values; Step 4-1: Calculate any slice Regional connection density jitter index Congestion sub-indicators Load distribution index ; Step 4-2: Calculate any slice Composite evaluation weighted value ; Step 5: New service access control; Step 5-1: [The following appears to be a separate, unrelated section:] ...and meet the conditions All slices Include in the effective set of slices Not satisfied The condition is that it is empty or has only one slice; Step 5-2: Calculate the effective set of slices arbitrary slice New Service Access Control Comprehensive Evaluation Indicators Select the slice with the minimum value. New services are integrated as target slices; Simulation experiment: The RLD-NSAC 5G network slicing access control method based on regional connection density in this embodiment was simulated on a MATLAB platform with the existing Hedy wireless access control method (traditional algorithm) with congestion control function. Network and service configurations were performed according to the above table. The resulting average access latency and number of service collisions are shown in the appendix. Figures 2 to 3 As shown.
[0026] like Figure 2 As shown, the RLD-NSAC algorithm of this invention consistently achieves lower access latency than traditional algorithms. Its mechanism lies in the fact that RLD-NSAC comprehensively calculates a composite evaluation weighted value based on the slice's connection density jitter, load, and congestion status, thereby selecting the most suitable target slice for access. Under the same connection density, it selects the slice with the optimal signal-to-noise environment for access, avoiding the problem of limiting the number of M2M access devices in traditional algorithms. Most importantly, RLD-NSAC's matching of connection density is smooth, selecting the slice with the smallest connection density jitter, which is reflected in a smoother curve and saves more system resources. Both algorithms share the characteristic that their average access latency continuously increases with the increase in the number of access services. like Figure 3 As shown, service collisions can lead to network congestion, and in severe cases, packet loss or even service interruption, greatly affecting customer experience. The RLD-NSAC of this invention directly analyzes the slice load and congestion status, and then combines the connection density threshold for comprehensive evaluation. Based on the signal-to-noise environment of the slice, it comprehensively calculates the target slice with the lowest collision probability. In contrast, the mechanism of traditional algorithms to avoid collisions is relatively simple, namely, reducing the number of access services. Therefore, when the number of access services increases sharply, the collision suppression capability will weaken and lag. As reflected in the curve, the number of collisions of RLD-NSAC is always lower than that of traditional algorithms when the number of access services is the same.
[0027] In summary, this invention designs a 5G network slicing access control method based on regional connection density. First, it starts by subdividing slices, analyzing the jitter status of the corresponding regional connection density and the average connection density of the slice, and calculating the regional connection density jitter sub-index for that slice. Next, it assesses service congestion within the slice and calculates the corresponding slice congestion sub-index. Then, it analyzes the slice load and calculates the corresponding slice load sub-index. Based on this, it generates a composite evaluation weighted value for the slice. Finally, it filters out all valid slices and calculates the comprehensive evaluation index for this type of slice, and then implements dynamic access adjustment control for new services based on the maximum value principle.
[0028] Example 2: This embodiment provides a network slice access control system based on regional connectivity density, which implements the network slice access control method based on regional connectivity density described in Embodiment 1, including: The data acquisition module is used to acquire m network slices and their sub-slice sets, the current signal-to-noise ratio SNRi, load LODi, and congestion rate Rcgij of existing services in each network slice for network slice SeSi,i∈[1,m]. Among them, the connection density of each sub-slice in network slice SeSi is defined as the regional connection density, and the regional connection density is SLKDi={Slkdi1,Slkdi2,⋯,SlkdiNmi}. The slice connection density calculation module is used to calculate the average connection density LKDi, maximum regional connection density, minimum regional connection density, and jitter value of regional connection density of network slices based on the regional connection density SLKDi, and to obtain the mathematical expectation value of the regional connection density jitter value. The service congestion rate assessment module is used to calculate the average congestion rate of each slice based on the congestion rate Rcgij of existing services in each network slice, and obtain the mathematical expectation value of the congestion rate of each slice. The slice performance index conversion module is used to convert the signal-to-noise ratio (SNRi) of each slice Seci into a decimal value and calculate the expected value of the load LODi of each slice. The composite evaluation weighted value calculation module is used to normalize the expected values of regional connection density jitter, congestion rate, and load to obtain regional connection density jitter sub-indices, congestion sub-indices, and load sub-indices. Combined with the preset regional connection density weights, the composite evaluation weighted value of each slice is calculated. The new service access control module is used to filter out the effective slice set based on the relationship between the average connection density LKDi of each slice and the preset connection density threshold LKDth. In the effective slice set, the comprehensive evaluation index for new service access control is calculated based on the composite evaluation weighted value and the decimal value of the signal-to-noise ratio of each slice. The slice with the smallest index value is selected as the target slice for accessing new services. If the effective slice set is empty, the new service is rejected.
[0029] Example 3: The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it adopts the network slicing access control method based on regional connection density described in Embodiment 1.
[0030] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.
[0031] Furthermore, the processor can be a central processing unit (CPU). Of course, depending on the actual use, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it in this regard.
[0032] Example 4: The present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the network slice access control method based on regional connection density described in Embodiment 1.
[0033] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.
[0034] Example 5: The present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to load and execute the network slice access control method based on regional connection density described in Embodiment 1.
[0035] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0036] For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on," "mounted on," "fixed to," or "set on" another element, it may be directly on the other element or there may be an intermediate element present. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible embodiments.
[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
[0038] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
Claims
1. A network slice access control method based on regional connectivity density, characterized in that, Includes the following steps: Get m network slices and their sub-slice sets, network slices Current signal-to-noise ratio ,load and the congestion rate of existing services within each network slice. Among them, network slices are defined. The connection density of each sub-slice is the region connection density, and the region connection density is... ; Based on region connectivity density Calculate the average connection density of a network slice The maximum region connection density, the minimum region connection density, and the jitter value of the region connection density are obtained, and the mathematical expectation value of the region connection density jitter value is obtained. Based on the congestion rate of existing services within each network slice Calculate the average congestion rate for each slice and obtain the expected value of the congestion rate for each slice. The signal-to-noise ratio of each slice Seci Convert to decimal value and calculate the load of each slice. The expected value of the mathematical expression; Normalize the expected values of regional connection density jitter, congestion rate, and load to obtain regional connection density jitter sub-indices, congestion sub-indices, and load sub-indices. Combine these with the preset regional connection density weights to calculate the composite evaluation weighted value for each slice. Based on the average connectivity density of each slice With preset connection density threshold Based on the relationship, a set of valid slices is selected. In the set of valid slices, the comprehensive evaluation index for new service access control is calculated based on the composite evaluation weighted value and the decimal value of the signal-to-noise ratio of each slice. The slice with the smallest index value is selected as the target slice for accessing new services. If the valid slice set is empty, new business will be rejected.
2. The network slice access control method based on regional connectivity density according to claim 1, characterized in that: Based on the region connection density SLKDi, the average connection density LKDi, maximum region connection density, minimum region connection density, and region connection density jitter of the network slice are calculated, and the expected value of the region connection density jitter is obtained, as follows: (2-1): For any network slice Calculate its connection density ; Calculate its maximum region connectivity density Calculate its minimum region connectivity density Where max(∙) and min(∙) are the maximum and minimum value functions, respectively; (2-2): Calculate slices jitter value of region connectivity density And the mathematical expectation of the region connectivity density jitter value. .
3. The network slice access control method based on regional connectivity density according to claim 2, characterized in that: Based on the congestion rate of existing services within each network slice Calculate the average congestion rate for each slice and obtain the expected value of the congestion rate for each slice, as follows: (3-1): For any slice Calculate all of its services Sum of congestion rates Based on this, calculate the slice. congestion rate ; (3-2): Calculate the expected value of the congestion rate for all slices. .
4. The network slice access control method based on regional connectivity density according to claim 3, characterized in that: slice signal-to-noise ratio Convert to decimal value and calculate the load of each slice. The expected value of the mathematical expression is as follows: (4-1): Cut each slice The signal-to-noise ratio is converted to a decimal value. ,in, Represents a power function; (4-2): Calculate the mathematical expectation of the load for all slices. .
5. The network slice access control method based on regional connectivity density according to claim 4, characterized in that: The expected values of regional connectivity density jitter, congestion rate, and load are normalized to obtain regional connectivity density jitter sub-indices, congestion sub-indices, and load sub-indices. Combined with preset regional connectivity density weights, the composite evaluation weighted value for each slice is calculated, as follows: (5-1): Calculate any slice Regional connection density jitter index Congestion sub-indicators Load sub-indicators ; (5-2): Set the region connectivity density weights ; Calculate any slice Composite evaluation weighted value .
6. The network slice access control method based on regional connectivity density according to claim 5, characterized in that: Based on the average connectivity density of each slice With preset connection density threshold Based on the relationship, a set of valid slices is selected. Within this set, a comprehensive evaluation index for new service access control is calculated using the composite evaluation weighted value and the decimal value of the signal-to-noise ratio for each slice, as detailed below: (6-1): Set the connection density threshold ; will meet the conditions All slices Include in the effective set of slices ;like If empty, the new service will be rejected; if... If there is only one slice, the new service will be directly connected to this slice; (6-2): Calculate the effective set of slices arbitrary slice New Service Access Control Comprehensive Evaluation Indicators Select the slice with the minimum value. New services are implemented and integrated as target slices.
7. A network slice access control system based on regional connectivity density, used to implement the network slice access control method based on regional connectivity density as described in any one of claims 1 to 6, characterized in that, include: The data acquisition module is used to acquire m network slices and their sub-slice sets, and network slices. Current signal-to-noise ratio ,load and the congestion rate of existing services within each network slice. Among them, network slices are defined. The connection density of each sub-slice is the region connection density, and the region connection density is... ; The slice connectivity density calculation module is used to calculate connectivity density based on region. Calculate the average connection density of a network slice The maximum region connection density, the minimum region connection density, and the jitter value of the region connection density are obtained, and the mathematical expectation value of the region connection density jitter value is obtained. The service congestion rate assessment module is used to assess the congestion rate of existing services within each network slice. Calculate the average congestion rate for each slice and obtain the expected value of the congestion rate for each slice. The slice performance index conversion module is used to convert the performance index of each slice. signal-to-noise ratio Convert to decimal value and calculate the load of each slice. The expected value of the mathematical expression; The composite evaluation weighted value calculation module is used to normalize the expected values of regional connection density jitter, congestion rate, and load to obtain regional connection density jitter sub-indices, congestion sub-indices, and load sub-indices. Combined with the preset regional connection density weights, the composite evaluation weighted value of each slice is calculated. The new service access control module is used to control the connection density based on the average connection density of each slice. With preset connection density threshold Based on the relationship, a set of valid slices is selected. In the set of valid slices, a comprehensive evaluation index for new service access control is calculated based on the composite evaluation weighted value and the decimal value of the signal-to-noise ratio of each slice. The slice with the smallest index value is selected as the target slice for accessing new services. If the set of valid slices is empty, new services are rejected.
8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on a processor. When the processor loads and executes the computer program, it employs the network slicing access control method based on regional connection density as described in any one of claims 1 to 6.
9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the network slice access control method based on regional connectivity density as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, is used to load and execute the network slice access control method based on regional connection density as described in any one of claims 1 to 6.