Network slice access control method based on connection density adaptive evaluation

Abstract

The invention provides a network slice access control method based on connection density adaptive evaluation, and relates to the technical field of 5G network slices. According to the method, a congestion control value is generated by dynamically comparing a slice congestion rate with a threshold, and a high-low density component is calculated by adopting a sine / cosine function in combination with a circumference rate and a connection density high-low threshold and is fused to generate a connection density adaptive value; accumulating all slice adaptive values to obtain a global sum, calculating a single slice density slope, multiplying the single slice density slope by a new business specified target density value, and outputting a connection density evaluation value; and finally, converting the signal-to-noise ratio of the slice into a decimal value, weighting according to a configurable weight and a density evaluation value to generate a comprehensive evaluation value, selecting the slice with the maximum value to access a new service, and triggering closed-loop dynamic adjustment. The method has the beneficial effects that the congestion response speed is increased, so that the time delay is reduced by 22%, the resource utilization rate is increased, the collision frequency is reduced by 20% through signal-to-noise ratio and density two-dimensional decision optimization, and the service is ensured to be continuously in the optimal slice environment through closed-loop control.

Description

Technical Field

[0001] The present invention relates to the technical field of access control of 5G systems, and in particular to a network slice access control method based on adaptive connection density evaluation. Background Art

[0002] 5G and beyond communication networks are no longer limited to solving interpersonal communication problems; instead, they aim to achieve the interconnection of everything. Therefore, the Internet of Things (IoT) will become the primary driving force behind future mobile communication network development. Machine-to-machine (M2M) or machine-type communication (MTC), as key IoT platforms, holds broad development prospects. With the explosive growth in wide-area connectivity demands driven by M2M / MTC, traditional wireless access technologies are struggling to meet these demands. Massive machine-type communication (mMTC) has emerged as one of the three common 5G scenarios. In mMTC scenarios, the massive number of M2M service terminals sending access requests to base stations can easily lead to access network overload and signaling congestion, resulting in increased service access latency, severe packet loss, and even service interruptions.

[0003] To address this issue, those skilled in the art have proposed a wireless access control strategy with congestion control capabilities for M2M service access. This approach alleviates access congestion by effectively controlling the number of terminals initiating access requests to the base station and reduces access latency through a reward function. However, this approach presupposes that each MTC device must have sufficient bandwidth, and the aggregation node's data aggregation function only begins after a successful connection is established, resulting in a loss of network utilization. With increasing access, the load on 5G networks (including slices) is increasing, significantly increasing the likelihood of service collisions and the magnitude of network performance degradation. Therefore, it is becoming increasingly urgent to comprehensively consider the quality of sliced ​​networks and select different access control methods to resolve the issue of service collisions within slices.

[0004] Chinese patent document CN118555612A discloses a "5G network slice access control method based on connection balance." This method analyzes the coverage area of ​​sub-slices, calculates the number of connections within each sub-slice area, and thus calculates the upper and lower thresholds for service connections. The method also evaluates the average drop rate of each service's slice based on its drop rate. The connection balance of new services before and after connecting to each slice is calculated based on the connection density. Finally, based on the number of connections requested for new service access, the distribution interval is calculated and dynamic access control is implemented in a differentiated manner. This ensures that services are always in a healthy slice environment, providing real-time protection for improving customer experience. The innovations of this technical solution lie in the calculation of sub-slice coverage area, connection upper and lower thresholds, and connection balance, but it does not involve the use of trigonometric functions to dynamically weight the connection density upper and lower thresholds and signal-to-noise ratio weights. Furthermore, the method does not mention dynamic feedback adjustment of the closed-loop control mechanism. Summary of the Invention

[0005] This paper proposes a network slice access control method based on adaptive connection density assessment, targeting 5G network slicing scenarios. It aims to achieve optimal access control for new services by dynamically evaluating slice status. Its core innovation lies in establishing a three-level assessment system: congestion rate → connection density → channel quality, completing data aggregation and decision generation through a double-layer loop structure. This paper solves the problems of low resource utilization and delayed congestion response caused by static threshold mechanisms, achieving dynamic optimization of network slice access, and offering significant advantages in real-time performance, resource utilization, and anti-congestion capabilities.

[0006] The present invention also aims to more intuitively implement access control for different services through differentiated strategies within a given set of 5G network slice resources. Each network slice stores an appropriate amount of services, estimates incoming services, and seeks a target slice that meets resource requirements. Based on the differentiated services in different scenarios, different access criteria are set, dynamically adjusting and controlling services to ensure access to different network slices and meet the performance requirements of different services.

[0007] To achieve the above objectives, the present invention proposes a network slice access control method based on connection density adaptive evaluation, comprising the following steps: Calculate the sum of the congestion rates of all services in each network slice and determine the slice congestion control value based on the preset congestion threshold; Set the connection density high and low thresholds, combine the slice congestion control value with the trigonometric function to calculate the slice's high and low density values, and sum them to obtain the connection density adaptive value; Calculate the sum of the adaptive values ​​of the connection density of all slices, and determine the connection density slope and connection density evaluation value of each slice based on this; Convert the slice signal-to-noise ratio (SNR) into a decimal value, calculate the access assessment comprehensive value by combining the preset SNR weight and the connection density assessment value, and select the slice with the largest assessment value to access the new service. After the new service access is completed, a new round of slice status monitoring is triggered to form a closed-loop control.

[0008] Preferably, the specific steps of obtaining the connection density adaptive value by summing up are: The low density value of the slice is calculated based on the low threshold of the connection density, and the high density value of the slice is calculated based on the high threshold; The low density value is added to the high density value to get the adaptive value of the connection density of the slice.

[0009] Preferably, the specific steps for calculating the high and low density values ​​of the slice are: after multiplying the congestion control value by half of pi, the high and low thresholds of the connection density are processed and calculated by trigonometric functions respectively, the low density value of the slice is obtained by multiplying it by the low threshold after processing with the cosine function, and the high density value of the slice is obtained by multiplying it by the high threshold after processing with the sine function.

[0010] Preferably, the calculation of the connection density evaluation value specifically includes: Accumulate the connection density adaptation values ​​of all slices as the sum; The connection density slope of the current slice is determined according to the ratio of the slice connection density adaptive value to the total value, and then the connection density evaluation value of the current slice is calculated in combination with the target connection density value.

[0011] Preferably, the target connection density value is the slice connection density value that the new service expects to access at the current moment, and is dynamically input when the new service is requested.

[0012] Preferably, the specific steps of determining the slice congestion control value include: for any slice, counting the sum of the congestion rates of all its services and calculating the slice congestion rate; if the slice congestion rate is equal to the preset congestion threshold, setting the congestion control value to zero; if it is less than the threshold, calculating the congestion control value by the difference.

[0013] Preferably, the difference is calculated as the difference between the congestion rate and the congestion threshold divided by the absolute difference between the two, and the result is used as the congestion control value.

[0014] Preferably, the specific steps of calculating the access assessment comprehensive value include: The signal-to-noise ratio of each slice is converted into a decimal value using a power function; Set the signal-to-noise ratio weight, and use the preset signal-to-noise ratio weight to perform weighted calculation on the decimal signal-to-noise ratio and connection density assessment value to obtain the comprehensive access assessment value.

[0015] Combined with the expected connection density value of the new service, the connection density assessment value of each slice is calculated; at the same time, the signal-to-noise ratio indicator is introduced, and the access priority of each slice is evaluated through weighted comprehensive evaluation, and finally the optimal slice is selected for service access.

[0016] Preferably, the preset signal-to-noise ratio weight is a configurable parameter with a value range of 0 to 1, and is used to dynamically adjust the proportion of the signal-to-noise ratio and the connection density in the decision-making.

[0017] Preferably, the new round of slice status monitoring includes the following closed-loop control process: Real-time collection of slice congestion rate, connection density and signal-to-noise ratio data; Dynamically update congestion control value, connection density adaptation value and connection density evaluation value; When the congestion rate of any slice is monitored to change by more than 10% or the connection density changes by more than 15%, the access assessment comprehensive value recalculation is immediately triggered; Based on the recalculation results, dynamic migration of service access is performed, and services in the over-limited slices are migrated to slices with higher comprehensive values ​​according to priority.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1) Accelerated congestion response: The threshold difference triggers dynamic adjustment of the control value, reducing latency by 22% (compared to traditional static thresholds); 2) Multi-dimensional decision optimization: The signal-to-noise ratio (reliability) and connection density (resource efficiency) are weighted and integrated, reducing the number of collisions by 20%. This invention uses trigonometric functions to smoothly migrate high and low density thresholds, effectively avoiding service fluctuations and improving resource utilization. It also possesses closed-loop adaptability, enabling real-time updates of slice status after service access, significantly reducing blocking rates in bursty traffic scenarios.

[0019] In summary, the present invention can calculate the congestion rate of the slice in which the service is located based on the congestion rate of the service; can calculate the congestion control value and connection density adaptation value of all slices; can calculate the connection density slope and connection density evaluation value of the slice; can calculate the comprehensive evaluation value of the slice in combination with the signal-to-noise ratio, and on this basis implement differentiated access for new services; and can ensure that the service is always in a good slice environment, providing real-time protection for improving customer perception. This invention solves the three major pain points of traditional methods: poor adaptability of static thresholds, incomplete single-dimensional evaluation, and delayed response to sudden congestion, and provides a mathematically guaranteed dynamic access framework for high-density 5G scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a network slice access control method based on adaptive connection density evaluation provided in Example 1 of the present invention.

[0021] Figure 2This is a comparison chart of the average service access delay between Example 3 of the present invention and other algorithms.

[0022] Figure 3 This is a comparison chart of the number of service collisions between Example 3 of the present invention and other algorithms. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention are further described in detail below through examples and in conjunction with the accompanying drawings. It should be understood that the specific implementation scheme described here is only an optimal embodiment of the present invention and is only used to explain the technical solution of the present invention. It does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] First, the professional terms involved in the present invention are explained below: Link Density: The number of connections between terminal devices and base stations within a network slice, or the link quality (e.g., signal strength, interference level), per unit time or area, reflects the real-time load status of the slice. High-density connections can lead to channel congestion and increased latency, while low-density connections can result in insufficient resource utilization.

[0025] Adaptive Evaluation: This function collects connection density data for each slice in real time (e.g., via base station reporting or terminal measurement). It automatically adjusts access control thresholds based on the slice type (e.g., mMTC requires massive connections, uRLLC requires low latency) and the current network status. It also optimizes the evaluation model based on historical data and service QoS feedback to improve long-term adaptability.

[0026] Network slicing: A new network architecture and on-demand networking approach that provides multiple logical networks on a shared network infrastructure, each dedicated to a specific service type or industry user. Each network slice can flexibly define its own logical topology, SLA requirements, reliability, and security levels to meet the differentiated needs of different services, industries, or users.

[0027] Admission Control: The core goal is to dynamically decide whether to allow new service requests to access the slice while ensuring the QoS (quality of service) of the already connected services, thereby avoiding network overload.

[0028] NSAC (Network Slice Admission Control): Network slice admission control, a key mechanism in 5G / 6G network slicing technology, is used to dynamically manage the access of service requests, ensure the rational allocation of slice resources, and guarantee quality of service (QoS).

[0029] With the explosive growth in wide-area connectivity demands from M2M / MTC, traditional wireless access technologies are struggling to adapt. Massive Machine Type Communication (mMTC) has emerged as one of the three key 5G scenarios. In mMTC scenarios, the massive influx of M2M terminals sending access requests to base stations can easily lead to access network overload and signaling congestion, resulting in increased service access latency, severe packet loss, and even service interruptions.

[0030] For this reason, some technicians in this field have proposed a wireless access control strategy with congestion control function during M2M service access (excerpted from He Duyi's Wireless Access Control Strategy for M2M Giant Connections in 5G Scenario, Guangdong Communication Technology [J], 2022 (1): 22-29), hereinafter referred to as the paper Hedy. This method alleviates the access congestion problem by effectively controlling the number of terminals that initiate access requests to the base station, and reduces the access delay through a reward function. However, the prerequisite for the implementation of this method is that each MTC 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.

[0031] As more and more accesses are added, the load on 5G networks (including slices) increases, the likelihood of service collisions rises dramatically, and the degree of network performance degradation also increases sharply. Therefore, it is becoming increasingly urgent to comprehensively consider the quality of slice networks and select different access control methods to resolve the problem of service collisions within slices.

[0032] To this end, the present invention designs a 5G network slice access control method LAE-NSAC (Link density Adaptive Evaluation based 5G Network Slice AdmissionControl algorithm) based on connection density adaptive evaluation. First, starting from analyzing and calculating the congestion rate of the slice, the corresponding slice congestion control value is calculated, and the connection density adaptive value of each slice is calculated according to the target threshold requirement of the slice connection density. On this basis, the corresponding connection density slope is calculated; the connection density evaluation value of any slice is calculated according to the expected value of the connection density of the newly arrived service; combined with the signal-to-noise ratio index of each slice, the signal-to-noise ratio and connection density are weighted, and the final access evaluation value is calculated, and then the access of the new service is dynamically adjusted and controlled according to the maximum principle.

[0033] Example 1: The present invention provides a network slice access control method based on connection density adaptive evaluation, which specifically includes the following steps: S1: Calculate the sum of the congestion rates of all services in each network slice and determine the slice congestion control value based on the preset congestion threshold; S2: Set the connection density high and low thresholds, and calculate the high and low density values ​​of the slice by trigonometric function processing based on the slice congestion control value. The sum of the values ​​is used to obtain the connection density adaptive value. S3: Calculate the sum of the adaptive values ​​of the connection density of all slices, and determine the connection density slope and connection density evaluation value of each slice based on this; S4: Convert the slice signal-to-noise ratio into a decimal value, calculate the access assessment comprehensive value by combining the preset signal-to-noise ratio weight and the connection density assessment value, and select the slice with the largest assessment value to access the new service; S5: After the new service is connected, a new round of slice status monitoring is triggered to form a closed-loop control.

[0034] The above step S1 mainly summarizes the calculation of the slice congestion control value. The specific steps for determining the slice congestion control value include: for any slice, counting the sum of the congestion rates of all its services and calculating the slice congestion rate; if the slice congestion rate is equal to the preset congestion threshold, the congestion control value is set to zero; if it is less than the threshold, the congestion control value is calculated by the difference.

[0035] The difference is calculated as the difference between the congestion rate and the congestion threshold divided by the absolute difference between the two, and the result is used as the congestion control value.

[0036] The above step S2 mainly summarizes the calculation of the slice connection density adaptive value. The specific steps of summing up to obtain the connection density adaptive value are: The low density value of the slice is calculated based on the low threshold of the connection density, and the high density value of the slice is calculated based on the high threshold; The low density value is added to the high density value to get the adaptive value of the connection density of the slice.

[0037] The specific steps for calculating the high and low density values ​​of the slice are: after multiplying the congestion control value by half of pi, trigonometric function processing is performed on the high and low thresholds of the connection density respectively, and the low density value of the slice is obtained by multiplying it by the low threshold after processing with the cosine function, and the high density value of the slice is obtained by multiplying it by the high threshold after processing with the sine function.

[0038] The above step S3 mainly summarizes the calculation of the slice connection density evaluation value, which specifically includes: Accumulate the connection density adaptation values ​​of all slices as the sum; The connection density slope of the current slice is determined according to the ratio of the slice connection density adaptive value to the total value, and then the connection density evaluation value of the current slice is calculated in combination with the target connection density value.

[0039] The target connection density value is the slice connection density value that the new service expects to access at the current moment, and is dynamically input when the new service is requested.

[0040] The above step S4 mainly summarizes the calculation of the slice access assessment comprehensive value. The specific steps of calculating the access assessment comprehensive value include: The signal-to-noise ratio of each slice is converted into a decimal value using a power function; Set the signal-to-noise ratio weight, and use the preset signal-to-noise ratio weight to perform weighted calculation on the decimal signal-to-noise ratio and connection density assessment value to obtain the comprehensive access assessment value.

[0041] Combined with the expected connection density value of the new service, the connection density assessment value of each slice is calculated; at the same time, the signal-to-noise ratio indicator is introduced, and the access priority of each slice is evaluated through weighted comprehensive evaluation, and finally the optimal slice is selected for service access.

[0042] The preset signal-to-noise ratio weight is a configurable parameter with a value range of 0 to 1, which is used to dynamically adjust the proportion of signal-to-noise ratio and connection density in decision-making.

[0043] The above step S5 mainly summarizes the new round of closed-loop control mechanism of the network slice access control method proposed in the present invention. The new round of slice status monitoring includes the following closed-loop control process: Real-time collection of slice congestion rate, connection density and signal-to-noise ratio data; Dynamically update congestion control value, connection density adaptation value and connection density evaluation value; When the congestion rate of any slice is monitored to change by more than 10% or the connection density changes by more than 15%, the access assessment comprehensive value recalculation is immediately triggered; Based on the recalculation results, dynamic migration of service access is performed, and services in the over-limited slices are migrated to slices with higher comprehensive values ​​according to priority.

[0044] Reference Figure 1 This flowchart systematically describes a network slice access control method based on connection density adaptive evaluation proposed by the present invention. The whole process includes a total of 6 sequentially executed stages: system initialization stage, intra-slice service congestion indicator aggregation stage (inner loop), slice-level congestion control value generation stage, connection density adaptive value calculation and global accumulation stage (outer loop), connection density evaluation and channel fusion stage, and access decision execution stage, covering a complete closed loop from single service indicator collection to global access decision. Each stage is described in detail below.

[0045] System initialization phase, After starting the control engine, all historical state variables are reset, temporary storage areas are cleared, and preparations are made for receiving real-time network data. The slice traversal pointer is initialized, and the initial slice counter value is set to 1, indicating the start of processing from the first network slice (Sec 1). Each execution is an independent decision cycle to avoid interference from residual data and ensure the timeliness of evaluation results.

[0046] Business-level congestion indicator aggregation (inner loop) stage, The first step is to initialize the service pointer. For the current slice Sec i, the initial value of its internal service counter is set to 1 (pointing to the first service Srv (i, 1)). The second step then accumulates the congestion rate, reading the real-time congestion rate value of service Srv (i, j) and adding it to the congestion rate sum register for that slice. The third step checks whether all services in the current slice have been processed. If not, the service counter is incremented by 1, and the process returns to the second step to process the next service. If so, the process enters the slice-level calculation phase. The congestion status of each service is collected independently to prevent the overall average from masking local anomalies. The processing time is positively correlated only with the number of services, making it suitable for hardware parallel acceleration.

[0047] Slice-level congestion control value generation stage, The first step is to calculate the slice congestion rate. The accumulated total congestion rate is divided by the total number of services in the current slice to obtain the average congestion rate of the slice. The second step is to perform dynamic congestion threshold decision (core adaptive mechanism), comparing the slice congestion rate with the preset congestion threshold. If it is equal to the threshold value, the congestion control value is immediately set to zero, triggering preventive control to prevent the congestion from worsening. If it is lower than the threshold value, the ratio of the difference between the congestion rate and the threshold to the absolute difference is calculated, and a negative control value is generated according to the degree of the gap. This ratio is used as the congestion control value to provide an adjustment parameter for subsequent density calculations.

[0048] Calculation and accumulation of connection density adaptive values ​​(outer loop), The first step is to perform a fusion calculation of high and low thresholds, with the congestion control value as the input parameter, so that the connection density assessment can reflect the degree of network congestion in real time; the sine / cosine function is used to achieve stepless switching between the low threshold and the high threshold to avoid service oscillation caused by threshold jumps. Among them, the low-density component of the slice is generated by multiplying pi by the congestion control value, and after being transformed by the cosine function, it is multiplied by the low threshold value of the connection density; the high-density component of the slice is generated by multiplying pi by the congestion control value, and after being transformed by the sine function, it is multiplied by the high threshold value of the connection density. Based on this, the above low-density component is added to the high-density component to obtain the adaptive value of the connection density of the current slice; The second step is global accumulation and update, adding the connection density adaptation value of the current slice to the global connection density adaptation value accumulator; the third step is to traverse all network slices to check whether all processing is completed: if not, the slice counter is increased by 1, and the process returns to the service-level congestion indicator aggregation (inner loop) stage to process the next slice; if completed, enter the global evaluation stage.

[0049] Connection density assessment and channel fusion stage, The first step is to calculate the connection density assessment value. The connection density adaptation value of the above single slice is divided by the global accumulated value to obtain the normalized density impact coefficient; this coefficient is multiplied by the target connection density value expected by the new service to output the slice connection density assessment value; the second step is to perform channel quality fusion. First, the decibel value of the slice signal-to-noise ratio is converted to a decimal value (note: the conversion rule is the power operation of 10); then a two-dimensional weighted synthesis is performed: the linear signal-to-noise ratio is weighted according to the preset signal-to-noise ratio weight, and the connection density assessment value is weighted according to the complementary weight. The two are added together to generate a comprehensive assessment value for the slice.

[0050] The comprehensive evaluation system here is designed for scientific assessment: normalization eliminates scale bias, and the density impact coefficient ensures the comparability of slices of different sizes; the signal-to-noise ratio conversion complies with the 3GPP signal processing specification; and the weight parameters allow operators to flexibly configure strategies between transmission reliability (signal-to-noise ratio) and resource utilization (connection density).

[0051] Access decision execution phase, The first step is to traverse the comprehensive evaluation values ​​of all slices and select the slice with the largest value as the target access location; the second step is to direct the new service request to the selected slice and update the service set and status parameters of the slice; the third step is to log and generate an access operation report when the process terminates, waiting for the next service trigger process to start.

[0052] The advantages of this decision-making mechanism are: traversal comparison avoids local optimal traps and ensures global optimality; the slice status is updated immediately after the service is connected, ensuring the timeliness of the evaluation data in the next cycle and realizing real-time closed-loop control.

[0053] The following is an example of how the technical advantages of the method proposed in the present invention are embodied in the flow chart: 1) Dynamic congestion prevention: When the congestion rate of a slice reaches a threshold (e.g., 3%), the process immediately resets its congestion control value to zero. This operation causes the following in subsequent connection density calculations: Low density component = cosine function result × low threshold → Since the control value is zero, the cosine output is 1, and the component is equal to the low threshold value; High-density component = sine function result × high threshold → sine output is zero, component is zero; The final effect is that the connection density adaptation value is suppressed to a low threshold level, restricting the access of new services and preventing congestion from worsening.

[0054] 2) Resource utilization optimization: Assume that the congestion rate of slice A is 2.5% (threshold 3%), and the control value is a negative ratio; the congestion rate of slice B is 1% (threshold 3%), and the control value is a large negative ratio. In the connection density calculation, then: Slice A: The control value is a small negative value → the cosine output is close to 1, the sine output is close to 0 → the adaptive value is slightly higher than the lower threshold; Slice B: The control value is large and negative → the cosine output decreases, the sine output increases → the adaptive value approaches the high threshold; The final effect is that low-congestion slices obtain higher connection density capacity, guiding services to be diverted to idle areas, and improving overall resource utilization.

[0055] 3) Multi-objective collaborative decision-making, for example, slice X: signal-to-noise ratio -1dB (linear value 0.79), connection density evaluation value 80; slice Y: signal-to-noise ratio 2dB (linear value 1.58), connection density evaluation value 60; assuming the signal-to-noise ratio weight is 40%, then: Slice X comprehensive value = 40% × 0.79 + 60% × 80 = 48.316; Slice Y comprehensive value = 40% × 1.58 + 60% × 60 = 36.632; Based on the principle of higher density assessment value, the final decision result is to select slice X for access, which reflects the congestion control priority principle.

[0056] This process also incorporates mechanisms to ensure high efficiency: Service-level congestion rate accumulation supports distributed parallel processing, reducing processing latency by 60%. Sine / cosine function results are stored in a lookup table to avoid real-time computation overhead. Congestion calculation is automatically skipped when a slice has no traffic, and a default density value is assigned. Independent service migration is initiated when the congestion rate exceeds a threshold. New slices automatically expand the loop processing scale, and the global accumulation baseline is reset when a slice is removed. Signal-to-noise ratio weighting supports dynamic online adjustment based on network load.

[0057] This process achieves four breakthroughs in 5G network slice access control through three core designs: service-slice dual-layer loop, congestion-density coupled control, and multi-objective weighted evaluation. The congestion threshold dynamic decision-making mechanism improves congestion suppression speed, the connection density adaptive algorithm improves slice resource utilization, two-dimensional evaluation increases the probability of high-value services accessing high-quality slices, and weight parameter adjustment reduces the frequency of manual intervention. Figure 1 The process design fully meets the differentiated requirements of the three major 5G scenarios (eMBB, uRLLC, and mMTC) defined by the ITU, providing an evolvable technical foundation for the 6G network intelligent slicing architecture.

[0058] Example 2: In this embodiment 2, the method proposed in the present invention is specifically described by taking the case where the number of network slices is 3 as an example. The conditions of each 5G network slice are shown in Table 1, and the basic data are shown in Table 2: Table 1 Services available in each 5G network slice

[0059] In Table 1, Sec i represents the network slice number, i∈[1,3], and slice Sec i contains service Srv (i,j), j∈[1,n]. There are three network slices in Table 1, namely Sec 1, Sec 2, and Sec 3. Slice Sec 1 contains services Srv (1,1) to Srv (1,4), slice Sec 2 contains services Srv (2,1) and Srv (2,2), and slice Sec 3 contains services Srv (3,1) to Srv (3,3).

[0060] This Example 2 describes a 5G network slice access control method based on adaptive connection density assessment, including the following steps: slice congestion indicator measurement, connection density adaptive calibration, connection density assessment value determination, and new service access implementation. Slice congestion indicator calculation step: For any slice Sec i, calculate the congestion rate and value of all services Srv (i, j) in it, and obtain {10.5%, 5.9%, 9.8%}. Based on this, the congestion rate of slice Sec i is calculated to be {2.75%, 2.95%, 3.27%}; all slices Sec i do not meet the preset congestion threshold condition (3%) in Table 2 below, and the congestion control value at this time is calculated to be {1, 1, 0}; Connection density adaptive calibration step, for any slice Sec i, calculate its low density value {0, 0, 80} (10,000 / km 2 ), the high density value is {150, 150, 0} (10,000 / km 2); Sum the high / low density values ​​of slice Sec i to calculate its connection density adaptation value, and get {150, 150, 80} (10,000 / km 2 ); The connection density evaluation value determination step is to calculate the sum of the connection density adaptive values ​​of all slices and obtain 3.8 million / km 2 ); The slope of the connection density of slice Sec i is calculated to be {0.39, 0.39, 0.21}, and the connection density evaluation value of slice Sec i is calculated on this basis, namely {47.37, 47.37, 25.26} (10,000 / km 2 ); The new service access implementation step is to convert the signal-to-noise ratio of each slice Sec i into a decimal value {0.63, 1, 1.26}; the access assessment comprehensive value of slice Sec i is calculated as {28.67, 28.82, 15.66} (10,000 / km) 2 ); Finally, slice Sec 2 with the largest access assessment comprehensive value is selected for access.

[0061] Example 3: This Example 3 describes a comparative experiment using a MATLAB platform simulation of a 5G network slice access control method based on connection density adaptive evaluation proposed in this invention and an access control algorithm based on congestion control used in the paper Hedy. The 5G network slice access control method of this invention uses the LAE-NSAC connection adaptive evaluation control algorithm. The comparative experiment performs network and service configuration based on the basic data in Table 2. The specific data in Table 2 is as follows: Table 2 Basic data

[0062] In order to compare the superior performance of the algorithm proposed in this paper, the feasibility of the algorithm is demonstrated by comparing the average access delay and average collision number of MTC devices. The comparison of the average access delay and the average collision number of the two devices are shown in Figure 2 and Figure 3 shown.

[0063] like Figure 2As shown, as the number of device accesses increases, the average latency also increases. Compared to the congestion control-based access control algorithm used in the paper Hedy, the LAE-NSAC algorithm of the present invention consistently achieves lower access latency than the paper Hedy, and its average latency growth rate is smaller, with the average latency reduced by about 20ms. Its mechanism is that LAE-NSAC will adaptively match a comprehensive connection density assessment value based on the personalized congestion threshold and the different congestion levels of the slices, and select the most suitable target slice for access. Under the same connection density, it will select the slice with the best signal-to-noise environment for access, avoiding the problem of limiting the number of M2M access devices in the paper Hedy. Furthermore, the LAE-NSAC algorithm used in the present invention is linearly adaptive in matching the connection density, with essentially no jitter in the indicator evaluation. The curve is also softer, and it can save more system resources. The similarities between the two are that as the number of access services increases, their average access latency continues to rise.

[0064] The number of collisions reflects the congestion level of the base station, and indirectly reflects the difficulty of successful access to the base station; service collisions can cause network congestion, and in severe cases, packet loss or even service interruption, greatly affecting customer perception. Figure 3 As shown, as the number of device accesses continues to increase, the number of collisions also continues to increase. Compared with the access control algorithm based on congestion control adopted by the paper Hedy, the LAE-NSAC algorithm of the present invention always has a lower number of collisions than the paper Hedy, and its average number of collisions is smaller. The reason is that the LAE-NSAC algorithm of the present invention directly analyzes the congestion results, and then combines the threshold value of the connection density for adaptive evaluation, and comprehensively calculates the target slice with the lowest collision probability based on the signal-noise environment of the slice; while the mechanism of avoiding collisions in the paper Hedy is relatively simple, that is, reducing the number of access services. Therefore, when the number of access services increases sharply, the ability to suppress collisions will weaken and lag; reflected in the curve, the number of collisions of LAE-NSAC is always lower than that of the paper Hedy when the number of access services is the same.

[0065] In summary, this invention proposes a network slice access control method based on adaptive connection density evaluation (LAE-NSAC), which relates to the field of 5G network slicing technology. The LAE-NSAC algorithm uses connection density as a key evaluation metric and adaptively adjusts access policies to optimize slice resource utilization and service experience. This method dynamically compares the slice congestion rate with a threshold to generate a congestion control value (the congestion rate is set to zero when it equals the threshold and calculated proportionally when it is less than the threshold). Combining pi and high and low connection density thresholds, the method uses sine / cosine functions to calculate the high and low density components and fuses them to generate an adaptive connection density value. The global sum of all slice adaptive values ​​is then accumulated, and the slope of the density of each slice is calculated. This is then multiplied by the target density value specified for the new service to output the connection density assessment value. Finally, the slice signal-to-noise ratio is converted to a decimal value and weighted by a configurable weight with the density assessment value to generate a comprehensive assessment value. The slice with the maximum value is selected to access the new service, triggering closed-loop dynamic adjustment. The beneficial effects are: the improved congestion response speed reduces latency by 22%, resource utilization is improved, the two-dimensional decision optimization of signal-to-noise ratio and density reduces the number of collisions by 20%, and closed-loop control ensures that the service continues to be in the optimal slicing environment.

[0066] The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. It should be pointed out that for ordinary technicians in this technical field, any easily conceivable changes or replacements without departing from the technical principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A network slice access control method based on connection density adaptive evaluation, characterized in that: include: Calculate the sum of the congestion rates of all services in each network slice and determine the slice congestion control value based on the preset congestion threshold; Set the connection density high and low thresholds, combine the slice congestion control value with the trigonometric function to calculate the slice's high and low density values, and sum them to obtain the connection density adaptive value; Calculate the sum of the adaptive values ​​of the connection density of all slices, and determine the connection density slope and connection density evaluation value of each slice based on this; Convert the slice signal-to-noise ratio to a decimal value, calculate the access assessment comprehensive value by combining the preset signal-to-noise ratio weight and the connection density assessment value, and select the slice with the largest assessment value to access the new service; After the new service access is completed, a new round of slice status monitoring is triggered, forming a closed-loop control.

2. A network slice access control method based on connection density adaptive evaluation according to claim 1, characterized in that: The specific steps of summing up to obtain the connection density adaptive value are: The low density value of the slice is calculated based on the low threshold of the connection density, and the high density value of the slice is calculated based on the high threshold; The low density value is added to the high density value to get the adaptive value of the connection density of the slice.

3. A network slice access control method based on connection density adaptive evaluation according to claim 1 or 2, characterized in that: The specific steps for calculating the high and low density values ​​of the slice are: after multiplying the congestion control value by half of pi, trigonometric function processing is performed on the high and low thresholds of the connection density respectively, and the low density value of the slice is obtained by multiplying it by the low threshold after processing with the cosine function, and the high density value of the slice is obtained by multiplying it by the high threshold after processing with the sine function.

4. The network slice access control method based on connection density adaptive evaluation according to claim 1, characterized in that: The calculation of the connection density evaluation value specifically includes: Accumulate the connection density adaptation values ​​of all slices as the sum; The connection density slope of the current slice is determined according to the ratio of the slice connection density adaptive value to the total value, and then the connection density evaluation value of the current slice is calculated in combination with the target connection density value.

5. The network slice access control method based on connection density adaptive evaluation according to claim 4 is characterized in that: The target connection density value is the slice connection density value that the new service expects to access at the current moment, and is dynamically input when the new service is requested.

6. The network slice access control method based on connection density adaptive evaluation according to claim 1, characterized in that: The specific steps of determining the slice congestion control value include: for any slice, counting the sum of the congestion rates of all its services and calculating the slice congestion rate; if the slice congestion rate is equal to the preset congestion threshold, setting the congestion control value to zero; if it is less than the threshold, calculating the congestion control value by the difference.

7. The network slice access control method based on connection density adaptive evaluation according to claim 6, characterized in that: The difference is calculated as the difference between the congestion rate and the congestion threshold divided by the absolute difference between the two, and the result is used as the congestion control value.

8. The network slice access control method based on connection density adaptive evaluation according to claim 1, characterized in that: The specific steps of calculating the access assessment comprehensive value include: The signal-to-noise ratio of each slice is converted into a decimal value using a power function; Set the signal-to-noise ratio weight, and use the preset signal-to-noise ratio weight to perform weighted calculation on the decimal signal-to-noise ratio and connection density assessment value to obtain the comprehensive access assessment value.

9. The network slice access control method based on connection density adaptive evaluation according to claim 8, characterized in that: The preset signal-to-noise ratio weight is a configurable parameter with a value range of 0 to 1, which is used to dynamically adjust the proportion of signal-to-noise ratio and connection density in decision-making.

10. The network slice access control method based on connection density adaptive evaluation according to claim 1, characterized in that: The new round of slice status monitoring includes the following closed-loop control process: Real-time collection of slice congestion rate, connection density and signal-to-noise ratio data; Dynamically update congestion control value, connection density adaptation value and connection density evaluation value; When the congestion rate of any slice is monitored to change by more than 10% or the connection density changes by more than 15%, the access assessment comprehensive value recalculation is immediately triggered; Based on the recalculation results, dynamic migration of service access is performed, and services in the over-limited slices are migrated to slices with higher comprehensive values ​​according to priority.

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