CN116506860A - Base station network slice management system based on dynamic resource allocation - Google Patents

Base station network slice management system based on dynamic resource allocation Download PDF

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Publication number
CN116506860A
CN116506860A CN202310485185.9A CN202310485185A CN116506860A CN 116506860 A CN116506860 A CN 116506860A CN 202310485185 A CN202310485185 A CN 202310485185A CN 116506860 A CN116506860 A CN 116506860A
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slice
network
network slice
base station
module
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王北戎
罗文茂
魏兴龙
魏亮
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Nanjing Vocational College Of Information Technology
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Nanjing Vocational College Of Information Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10—Dynamic resource partitioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a base station network slice management system based on dynamic resource allocation, which comprises a data processing module, a network slice management module, a performance monitoring module, an edge computing server and a data interface connected with a base station, wherein the edge computing server comprises a flow prediction module and a flow control module, the network slice management system establishes a network slice according to the requirement of a user, configures parameters and transmits the parameters to the data processing module positioned in a service layer through the data interface; the data processing module establishes network data links for slices of different services according to the slice parameter requirements and issues the network data links to the network slice management module located in the network function layer. The invention improves the use efficiency of the network slice, meets the requirements of high concurrency and real-time performance, and is convenient for unified management; the resource allocation accuracy is high, the slice allocation speed is high, and the response time of the dynamic allocation of the slice resources is reduced.

Description

Base station network slice management system based on dynamic resource allocation
Technical Field
The invention relates to the technical field of 5G wireless communication, in particular to a 5G base station network slice management system based on dynamic resource allocation.
Background
Three application scenes are defined by the 5G technology, namely, the mobile broadband, ultra-reliable low-delay communication and mass Internet of things communication are enhanced, and the application requirements of improving the user experience rate, lower delay and improving the connection number density are met. For different requirements and services under 5G network coverage, it is necessary to separate the same physical network infrastructure into multiple logically independent virtual networks using network slicing techniques. Because each network slice has independent speed, time delay, bandwidth characteristics and the like, unified allocation management from the access network to the core network end to end is needed for the slices, and physical channel resources are reasonably allocated so as to adapt to the use requirements of different services of different users.
In the prior art, the 5G network slice is mature in slice resource isolation and has reliable safety, part of technologies adopt a construction prediction model to provide a plurality of adjustment methods of the network resource slice, identify user use information at historical moments of the network slice, train the prediction model, thereby predicting user resource use behaviors at future moments, and perform resource allocation in advance to avoid data congestion. The method needs to process a large amount of data on the core network side, and transmits the data to each base station through the bearing network, and each base station responds to the processed data to reconfigure the end-to-end slice, so that the configuration speed is slower, and the system time delay is higher. In addition, because the prediction capability of the prediction algorithm is limited, when a certain slice burst flow exceeds the system prediction result, the system cannot respond to complete the resource reallocation in time, so that the method for reallocating the slice resources needs to be improved.
Disclosure of Invention
The invention aims to: the invention aims to provide a 5G base station network slice management system based on dynamic resource allocation.
The technical scheme is as follows: the system comprises a data processing module, a network slice management module, a performance monitoring module, an edge computing server and a data interface connected with a base station, wherein the edge computing server comprises a flow prediction module and a flow control module, the network slice management system establishes a network slice according to the requirement of a user, configures parameters and transmits the parameters to the data processing module positioned at a service layer through the data interface; the data processing module establishes network data links for slices of different services according to the slice parameter requirements and issues the network data links to the network slice management module positioned at the network function layer; the network slice management module receives slice network characteristics, maps and configures the slice network characteristics, and generates an end-to-end slice; the performance monitoring module collects the information of each base station and the performance parameters of the network slice and dynamically feeds back the information to the flow prediction module in the edge calculation server; the flow prediction module performs flow prediction training aiming at the acquired base station parameter dynamics and the historical data of the user access base station; the flow control module acquires a predefined flow control strategy in the system, analyzes the flow prediction condition and the current base station flow condition, and performs flow control processing on the network slice according to the flow control strategy.
Further, the data processing module establishes a network data link for the service instance according to the SLA protocol of the network service entity, and issues the network data link to the network slice management module of the network function layer.
Further, the network slice management module creates a network slice instance according to the resource and service configuration file.
Further, the network slice management module configures starting parameters of network elements for network slice examples, associates the IP of other network elements in the slice system, and generates data connection with the base station and the core network element.
Further, the performance monitoring module feeds back the collected dynamic parameters of the base station to an edge computing server located in the network function layer.
Further, the flow prediction module receives data of the performance monitoring module, analyzes and identifies current and historical flow information of a user in the network slice, trains a prediction model, and predicts future uplink and downlink resource demands of the network slice.
Further, the flow control module compares the current flow condition and the predicted flow condition of each slice with a predefined flow control strategy in the system, and performs dynamic flow control processing on the network slice according to the strategy to adjust slice parameters.
Further, the newly-built network slice configuration parameters of the network slice management system comprise a slice type, a maximum UE access number, a resource sharing level, a slice maximum uplink rate, a slice maximum downlink rate, a UE maximum uplink rate, a UE maximum downlink rate, a time delay, a UE moving speed, a slice provider, a slice name, a deployment plan, a deployment area and a reservation time.
Further, the flow control module performs general dynamic management when each slice is loaded normally.
Further, the flow control module performs burst dynamic management when flow congestion occurs.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages: according to the current service flow and the resource utilization condition of the flow prediction model at the future moment, slice resources are flexibly and dynamically allocated, the service efficiency of network slices is improved, the requirements of high concurrency and real-time are met, and unified management is facilitated; the edge computing technology is adopted to sink the data processing function of resource allocation to the base station in the target area, so that the resource allocation accuracy is high, the slice allocation speed is high, and the response time of the dynamic allocation of the slice resources is reduced.
Drawings
FIG. 1 is a schematic diagram of the structure of the present invention;
FIG. 2 is a schematic diagram of a network slice management module;
fig. 3 is a management control flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
The invention relates to a management system for end-to-end network slicing, which comprises a data processing module, a network slicing management module, a performance monitoring module, an edge computing server (comprising a flow prediction module and a flow control module), a data interface connected with a base station and the like.
The key management control flow of the slice mainly comprises:
the network slice management system builds a network slice according to the network demands of users, configures parameters and sends the parameters to the data processing module of the service layer through a network interface; the data processing module establishes network data links for slices of different services according to different slice parameter requirements and issues the network data links to the network slice management module positioned at the network function layer; the network slice management module receives slice network characteristics, maps and configures the slice network characteristics, and generates an end-to-end slice; the performance monitoring module collects the information of each base station and the performance parameters of the network slice, and dynamically feeds the parameters back to the flow prediction module in the edge calculation server; the flow prediction module performs flow prediction training aiming at the acquired base station parameter dynamics and the historical data of the user access base station, and predicts uplink and downlink channel resources required by the user access at the future moment; a flow control module in the edge calculation server acquires a predefined flow control strategy in the system, comprehensively analyzes the flow prediction condition and the current base station flow condition, and performs flow control processing on the network slice according to the flow control strategy so as to achieve the purpose of dynamically distributing the current bandwidth resources of the slice;
for a newly built network slice, an administrator fills out parameters required by a slice service in a slice configuration interface, including: the method comprises the steps of slicing type, maximum UE access number, resource sharing level, slicing maximum uplink rate (Mbps), slicing maximum downlink rate (Mbps), UE maximum uplink rate (Mbps), UE maximum downlink rate (Mbps), time delay (ms), UE moving speed, slicing provider, slicing name, deployment planning, deployment area and reservation time (day), and transmitting to a data processing module through a network management interface.
The data processing module identifies the information such as slice type, resource requirement, connection relation and the like in the parameters, and creates a service instance. The data processing module establishes a network data link for the service instance according to the SLA protocol of the network service entity and transmits the network data link to the network slice management module of the network function layer.
The network slice management module receives the network characteristics of the service instance, analyzes the slice type and the slice requirement, maps with a slice management interface of an operator, and generates a resource and service configuration file. And the network slice management module creates a network slice instance according to the resource and service configuration file. The network slice management module configures starting parameters of network element service for the network slice example, associates the IP of other network elements (AMF, CUCP, CUUP, DU, etc.) in the slice system, and generates data connection with the base station and the core network element. The network slice instance accesses base station equipment in the resource layer and connects with the physical network, thereby generating an end-to-end slice. Multiple network slice instances can be simultaneously accessed into the base station side network structure to form multiple end-to-end slices.
The performance monitoring module collects base station information and slice access conditions including real-time flow, access user number, time delay, uplink and downlink rates and the like through a data interface connected with the base station. And the performance monitoring module feeds back the acquired dynamic parameters of the base station to an edge computing server positioned at the network function layer.
The traffic prediction module of the edge computing server receives the data of the performance monitoring module, analyzes and identifies current and historical traffic information of users in the network slice, trains a prediction model, and predicts future uplink and downlink resource demands of the network slice. And the flow control module of the edge computing server performs dynamic flow control and recalculates the bandwidth resources. The flow control module receives data sent by the flow prediction module, and the data comprises the real-time uplink and downlink speed of the slice, the current access user number and the predicted future uplink and downlink flow requirement. The flow control module compares the current flow condition and the predicted flow condition of each slice with a predefined flow control strategy in the system, and performs dynamic flow control processing on the network slices according to the strategy, adjusts slice parameters, and achieves the purpose of dynamically distributing the existing bandwidth resources of the slices.
The dynamic flow control process flow is as follows:
the user predefines a flow control strategy on a configuration interface and generates strategy text;
the flow control module located at the edge computing server reads the strategy text and acquires the contents of real-time uplink and downlink flow of each slice of the current base station, the current access user number, predicted future uplink and downlink flow requirements, SLA protocol and the like from the flow prediction module:
(1) When the loads of all the slices are normal, the flow control module performs general dynamic management according to the flow control strategy and the prediction result of the flow prediction module:
(1.1) predicting by a flow prediction module, when the flow of the NS1 increases in the future, the flow control module rewrites the resource and service configuration file of the NS1 according to the requirement of the uplink and downlink flow in the future, increases the maximum uplink rate and the maximum downlink rate allocated to the NS1, and regenerates an end-to-end slice NS1x;
(1.2) predicting by the flow prediction module, when the flow of the NS1 is reduced in the future, the flow control module rewrites the resource and service configuration file of the NS1 according to the requirement of the uplink and downlink flow in the future, reduces the maximum uplink rate and the maximum downlink rate allocated to the NS1, and regenerates the end-to-end slice NS1x;
(1.3) other slicing processes are as above;
and (1.4) when the current load of each slice is normal, but the predicted flow rate at the future moment is increased, and the maximum resource upper limit of the base station bandwidth is reached, the maximum slice uplink rate and the maximum slice downlink rate are allocated to each slice, and the current limiting treatment is carried out.
(2) When the NS1 generates burst flow and flow congestion occurs, the flow control module checks the load conditions of other slices of the current base station according to the flow control strategy and data sent to the flow prediction module through the performance monitoring module, and performs burst dynamic management:
(2.1) when the base station has idle bandwidth resources which are not allocated to the slice, the flow control module rewrites the resources and service configuration files of the NS1 according to the residual bandwidth resources and the current demand flow of the NS1, increases the maximum uplink rate and the maximum downlink rate allocated to the NS1, and regenerates the end-to-end slice NS1x;
(2.2) when the base station bandwidth resources have been fully allocated to multiple network slices, checking other slice load data, if it is detected that there are currently idle bandwidth resources of NS2, then further checking future predicted upstream and downstream traffic requirements of NS 2:
A. if the traffic demand of the NS2 is reduced in the future, the resources and service configuration files of the NS1 and the NS2 are rewritten, the maximum uplink rate and the maximum downlink rate allocated to the NS1 are increased, the maximum uplink rate and the maximum downlink rate allocated to the NS2 are reduced, and the end-to-end slices NS1x and NS2x are regenerated;
B. if the traffic demand increases in the future time of the NS2, further checking the SLA index of the NS 2:
a. if the priority of the NS2 is smaller than that of the NS1, the resources and service configuration files of the NS1 and the NS2 are rewritten, the maximum uplink rate and the maximum downlink rate allocated to the NS1 are increased, the maximum uplink rate and the maximum downlink rate allocated to the NS2 are reduced, and the end-to-end slices NS1x and NS2x are regenerated;
b. if the priority of the NS2 is greater than the NS1, continuing to check the load data of other slices until detecting that a slice NS3 has lower load and idle bandwidth resources exist, further checking the future predicted uplink and downlink flow requirements of the NS2, and repeating the step A-B;
c. if no slice satisfies the lower load, or the flow demand in the future is reduced, or the flow demand in the future is increased and the priority is smaller than NS1, the NS1 is subjected to current limiting treatment, and other slices are not subjected to treatment.
(3) When the bandwidth resources of the base station are completely allocated to a plurality of network slices and the maximum upper limit of the bandwidth resources of the base station is reached, the current limiting processing is carried out on the NS1, and other slices are not processed.
(4) After the NS1x traffic congestion condition is recovered, the current allocation condition of each slice resource and service configuration is reserved, and a multiplexing user inputs initial parameters through a configuration interface of the management system to configure certain base station resources, so that three slices NS1, NS2 and NS3 are generated to perform general dynamic management, and burst dynamic management is performed until the situation that the total maximum downlink rate of the base station is dlRBmax and the total maximum uplink rate is ulRBmax occurs.
One embodiment of the invention is:
1. the user inputs initial parameters through a configuration interface of the management system, configures certain base station resources, and generates three slices NS1, NS2 and NS3, wherein:
NS1 bears high-definition Video service, the slice type is eMBB-Video, the maximum downlink rate of the allocated slice is dlRNSmax1, the maximum uplink rate of the slice is ulRNSmax1, and the priority is lowest according to SLA protocol;
NS2 bears intelligent Medical service, the slice type is uRLLC-Medical, the maximum downlink rate of the allocated slice is dlRNSmax2, the maximum uplink rate of the slice is ulRNSmax2, and the priority is highest according to SLA protocol;
NS3 bears smart Grid service, the slice type is mMTC-Grid, the maximum downlink rate of the allocated slice is dlRNSmax3, the maximum uplink rate of the slice is ulRNSmax3, and the priority is second according to SLA protocol.
2. The total maximum downlink rate of the base station is dlRBmax, and the total maximum uplink rate is ulRBmax.
3. After the end-to-end slice is established, generated and operated, the performance monitoring module collects base station data and slice access conditions, real-time uplink and downlink rates of all slices are fed back to the flow prediction module of the edge calculation server, the downlink rate of the current NS1 slice is dlRNS1, the uplink rate is ulRNS1, the downlink rate of the current NS2 slice is dlRNS2, the uplink rate is ulRNS2, the downlink rate of the current NS3 slice is dlRNS3, and the uplink rate is ulRNS3.
4. The flow prediction module analyzes historical information of the user using time and flow of the identified network slice, trains a prediction model, predicts the uplink and downlink rates of the slice at the future time, sets the downlink rate of the slice at the future time NS1 as dlRNSpre1, the uplink rate as ulRNSpre1, predicts the downlink rate of the slice at the future time NS2 as dlRNSpre2, the uplink rate as ulRNSpre2, predicts the downlink rate of the slice at the future time NS3 as dlRNSpre3 and the uplink rate as ulRNSpre3.
5. For a single slice, setting the uplink rate (ulRNnext) or the downlink rate (dlRNnext) of the single slice to be more than N times of the real-time uplink or downlink rate, and judging that burst traffic occurs when the single slice reaches more than M times of the original predicted uplink or downlink rate at the future time, and implementing burst dynamic management. The non-occurrence of burst traffic is generally dynamic management.
6. In general dynamic management, NS1 is taken as an example:
if the NS1 slice predicts that the downlink rate is dlRNSpre1> dlRNSmax1 or the uplink rate is ulRNSpre1> ulRNSmax1 at the future time, then the end-to-end slice NS1x is rewritten and generated, so that the maximum downlink rate dlRNSmax1x and the maximum uplink rate ulRNSmax1x of the new slice NS1x are improved;
if the NS1 slice predicts that the downlink rate dlRNSprel is less than 1/L of the maximum downlink rate of the slice or the uplink rate ulRNSpre1 is less than 1/L of the maximum uplink rate of the slice at future time, the end-to-end slice NS1x is rewritten and generated so that the maximum downlink rate dlRNSmax1x and the maximum uplink rate ulRNSmax1x of the new slice NS1x are reduced;
if the predicted traffic increases at future time of all slices including NS1 and reaches the maximum resource upper Limit of the base station bandwidth, that is, dlrnspre1+dlrnspre2+dlrnspre3> dlRBmax, ulRNSpre1+ulrnspre2+ulrnspre3> ulRBmax, the maximum slice uplink rate and downlink rate are allocated to each slice, and a current limiting (Limit) function is operated.
When burst dynamic management, taking NS3 as an example:
if the base station has idle bandwidth resources which are not allocated to the slice, the flow control module rewrites and generates an end-to-end slice NS3x according to the residual bandwidth resources and the current demand flow of NS3, so that the maximum downlink rate dlRNSmax3x and the maximum uplink rate ulRNSmax3x of the new slice NS3x are improved;
when the base station bandwidth resources are completely allocated to NS1, NS2, NS3, if it is detected that NS2 has idle resources, i.e. current uplink and downlink rates dlRNS2 < dlRNSmax2 and ulRNS2 < ulRNSmax2, then future predicted uplink and downlink rates dlRNSpre2 and ulRNSpre2 of NS2 are further checked:
A. if the traffic demand is reduced in the future of NS2, that is, dlRNSpre2 < dlRNS2 and uRNSpre2 < ulRNS2, the end-to-end slices NS3x and NS2x are rewritten to increase the maximum downlink rate dlRNSmax3x and the maximum uplink rate ulRNSmax3x of the new slice NS3x, and decrease the maximum downlink rate dlRNSmax2x and the maximum uplink rate ulRNSmax2x of the new slice NS2x;
B. if the traffic demand increases at the future time of NS2, i.e., dlRNSpre2> =dlrns2, or uRNSpre2> =ulrns2, then the SLA index priority of NS2 is further checked:
NS2 is a smart medical service slice, the priority is greater than that of an NS3 smart grid service slice, and therefore other slices NS1 are inspected;
b. detecting NS1 according to the above steps, if NS1 has idle resources (dlRNS 1 < dlRNSmax1 and ulRNS1 < ulRNSmax 1), and the traffic demand of NS1 is reduced (dlRNSpre 2 < dlRNS2 and ulrnspre2 < ulRNS 2) at the future time, then rewriting the resource and service configuration file of NS3 and NS1, increasing the maximum uplink rate and maximum downlink rate allocated to NS3, decreasing the maximum uplink rate and maximum downlink rate allocated to NS1, and regenerating end-to-end slices NS3x and NS1x;
c. if the traffic demand increases in the future of NS1, that is, dlRNSpre1> =dlrns1, or uRNSpre1> =ulrns1, further checking the SLA index priority of NS1, where NS1 is a high-definition video service slice, and the priority is smaller than NS3, rewriting the resources and service configuration files of NS3 and NS1, increasing the maximum uplink rate and the maximum downlink rate allocated to NS3, reducing the maximum uplink rate and the maximum downlink rate allocated to NS1, and regenerating end-to-end slices NS3x and NS1x;
when the base station bandwidth resources are fully allocated, i.e., dlrnsmax1+dlrnsmax2+dlrnsmax3=dlrbmax, and ulrnsmax1+ulrnsmax2+ulrnsmax3=ulrbmax, and NS1, NS2 have no free resources, the current Limit (Limit) function is run on NS3. Other slices were not processed.

Claims (10)

1. A base station network slice management system based on dynamic resource allocation is characterized in that: the system comprises a data processing module, a network slice management module, a performance monitoring module, an edge computing server and a data interface connected with a base station, wherein the edge computing server comprises a flow prediction module and a flow control module, the network slice management system establishes a network slice according to the requirement of a user, configures parameters and transmits the parameters to the data processing module positioned in a service layer through the data interface; the data processing module establishes network data links for slices of different services according to the slice parameter requirements and issues the network data links to the network slice management module positioned at the network function layer; the network slice management module receives slice network characteristics, maps and configures the slice network characteristics, and generates an end-to-end slice; the performance monitoring module collects the information of each base station and the performance parameters of the network slice and dynamically feeds back the information to the flow prediction module in the edge calculation server; the flow prediction module performs flow prediction training aiming at the acquired base station parameter dynamics and the historical data of the user access base station; the flow control module acquires a predefined flow control strategy in the system, analyzes the flow prediction condition and the current base station flow condition, and performs flow control processing on the network slice according to the flow control strategy.
2. The dynamic resource allocation based base station network slice management system of claim 1, wherein: and the data processing module establishes a network data link for the service instance according to the SLA protocol of the network service entity and transmits the network data link to the network slice management module of the network function layer.
3. The dynamic resource allocation based base station network slice management system of claim 1, wherein: and the network slice management module creates a network slice instance according to the resource and service configuration file.
4. The dynamic resource allocation based base station network slice management system of claim 1, wherein: the network slice management module configures starting parameters of network elements for network slice examples, associates the IP of other network elements in the slice system, and generates data connection with the base station and the core network element.
5. The dynamic resource allocation based base station network slice management system header of claim 1, wherein: and the performance monitoring module feeds back the acquired dynamic parameters of the base station to an edge computing server positioned at the network function layer.
6. The dynamic resource allocation based base station network slice management system of claim 1, wherein: the flow prediction module receives the data of the performance monitoring module, analyzes and identifies current and historical flow information of a user in the network slice, trains a prediction model and predicts future uplink and downlink resource demands of the network slice.
7. The dynamic resource allocation based base station network slice management system of claim 1, wherein: the flow control module compares the current flow condition and the predicted flow condition of each slice with a predefined flow control strategy in the system, and performs dynamic flow control processing on the network slice according to the strategy to adjust slice parameters.
8. The dynamic resource allocation based base station network slice management system of claim 1, wherein: the newly-built network slice configuration parameters of the network slice management system comprise a slice type, a maximum UE access number, a resource sharing level, a slice maximum uplink rate, a slice maximum downlink rate, a UE maximum uplink rate, a UE maximum downlink rate, time delay, a UE moving speed, a slice provider, a slice name, a deployment planning, a deployment area and a reservation time.
9. The dynamic resource allocation based base station network slice management system of claim 7 wherein: the flow control module performs general dynamic management when the slice loads are normal.
10. The dynamic resource allocation based base station network slice management system of claim 7 wherein: and the flow control module performs burst dynamic management when flow congestion occurs.
CN202310485185.9A 2023-04-28 2023-04-28 Base station network slice management system based on dynamic resource allocation Withdrawn CN116506860A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117715088A (en) * 2024-02-05 2024-03-15 苏州元脑智能科技有限公司 Network slice management method, device, equipment and medium based on edge calculation

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117715088A (en) * 2024-02-05 2024-03-15 苏州元脑智能科技有限公司 Network slice management method, device, equipment and medium based on edge calculation
CN117715088B (en) * 2024-02-05 2024-04-26 苏州元脑智能科技有限公司 Network slice management method, device, equipment and medium based on edge calculation

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