CN107276785A - A kind of WLAN optimization method and device - Google Patents
A kind of WLAN optimization method and device Download PDFInfo
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- CN107276785A CN107276785A CN201710303778.3A CN201710303778A CN107276785A CN 107276785 A CN107276785 A CN 107276785A CN 201710303778 A CN201710303778 A CN 201710303778A CN 107276785 A CN107276785 A CN 107276785A
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
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Abstract
The invention discloses a kind of WLAN WLAN optimization methods, this method includes：Receive the data of wlan network equipment collection；Analysis model is set up by the intelligent algorithm of machine learning, the data analyzed using analysis model；Network Optimization Strategy is determined according to the result of the analysis and execution is issued.By the wireless environment data, the user experience data that receive the whole net that wlan network equipment is gathered, it is pooled to cloud platform and carries out analysis calculating, the technical advantage of machine learning is made full use of to analyze data, obtain whole net wireless environment and network operation trend, Consumer's Experience situation, it is determined that optimal Network Optimization Strategy, the feedback of network optimization effect can also be obtained by analysis model, with further reference to Consumer's Experience situation, Network Optimization Strategy adjustment is carried out, is realized using Consumer's Experience as the optimal network optimization.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for optimizing a Wireless Local Area Network (WLAN).
Background
In recent years, WLANs based on IEEE802.11 protocols have been widely used in network construction such as shopping malls, enterprises, hospitals, schools, and meeting places due to their outstanding advantages and mature technology.
In an enterprise-level WLAN network for large-scale applications, limited non-overlapping communication channels make network optimization problems such as network interference, load balancing, terminal access, etc. more complicated. Because the scale of the wireless Access Point (AP) equipment is huge, in the aspect of channel planning, a large amount of labor cost is consumed by only depending on manual planning, and the problem of overlong convergence time exists by utilizing automatic channel adjustment of the AC.
At present, WLAN network optimization needs to balance interference and coverage in the aspect of power planning, and because each AP has different regional physical environments, the AP cannot be completely copied; in the aspects of roaming and access, a large number of complex roaming related radio frequency parameters need to be controlled and adjusted; in the aspect of effect acceptance, a large amount of manpower is also needed for point location test acceptance. In the prior art, the problem of frequency spectrum interference of a wireless communication channel is mainly solved through automatic optimization management of radio frequency resources, a surrounding network is scanned through a wireless access point, radio frequency environment information is obtained and network interference evaluation is carried out, then network planning is carried out automatically, and network performance is improved. However, the channel automation adjustment needs to collect a large amount of environment information, distinguish interference information of the local network and the non-local network according to the collected information, and calculate the actual interference degree and the channel saturation degree of the WLAN, where if a centralized calculation mode of an Access Controller (AC) is adopted, storage and calculation bottlenecks of the controller will be caused; if the distributed calculation mode of the AP is adopted, the optimization convergence time is too long, and the error of the local optimal substitution of the whole network is optimal; and frequent dynamic adjustment may also cause the terminal device to frequently switch the access channel, which results in poor practical experience.
Disclosure of Invention
The embodiment of the invention provides a WLAN optimization method and device, which are used for solving the problems of poor WLAN network optimization effect and poor user actual experience in the prior art.
A wireless local area network, WLAN, optimization method, the method comprising:
receiving data collected by WLAN network equipment;
establishing an analysis model through an intelligent algorithm of machine learning, and analyzing the data by using the analysis model;
and determining a network optimization strategy according to the analysis result and issuing and executing the network optimization strategy.
Optionally, the receiving data collected by the WLAN network device includes:
and receiving network equipment running state data, wireless environment data and user experience data which are acquired by the WLAN network equipment, wherein the data are acquired by a wireless controller AC and a wireless access point AP and are sent by compression operation.
Optionally, the establishing an analysis model through the machine learning intelligent algorithm, and analyzing the data by using the analysis model includes:
establishing a wireless environment perception trend model and a user experience perception model through an intelligent algorithm of machine learning;
based on the data, the wireless environment perception trend model is used for analyzing and obtaining the whole network WLAN wireless environment coverage condition, the interference condition and the network running state trend, the wireless network state is restored, the user experience condition is analyzed and measured by the user experience perception model, and the wireless network state and the whole network user experience condition are visualized.
Optionally, the determining a network optimization policy and issuing and executing the network optimization policy include:
and diagnosing the wireless network state according to the wireless WLAN environment, the equipment running state and the user experience obtained by analysis, analyzing the existing wireless network problems, determining a network optimization strategy aiming at the wireless network problems, and issuing and executing the network optimization strategy, wherein the wireless network problems comprise one or more of interference, access and roaming.
Optionally, the method further includes:
after the network optimization strategy is issued and executed, wireless network state feedback is obtained through analysis of the analysis model, and the network optimization strategy is adjusted according to user experience conditions and wireless network state differences before and after the network optimization strategy is executed, so that closed-loop convergence is achieved.
A WLAN optimization apparatus, comprising: the device comprises a receiving unit, an analyzing unit and a strategy unit; wherein,
the receiving unit is used for receiving data collected by the WLAN network equipment;
the analysis unit is used for establishing an analysis model through an intelligent algorithm of machine learning and analyzing the data by using the analysis model;
and the strategy unit is used for determining a network optimization strategy according to the analysis result and issuing and executing the network optimization strategy.
Optionally, the receiving unit is specifically configured to receive network device running state data, wireless environment data, and user experience data acquired by the WLAN network device, where the data is acquired by the wireless controller AC and the wireless access point AP and sent by performing compression operation.
Optionally, the analysis unit is specifically configured to establish a wireless environment perception trend model and a user experience perception model through an intelligent algorithm of machine learning; based on the data, the wireless environment perception trend model is used for analyzing and obtaining the whole network WLAN wireless environment coverage condition, the interference condition and the network running state trend, the wireless network state is restored, the user experience condition is analyzed and measured by the user experience perception model, and the wireless network state and the whole network user experience condition are visualized.
Optionally, the policy unit is specifically configured to diagnose the wireless network state according to a wireless WLAN environment, an equipment operating state, and the user experience obtained through analysis, analyze an existing wireless network problem, determine a network optimization policy for the wireless network problem, and issue and execute the network optimization policy, where the wireless network problem includes one or more of interference, access, and roaming.
Optionally, the analysis unit is further configured to obtain a wireless network state feedback through analysis of the analysis model after the network optimization strategy is issued and executed, and adjust the network optimization strategy according to a user experience condition and a wireless network state difference before and after the network optimization strategy is executed, so as to implement closed-loop convergence.
The invention has the following beneficial effects:
according to the WLAN optimization method and device provided by the embodiment of the invention, the running state data, the wireless environment data and the user experience data of the whole network, which are collected by the WLAN network equipment, are collected to the cloud platform for analysis and calculation, the technical advantages of machine learning are fully utilized to analyze the data, the whole network wireless environment, the network running trend and the user experience condition are obtained, the optimal network optimization strategy is determined, the feedback of the network optimization effect can be obtained through an analysis model, the user experience condition is further referred, the network optimization strategy is adjusted, and the network optimization taking the user experience as the best is realized.
Drawings
FIG. 1 is a flow chart of a WALN optimization method in an embodiment of the invention;
fig. 2 is a schematic structural diagram of a WLAN optimization apparatus according to an embodiment of the present invention;
fig. 3 is a system architecture diagram of a WLAN optimization method according to an embodiment of the present invention.
Detailed Description
Aiming at the problems of poor WLAN network optimization effect and poor user actual experience in the prior art, the WLAN optimization method provided by the embodiment of the invention comprises the following steps of firstly, as shown in figure 1, executing the following steps:
step 101, receiving data collected by WLAN network equipment;
here, the received data is data acquisition control performed by the AC and the AP, compression operation is performed on the acquired data, and the https encryption technology is used for transmitting the data to the cloud data platform through a specific channel. The data mainly includes network device operating state data, wireless environment data, user experience data and the like, wherein the data specifically includes AP, AC device operating parameters, air interface environment information around each AP, access behavior parameters of a user terminal, WLAN WIFI performance parameters, user experience measurement parameters and the like.
102, establishing an analysis model through an intelligent algorithm of machine learning, and analyzing the data by using the analysis model;
specifically, a wireless environment perception trend model and a user experience perception model are established through an intelligent algorithm of machine learning; based on the data, the wireless environment perception trend model is used for analyzing and obtaining the whole network WLAN wireless environment coverage condition, the interference condition and the network running state trend, the wireless network state is restored, the user experience perception model is used for analyzing and measuring the user experience condition, the wireless network state and the whole network user experience condition are subjected to visual data model packaging for visual display, and the visual user experience can be used for problem identification, reason analysis, network optimization strategy selection and subsequent optimization effect inspection.
And 103, determining a network optimization strategy according to the analysis result and issuing and executing the network optimization strategy.
Specifically, the wireless network state is diagnosed according to the wireless WLAN environment, the equipment running state and the user experience condition obtained by analysis, the existing wireless network problems are analyzed according to the optimization principle that the user experience is optimal, and a network optimization strategy is determined and issued and executed aiming at the wireless network problems of interference, access, roaming and the like.
Here, a network optimization strategy based on an intelligent analysis algorithm, such as a network optimization solution for intelligent Radio Resource Management (RRM), intelligent access, intelligent roaming, etc., is provided for wireless network problems such as interference, access, roaming, etc. The network optimization policy execution specifically includes:
coverage assessment, conventional coverage assessment generally adopts two ways: thermography and artificial point location measurement. The thermal diagram simulates the signals, the real moving track of the network user cannot be predicted, and a large amount of time is consumed by adopting a manual single-point signal scanning mode. Therefore, the network deployment designed in the embodiment of the present invention is based on the basic idea where the wireless signal of the user needs to be covered, and performs coverage evaluation by combining the wireless indexes of the user terminal, so that the signal strength of the coverage area meets the requirements of the user, and the coverage condition of the most real network requirement is fed back by the coverage evaluation.
And (4) interference optimization, namely analyzing the neighbor relation between the APs based on the whole network data, and visualizing the interference. And the method comprehensively analyzes and automatically adjusts, and reduces the co-channel interference and adjacent channel interference between the local network APs and the non-local network wireless equipment.
In access roaming optimization, a terminal generally takes signal strength as a first element of access selection, but cannot achieve the best experience before optimization. Therefore, it is required to make the terminal achieve the best experience no matter in the access or roaming stage according to the terminal access condition and experience prediction after access.
And machine learning, namely continuously learning user experience, network characteristics and an optimization strategy by using a machine learning engine, so that the network has self-optimization capability and is always in an excellent user experience state.
Further, the method may further include: after the network optimization strategy is issued and executed, wireless network state feedback is obtained through analysis of the analysis model, adjustment of the network optimization strategy is carried out according to user experience conditions, and closed-loop convergence is achieved.
According to the WLAN optimization method provided by the invention, the running state data of the network equipment, the wireless environment data of the whole network and the user experience data collected by the WLAN network equipment are received and collected to the cloud platform for analysis and calculation, the technical advantages of machine learning are fully utilized to analyze the data, the whole network wireless environment, the network running trend and the user experience condition are obtained, the optimal network optimization strategy is determined, the feedback of the network optimization effect can be obtained through an analysis model, the user experience condition is further referred, the network optimization strategy is adjusted, and the network optimization taking the user experience as the best is realized.
Based on the same inventive concept, an embodiment of the present invention provides a WLAN optimization apparatus, which has a structure shown in fig. 2 and includes: a receiving unit 21, an analyzing unit 22, and a policy unit 23; wherein,
a receiving unit 21, configured to receive data collected by a WLAN network device; here, the received data is data acquisition control performed by the AC and the AP, compression operation is performed on the acquired data, and the https encryption technology is used for transmitting the data to the cloud data platform through a specific channel. The data mainly includes network device operating state data, wireless environment data, user experience data and the like, wherein the data specifically includes AP, AC device operating parameters, air interface environment information around each AP, access behavior parameters of a user terminal, WLAN WIFI performance parameters, user experience measurement parameters and the like.
An analysis unit 22, configured to establish an analysis model through a machine learning intelligent algorithm, and analyze the data using the analysis model; establishing a wireless environment perception trend model and a user experience perception model through an intelligent algorithm of machine learning; based on the data, the wireless environment perception trend model is used for analyzing and obtaining the whole network WLAN wireless environment coverage condition, the interference condition and the network running state trend, the wireless network state is restored, the user experience perception model is used for analyzing and measuring the user experience condition, the wireless network state and the whole network user experience condition are subjected to visual data model packaging for visual display, and the visual user experience can be used for problem identification, reason analysis, network optimization strategy selection and subsequent optimization effect inspection.
And the strategy unit 23 is used for determining a network optimization strategy according to the analysis result and issuing and executing the network optimization strategy. Specifically, the wireless network state is diagnosed according to the wireless WLAN environment, the equipment running state and the user experience condition obtained by analysis, the existing wireless network problems are analyzed according to the optimization principle that the user experience is optimal, and a network optimization strategy is determined and issued and executed aiming at the wireless network problems of interference, access, roaming and the like.
The receiving unit 21 is specifically configured to receive network device running state data, wireless environment data, and user experience data acquired by a WLAN network device, where the data is sent by performing data acquisition on an AC and an AP and performing compression desensitization operation.
The analysis unit 22 is specifically configured to establish a wireless environment perception trend model and a user experience perception model through an intelligent algorithm of machine learning; based on the data, the wireless environment perception trend model is used for analyzing and obtaining the whole network WLAN wireless environment coverage condition, the interference condition and the network running state trend, the wireless network state is restored, the user experience condition is analyzed and measured by the user experience perception model, and the wireless network state and all the user experience conditions of the whole network are visualized.
The policy unit 23 is specifically configured to diagnose the wireless network state according to the user experience obtained through analysis, the wireless WLAN environment, and the device operating state, analyze existing wireless network problems, determine a network optimization policy, and issue and execute the network optimization policy, where the wireless network problems include one or more of interference, access, and roaming.
Optionally, the analysis unit 22 is further configured to obtain a wireless network state feedback through analysis of the analysis model after the network optimization strategy is issued and executed, and adjust the network optimization strategy according to a user experience condition and a wireless network state difference before and after the network optimization strategy is executed, so as to implement closed-loop convergence.
It should be understood that the implementation principle and process of the WLAN optimization device provided in the embodiment of the present invention are similar to those of the embodiment shown in fig. 1, and are not described herein again.
The WLAN optimization device provided by the embodiment of the invention receives the wireless environment data and the user experience data of the whole network collected by the WLAN network equipment, collects the wireless environment data and the user experience data to the cloud platform for analysis and calculation, fully utilizes the technical advantages of machine learning to analyze the data, obtains the wireless environment, the network operation trend and the user experience condition of the whole network, determines the optimal network optimization strategy, can also obtain the feedback of the network optimization effect through an analysis model, further refers to the user experience condition, adjusts the network optimization strategy, and realizes the network optimization taking the user experience as the best.
An embodiment of the present invention further provides an architecture diagram applying the WLAN optimization method, as shown in fig. 3, the architecture is hierarchically divided into: the device comprises a presentation layer, a big data analysis layer and an equipment acquisition layer; wherein, big data analysis layer further divides into: the system comprises an access control layer, a data storage layer, a machine learning layer and an intelligent network optimization solution layer.
Specifically, the equipment acquisition layer is mainly controlled by AC and AP for data acquisition, utilizes https encryption technology to carry out authentication and data interaction with the big data analysis layer, and carries out compression and desensitization operations on the acquired data. The data collected by the layer mainly comprises: the method comprises the following steps of AP, AC equipment operation parameters, air interface environment information around each AP, access behavior parameters of a user terminal, WLAN WIFI performance parameters, user experience measurement parameters and the like.
The big data analysis layer mainly has the functions of storing, analyzing and learning the data periodically acquired and uploaded by the WLAN network equipment. The amount of service processing data of the layer is in direct proportion to the scale of WLAN network equipment and the scale of network users. The sub-layers are divided into the following sub-layers:
(1) and the access control layer realizes a data acquisition interface and access control with the WLAN network equipment. Through https encryption protocol and data compression mode, identity authentication information is interacted with the WLAN network equipment, wireless network environment data and user experience data and the like uploaded by an equipment port of the WLAN network equipment are received, and optimization parameters generated by an intelligent network optimization solution layer are issued. The layer supports distributed acquisition, and the functions of concurrency of multiple data sources and high acquisition frequency are realized in a cluster control and load balancing mode.
(2) And the data storage layer is used for storing the acquired data, providing storage based on the original data for the machine learning layer, realizing hierarchical data storage and providing safe and reliable basic data for upper-layer services. The data storage layer can also provide service model data according to service requirements and directly perform visualization.
(3) Machine learning layer, the main functions include: firstly, analyzing and predicting the whole network wireless environment and network operation state trend by using wireless environment parameters uploaded by WLAN network equipment; secondly, analyzing and measuring the user experience condition by utilizing the collected user experience data; and finally, continuously learning the terminal user experience and the intelligent network optimization solution, and automatically adjusting the strategy of the network optimization solution to achieve the best effect.
(4) The intelligent network optimization solution layer provides corresponding intelligent solutions and algorithms by using the result of machine learning and a distributed computing structure aiming at the wireless network optimization problem, and realizes intelligent Radio Resource Management (RRM), intelligent access (optimal access optimization based on terminal access behavior analysis), intelligent roaming (roaming guide optimization based on terminal historical roaming characteristic analysis and environmental policy), and the like.
The presentation layer can provide an interactive interface with a user, visually and vividly show a complex wireless network and combine a service application effect. On one hand, the lower-layer service data is presented in different visual modes, and on the other hand, the data of user interaction is provided for the lower-layer service.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While alternative embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following appended claims be interpreted as including alternative embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.
Claims (10)
1. A method for optimizing a Wireless Local Area Network (WLAN), the method comprising:
receiving data collected by WLAN network equipment;
establishing an analysis model through an intelligent algorithm of machine learning, and analyzing the data by using the analysis model;
and determining a network optimization strategy according to the analysis result and issuing and executing the network optimization strategy.
2. The method of claim 1, wherein the receiving data collected by the WLAN network device comprises:
and receiving network equipment running state data, wireless environment data and user experience data which are acquired by the WLAN network equipment, wherein the data are acquired by a wireless controller AC and a wireless access point AP and are sent by compression operation.
3. The method of claim 1 or 2, wherein the establishing an analytical model through the machine-learned intelligent algorithm, and the analyzing the data with the analytical model comprises:
establishing a wireless environment perception trend model and a user experience perception model through an intelligent algorithm of machine learning;
based on the data, the wireless environment perception trend model is used for analyzing and obtaining the whole network WLAN wireless environment coverage condition, the interference condition and the network running state trend, the wireless network state is restored, the user experience condition is analyzed and measured by the user experience perception model, and the wireless network state and the whole network user experience condition are visualized.
4. The method of claim 3, wherein determining the network optimization policy and issuing the execution comprises:
and diagnosing the wireless network state according to the wireless WLAN environment, the equipment running state and the user experience obtained by analysis, analyzing the existing wireless network problems, determining a network optimization strategy aiming at the wireless network problems, and issuing and executing the network optimization strategy, wherein the wireless network problems comprise one or more of interference, access and roaming.
5. The method of claim 1, further comprising:
after the network optimization strategy is issued and executed, wireless network state feedback is obtained through analysis of the analysis model, and the network optimization strategy is adjusted according to user experience conditions and wireless network state differences before and after the network optimization strategy is executed, so that closed-loop convergence is achieved.
6. An apparatus for WLAN optimization, comprising: the device comprises a receiving unit, an analyzing unit and a strategy unit; wherein,
the receiving unit is used for receiving data collected by the WLAN network equipment;
the analysis unit is used for establishing an analysis model through an intelligent algorithm of machine learning and analyzing the data by using the analysis model;
and the strategy unit is used for determining a network optimization strategy according to the analysis result and issuing and executing the network optimization strategy.
7. The apparatus according to claim 6, wherein the receiving unit is specifically configured to receive network device operating state data, wireless environment data, and user experience data collected by a WLAN network device, where the data is sent by performing data collection and compression operations on a wireless controller AC and a wireless access point AP.
8. The device according to claim 6 or 7, wherein the analysis unit is specifically configured to establish a wireless environment perception trend model and a user experience perception model through a machine learning intelligent algorithm; based on the data, the wireless environment perception trend model is used for analyzing and obtaining the whole network WLAN wireless environment coverage condition, the interference condition and the network running state trend, the wireless network state is restored, the user experience condition is analyzed and measured by the user experience perception model, and the wireless network state and the whole network user experience condition are visualized.
9. The apparatus of claim 8, wherein the policy unit is specifically configured to diagnose the wireless network status according to a wireless WLAN environment and an equipment operating status and the user experience obtained through analysis, analyze an existing wireless network problem, determine a network optimization policy for the wireless network problem, and issue and execute the network optimization policy, where the wireless network problem includes one or more of interference, access, and roaming.
10. The apparatus of claim 6, wherein the analysis unit is further configured to obtain a wireless network state feedback through analysis of the analysis model after issuing and executing a network optimization policy, and adjust the network optimization policy with reference to a user experience and a wireless network state difference before and after executing the network optimization policy, so as to implement closed-loop convergence.
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| CN109274541A (en) * | 2018-11-19 | 2019-01-25 | 国家电网公司 | Communication network adjustment method and device |
| CN112335278A (en) * | 2018-06-22 | 2021-02-05 | 上海诺基亚贝尔股份有限公司 | Method, apparatus and computer readable medium for network optimization |
| CN112512059A (en) * | 2020-05-24 | 2021-03-16 | 中兴通讯股份有限公司 | Network optimization method, server, network side equipment, system and storage medium |
| WO2022105860A1 (en) * | 2020-11-23 | 2022-05-27 | 华为技术有限公司 | Method, system, and apparatus for terminal scanning, electronic device, and storage medium |
| CN116260721A (en) * | 2023-03-14 | 2023-06-13 | 国网智能电网研究院有限公司 | Virtual power plant software-defined communication control method, system, equipment and storage medium |
| CN116321238A (en) * | 2023-04-19 | 2023-06-23 | 深圳市九洲电器有限公司 | Intelligent adjustment system and method for wireless network |
| CN117676638A (en) * | 2023-11-17 | 2024-03-08 | 上海市信息网络有限公司 | A wireless network optimization supervision system and method based on the Internet of Things |
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