Wireless roaming network AP and Extender selection optimization method based on big data

Abstract

The invention provides a wireless roaming network AP and Extender selection optimization method based on big data, and relates to the technical field of network communication, the original STA trigger is changed into STA or big data trigger according to a modified roaming trigger mechanism; the AP collects STA RSSI, channel load and surrounding AP list information and periodically submits the information to the big data platform; the big data platform introduces a machine learning technology to train the previously collected wireless channel link parameters and throughput performance data to drive when to trigger roaming; the big data decides whether to roam or not, if yes, roaming is started, and the STA sends a roaming request to the AP; sending a roaming priority list to big data according to the AP; big data is returned to a roaming priority list; whether the STA is successfully connected with the target AP or not is judged, and if yes, roaming is ended; the invention introduces a wireless roaming network AP and Extender selection optimization method based on big data to improve the stability of STA roaming switching and the user experience after roaming.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a wireless roaming network AP and Extender selection optimization method based on big data. Background Art

[0002] Although the current roaming solution ensures that the services of wireless terminals are not interrupted during mobility, it still faces some problems in actual use;

[0003] First, the roaming behavior of wireless terminals is triggered by wireless terminals. However, due to differences in the implementation of wireless terminals by different manufacturers, signal thresholds and evaluation criteria are different, and the roaming effects of wireless terminals vary greatly. In addition, some wireless terminals are very slow to roam. This slowness is reflected in the fact that wireless terminals will prioritize the associated AP. As long as the signal of the currently associated AP has not decayed to the point where it cannot be used, even if there is an AP with better signal nearby that can be accessed, the wireless terminal will still hang on to the original AP and will not actively switch. This phenomenon is also called stickiness.

[0004] Secondly, wireless terminals do not have a view of the entire wireless network. When wireless terminals decide which AP to roam to, these decisions are entirely made by the AP. Currently, the AP basically makes judgments based on RSSI. The AP with the best signal is guided to connect to the STA as the first choice. This may cause too many terminals to be connected to an AP with the best signal. The quality of the wireless network after switching is still poor, while other APs with weaker signals have low utilization rates, thereby reducing the performance of the entire network.

[0005] In summary, in the prior art, although the wireless roaming solution ensures the service continuity of the wireless terminal during the mobile process, it still has significant shortcomings, which are specifically manifested in:

[0006] 1) Due to the inconsistency of roaming trigger mechanisms and signal evaluation standards of wireless terminals from different manufacturers, the roaming effects vary. Some terminals show obvious "stickiness" characteristics. Even if there are better access points (APs) around, they will not automatically switch, affecting user experience and effective use of network resources.

[0007] 2) Wireless terminals lack a global network perspective, and roaming decisions rely entirely on simple judgments by access points (APs) based on received signal strength indication (RSSI). This often leads to an unbalanced network load, with high-quality APs being overloaded and performance degraded, while other APs have idle resources, limiting overall network performance.

[0008] Therefore, we propose a wireless roaming network AP and Extender selection optimization method based on big data. Summary of the invention

[0009] In view of this, an embodiment of the present invention provides a wireless roaming network AP and Extender selection optimization method based on big data to achieve the purpose of improving the effect of network optimization.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] A wireless roaming network AP and Extender selection optimization method based on big data, comprising:

[0012] According to the modification of roaming trigger mechanism, the original STA trigger is changed to STA or big data trigger;

[0013] The AP collects STA RSSI, channel load, and surrounding AP list information and periodically submits it to the big data platform;

[0014] The big data platform introduces machine learning technology to train previously collected wireless channel link parameters and throughput performance data to drive when to trigger roaming. After receiving the preferred AP recommended by big data, the STA can combine its own logic to select the best time to trigger roaming.

[0015] Big data determines whether to roam, including whether to start roaming and the STA sending a roaming request to the AP;

[0016] Request roaming priority list based on big data sent by AP;

[0017] Big data returns the roaming priority list;

[0018] Whether the STA successfully connects to the target AP, including yes, the roaming ends.

[0019] Furthermore, the target AP selection mechanism is modified to change the original method of selecting the target AP based on the best RSSI to big data selection, thereby solving the problem of non-optimal AP selection.

[0020] Furthermore, signal strength is not the only factor that determines the quality of a wireless link. AP load, channel load, and surrounding signal interference also need to be considered.

[0021] Furthermore, the big data determines whether to roam, and also includes whether the STA repeatedly accesses the wireless network and periodically reports link information to the AP, and whether repeated roaming is triggered.

[0022] Furthermore, the AP periodically submits the collected RSSI, AP load, channel load, access and return link load information, and other information obtained from the beacon frame to the big data platform.

[0023] Furthermore, the big data platform trains previously collected wireless channel link parameters and throughput performance data through machine learning technology to drive the selection of the most suitable AP.

[0024] Furthermore, whether the STA is successfully connected to the target AP also includes whether or not, repeating the big data to return to the roaming priority list to perform the target connection.

[0025] Compared with the prior art, this technical solution mainly solves the following problems:

[0026] 1. Sticky terminal problem. Currently, the roaming triggering timing is unreasonable due to the sluggishness of wireless terminals to roaming. The original STA trigger is changed to STA or big data trigger. The AP collects STA RSSI, channel load, and surrounding AP list information and periodically submits it to the big data platform. The big data platform introduces machine learning (ML) technology to train the previously collected wireless channel link parameters and throughput performance data to select the best time to trigger roaming, completely solving the sticky terminal problem.

[0027] 2. The selection of the target AP is not the best problem. The original method of selecting the target AP based on the best RSSI is changed to big data selection. RSSI is not the best factor to determine the quality of the wireless link. The AP load, channel load, surrounding signal interference and other conditions also need to be considered. The AP regularly submits the collected RSSI, AP load, channel load, access and return link load information, and other information obtained from the beacon frame to the big data platform. The big data platform introduces machine learning (ML) technology to train the previously collected wireless channel link parameters and throughput performance data to drive the selection of the most suitable AP.

[0028] In summary, the beneficial effects brought by this technical solution are as follows:

[0029] 1. Ensure that the terminal triggers roaming at the most appropriate time: The roaming timing is jointly decided by the STA and big data to avoid mobile data interruption, improve the roaming switching success rate and user experience;

[0030] 2. Ensure that the terminal selects the most reasonable target AP: The original method of selecting the target AP based on the best RSSI is changed to big data selection. The big data platform introduces machine learning (ML) technology to train the previously collected wireless channel link parameters and throughput performance data to drive the selection of the most suitable AP, thereby improving the overall throughput of the network and user experience. Compared with the existing technology, this technical solution improves the switching success rate and user experience during roaming, and has obvious advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1A schematic diagram of a scenario of a wireless roaming network AP and Extender selection optimization method based on big data provided by the present invention;

[0032] Figure 2 A flowchart of a wireless roaming network AP and Extender selection optimization method based on big data provided by the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments 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.

[0034] To facilitate understanding of the present invention, the present invention will be described more comprehensively below with reference to related references, and several embodiments of the present invention are given. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0035] It should be noted that when an element is referred to as being "fixed on" another element, it may be directly on the other element or there may also be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used in this article are for illustrative purposes only.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0037] Example 1

[0038] like Figure 1-2 As shown, the present invention provides a technical solution: the wireless roaming network AP and Extender selection optimization method based on big data in this embodiment includes:

[0039] According to the modification of the roaming trigger mechanism, the original STA trigger is changed to STA or big data trigger, which completely solves the problem of sticky terminals;

[0040] The AP collects STA RSSI, channel load, and surrounding AP list information and periodically submits it to the big data platform. Specifically, the AP periodically submits the collected RSSI, AP load, channel load, access and backhaul link load information, and other information obtained from the beacon frame to the big data platform;

[0041] The big data platform introduces machine learning (ML) technology to train previously collected wireless channel link parameters and throughput performance data to drive when to trigger roaming. After receiving the preferred AP recommended by big data, the STA can combine its own logic to select the best time to trigger roaming.

[0042] Big data determines whether to roam, including whether to start roaming and the STA sending a roaming request to the AP;

[0043] It also includes whether the STA repeatedly accesses the wireless network and periodically reports link information to the AP, triggering repeated roaming;

[0044] Then, the AP sends a request to the big data for the roaming priority list;

[0045] Big data returns the roaming priority list;

[0046] Whether the STA successfully connects to the target AP, including yes, then the roaming ends;

[0047] Also includes no, duplicate big data returns to the roaming priority list for target connection.

[0048] What needs to be supplemented in the above embodiment is that the target AP selection mechanism is modified, and the original method of selecting the target AP based on the best RSSI is changed to big data selection, so as to solve the problem that the AP selection is not optimal.

[0049] Signal strength is not the only factor that determines the quality of a wireless link. AP load, channel load, and surrounding signal interference also need to be considered.

[0050] Furthermore, the big data platform uses machine learning technology to train the previously collected wireless channel link parameters and throughput performance data to drive the selection of the most suitable AP. This optimization can avoid an AP with the best signal connecting to too many terminals, resulting in poor wireless network quality after STA switching, thereby improving network performance.

[0051] Workflow of the present invention:

[0052] First, information collection and submission: AP regularly collects RSSI, channel load, AP load and other information of its associated STAs and submits it to the big data platform.

[0053] Machine learning training: The big data platform uses machine learning technology to train the collected data to predict when roaming is triggered and which AP is selected as the target AP;

[0054] Roaming trigger decision: The big data platform decides whether to trigger roaming based on the prediction results of the machine learning model and the current network conditions;

[0055] Target AP selection: If roaming is triggered, the big data platform recommends the most suitable AP as the target AP for the STA;

[0056] Roaming request and execution: STA selects the target AP and sends a roaming request based on the roaming priority list provided by the big data platform. If the connection is successful, the roaming ends; if the connection fails, the above process is repeated.

[0057] In summary, the present invention introduces a wireless roaming network AP and Extender selection optimization method based on big data to improve the stability of STA roaming switching and the user experience after roaming.

[0058] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wireless roaming network AP and Extender selection optimization method based on big data, characterized in that: include: According to the modification of roaming trigger mechanism, the original STA trigger is changed to STA or big data trigger; The AP collects STA RSSI, channel load, and surrounding APlist information and periodically submits it to the big data platform; The big data platform introduces machine learning technology to train previously collected wireless channel link parameters and throughput performance data to drive when to trigger roaming. After receiving the preferred AP recommended by big data, the STA can combine its own logic to select the best time to trigger roaming. Big data determines whether to roam, including whether to start roaming and the STA sending a roaming request to the AP; Request roaming priority list based on big data sent by AP; Big data returns the roaming priority list; Whether the STA successfully connects to the target AP, including yes, the roaming ends.

2. According to the big data-based wireless roaming network AP and Extender selection optimization method of claim 1, it is characterized by: The modified target AP selection mechanism changes the original target AP selection method based on the best RSSI to big data selection, thereby solving the problem that AP selection is not optimal.

3. According to the big data-based wireless roaming network AP and Extender selection optimization method of claim 1, it is characterized by: Signal strength is not the only factor that determines the quality of a wireless link. AP load, channel load, and surrounding signal interference must also be considered.

4. According to the big data-based wireless roaming network AP and Extender selection optimization method of claim 1, it is characterized by: The big data determines whether to roam, and also includes whether the STA repeatedly accesses the wireless network and periodically reports link information to the AP, and whether repeated roaming is triggered.

5. According to the big data-based wireless roaming network AP and Extender selection optimization method of claim 1, it is characterized by: The AP periodically submits the collected RSSI, AP load, channel load, access and backhaul link load information, and other information obtained from the beacon frame to the big data platform.

6. According to claim 5, a wireless roaming network AP and Extender selection optimization method based on big data is characterized in that: The big data platform trains previously collected wireless channel link parameters and throughput performance data through machine learning technology to drive the selection of the most suitable AP.

7. According to claim 1, a wireless roaming network AP and Extender selection optimization method based on big data is characterized in that: Whether the STA is successfully connected to the target AP also includes whether the STA is successfully connected to the target AP, and repeating the big data to return to the roaming priority list to perform the target connection.

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