Inverse BTM Guidance for Temporal Roaming Avoidance
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Solution Overview
Problem
Existing wireless communication standards, such as IEEE 802.11, fail to optimize client roaming decisions, leading to sub-optimal performance due to client stations making 'useless' roaming decisions that consume resources without finding better signal strength, resulting in reduced throughput, increased latency, and lower transmission range.
Innovation Solution
Implementing artificial intelligence and machine learning techniques to guide client stations on which access points to associate with and avoid, using inverse basic service set transition management (BTM) to predict and mitigate temporal roaming based on machine learning models trained on roaming activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If client stations autonomously make roaming decisions based on signal strength, then roaming autonomy is maintained, but sub-optimal roaming decisions occur leading to reduced throughput and increased latency
Solution Approach 1:
The patent introduces an intermediary system (network controller or AP) that acts as a mediator between the client station and roaming decisions. The intermediary receives roaming-related information from the client, processes it using machine learning models, and provides guided recommendations. This resolves the contradiction by maintaining client autonomy while incorporating expert guidance to prevent sub-optimal decisions, thereby preserving throughput.
Solution Approach 2:
The patent implements a feedback mechanism where the network controller receives information about client roaming activity and performance metrics, processes this feedback through machine learning models, and sends back guided recommendations to the client station. This closed-loop feedback system enables the client to make informed roaming decisions that balance autonomy with performance optimization, resolving the throughput-latency issue.
2Productivity
If machine learning models guide roaming decisions, then throughput is improved and latency is reduced, but system complexity increases
Solution Approach 1:
The patent places the complex machine learning models in an intermediary network controller or AP rather than in the client station itself. This intermediary absorbs the computational complexity of running ML models, while the client station only needs to provide input data and receive simple guidance recommendations. This resolves the contradiction by maintaining high throughput through intelligent guidance while preventing client-side complexity from increasing.
Solution Approach 2:
The machine learning models in the network controller perform self-service by automatically processing roaming information and generating guidance recommendations without requiring manual configuration or complex client-side processing. The system serves itself by using historical data to continuously improve its guidance accuracy, thereby improving throughput without proportionally increasing system complexity.
3Reliability
If machine learning models are trained on roaming activity data, then roaming performance is optimized, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using historical roaming activity data before deployment. Once trained, the models are deployed to the network controller where they can quickly process real-time roaming decisions without requiring extensive computation during actual roaming events. This resolves the contradiction by shifting computational burden to the offline training phase, ensuring reliable roaming performance with minimal real-time processing delay.
Data Source
AI summary
Techniques and apparatus for facilitating client roaming within a wireless network are described. An example technique includes determining one or more parameters associated with roaming activity of a client station (STA) within the wireless network. The one or more parameters are evaluated with one or more machine learning (ML) models to determine a mobile classification associated with the client STA and roaming prediction information associated with the client STA. A message including the roaming prediction information and the mobile classification information is transmitted. Another example technique includes generating a message comprising an indication of the roaming prediction information, based at least in part on the mobile classification, and transmitting the message to a client STA.