Inverse BSS Transition Management for Temporal Roaming Avoidance
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Solution Overview
Problem
Existing wireless communication standards, such as IEEE 802.11, fail to optimize client station (STA) roaming, leading to sub-optimal decisions that consume resources without improving performance due to client STAs attempting to associate with APs that do not provide significant signal strength improvements.
Innovation Solution
Implementing artificial intelligence (AI)/machine learning (ML) techniques to guide client STAs on which APs to associate with and avoid, using trained models to predict roaming behavior and optimize roaming decisions based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If client STAs autonomously decide to roam to target APs based on signal strength thresholds, then roaming responsiveness is improved, but unnecessary roaming occurs consuming resources without performance improvement
Solution Approach 1:
The system performs preliminary actions by having APs predict future roaming behavior and classify client STAs before roaming decisions are made. The source AP evaluates parameters and determines mobile classification in advance, providing prediction information to guide the client STA's roaming decision, thereby avoiding unnecessary roaming while maintaining responsive roaming when needed.
Solution Approach 2:
The system implements feedback by having the source AP evaluate client STA parameters, predict roaming behavior, and communicate this prediction information back to the client STA. The client STA then uses this feedback to make informed roaming decisions, reducing unnecessary roaming while maintaining appropriate roaming responsiveness.
2Reliability
If client STAs frequently roam between APs to optimize signal strength, then connection quality is improved, but network stability deteriorates due to temporal roaming
Solution Approach 1:
The source AP performs preliminary evaluation of client STA parameters and predicts roaming behavior before the client STA makes a roaming decision. By classifying the client STA as mobile or non-mobile in advance and providing prediction information, the system prevents unnecessary roaming that would destabilize the network while allowing roaming when it improves connection quality.
Solution Approach 2:
The system provides feedback to the client STA about predicted roaming outcomes, enabling the client to make informed decisions that balance connection quality optimization with network stability. The prediction information acts as feedback that guides the client STA to roam only when beneficial, reducing temporal roaming and improving overall network stability.
3Productivity
If AI/ML models are implemented to predict roaming behavior and guide STA decisions, then roaming optimization is improved, but device complexity increases
Solution Approach 1:
The system introduces an intermediary approach where the source AP performs the complex evaluation and prediction functions, then communicates simplified prediction information to the client STA. This intermediary role at the AP side enables sophisticated roaming optimization without requiring complex AI/ML models to be implemented in the client STA, thereby improving roaming optimization while limiting the increase in device complexity.
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.