Sticky Client Detection via ML Sliding Boundary Prediction
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Solution Overview
Problem
Wireless networks face performance issues due to sticky clients, which fail to roam to better connection conditions, leading to sub-optimal network performance for both the sticky client and other connected devices, as they often use lower modulation and coding schemes, occupying more airtime and causing increased latency and reduced throughput.
Innovation Solution
The implementation of machine learning models to detect and remediate sticky clients by predicting a sliding boundary between access points, determining the likelihood of successful association with a target AP, and triggering roaming requests to improve client STA connections, thereby optimizing roaming and network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If client STA uses lower MCS due to poor connection conditions, then communication reliability is improved, but airtime consumption increases and network throughput decreases
Solution Approach 1:
The access point performs preliminary detection of sticky client status by monitoring roaming parameters and predicting sliding boundaries before the client actually roams. This allows the network to proactively manage the sticky client's connection, adjusting MCS and airtime allocation in advance to prevent throughput degradation while maintaining reliable communication.
Solution Approach 2:
The system continuously monitors roaming parameters (signal strength, connection stability) and uses this feedback to detect when a client becomes sticky. The access point adjusts transmission parameters based on this feedback, dynamically balancing reliability requirements with throughput optimization by identifying and managing sticky clients through ongoing parameter evaluation and adaptive control.
2Stability of the object's composition
If sticky client remains associated with current AP, then connection stability is maintained, but network performance for all clients deteriorates
Solution Approach 1:
The patent replaces traditional client-autonomous roaming decisions with a network-controlled approach using machine learning models. The access point uses ML to predict sliding boundaries and determine optimal roaming timing, substituting the mechanical/client-based roaming process with an intelligent network-managed system that balances individual connection stability with overall network performance.
Solution Approach 2:
The system changes the parameter of roaming decision-making from client-based threshold comparisons to network-based predictive sliding boundary calculations. By using machine learning models to evaluate multiple parameters (signal strength, connection stability, network load) and dynamic threshold adjustment, the system optimizes when clients should roam to maintain stability while preventing network performance degradation.
3Ease of operation
If roaming process is controlled by client STA, then client autonomy is improved, but sticky client detection accuracy decreases
Solution Approach 1:
The access point acts as an intermediary between the client STA and the network control system. It collects roaming parameters from autonomous client decisions, processes them through machine learning models, and provides refined roaming recommendations. This intermediary role allows the system to leverage client autonomy while improving detection accuracy through network-side analysis of roaming patterns and sliding boundary predictions.
Data Source
AI summary
Techniques and apparatus for performing sticky client detection and/or remediation with machine learning are described. An example technique includes determining one or more parameters associated with roaming activity of a client station (STA) within a wireless network. The one or more parameters are evaluated with a machine learning model to predict a sliding boundary associated with the client STA, a first access point (AP) within the wireless network, and a second AP within the wireless network. Information associated with the sliding boundary is transmitted to the first AP. A frame including a request for the client STA to roam to the second AP and the information associated with the sliding boundary is transmitted to the client STA.