Dynamic RF Site Grouping via Station Mobility Prediction
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Solution Overview
Problem
Current wireless local area network (WLAN) deployments face challenges with increased signal density, overhead contention, and suboptimal handoff due to high AP density and unawareness of station mobility patterns, leading to inefficiencies in RF site configurations.
Innovation Solution
The use of machine learning to predict station movement patterns, allowing for dynamic configuration of RF sites by allocating and configuring access points based on predicted association events, thereby optimizing WLAN and RF parameters to reduce congestion and improve quality of service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the density of access points is increased to avoid gaps or holes in the deployment, then full station mobility is supported, but signal density increases leading to overhead contention and suboptimal handoff
Solution Approach 1:
The system performs preliminary actions by collecting historical association data and training machine learning models to predict future station associations before they occur. This allows the network to proactively configure RF sites and allocate access points in advance, preventing overhead contention rather than reacting to it after stations have already connected and caused congestion.
Solution Approach 2:
The patent implements dynamic RF site configuration where access point allocations and WLAN configurations are continuously adjusted based on real-time predictions from machine learning models. This dynamic adaptation allows the system to optimize network performance by reconfiguring RF sites according to predicted station mobility patterns, thereby reducing overhead contention while maintaining full station mobility support.
2Ease of operation
If WLAN broadcast is enabled on all APs to simplify client association, then client devices can automatically connect, but overhead contention increases due to repeated broadcast probes
Solution Approach 1:
The system applies local quality by enabling WLAN broadcast selectively on specific access points based on predicted station association patterns. Instead of uniformly enabling broadcast on all APs, the machine learning model identifies which APs are likely to serve incoming stations and enables broadcast only on those, thereby maintaining ease of client association while minimizing overhead contention from unnecessary broadcast probes.
Solution Approach 2:
The patent changes the parameter of WLAN broadcast enablement from a static all-or-nothing configuration to a dynamic, prediction-driven setting. The system continuously adjusts which APs enable broadcast based on real-time analysis of historical association data and current predictions, optimizing the balance between client association ease and overhead contention reduction.
3Device complexity
If current deployments are incognizant of station mobility patterns, then deployment complexity is reduced, but handoff between APs becomes suboptimal
Solution Approach 1:
The system implements self-service by automatically collecting historical association data, training machine learning models, and generating predictive configurations without requiring manual network planning or configuration. The patent employs automated workflows where the system serves itself by continuously learning from observed station mobility patterns and applying this knowledge to optimize handoff performance, thereby maintaining low deployment complexity while achieving high reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors actual station association patterns and compares them with predictions from the machine learning model. This feedback loop allows the system to refine its predictions and adjust RF site configurations dynamically, improving handoff performance while maintaining automated operation and low deployment complexity.
Data Source
AI summary
Techniques for dynamic RF site configuration are provided. Historical association data is collected from a plurality of access points in a physical environment, and a machine learning model is trained to predict future association events, based on the historical association data. Current association data is then collected from the plurality of access points, and at least one predicted association event is generated by processing the current association data using the trained machine learning model. The plurality of access points is allocated to a plurality of radio frequency (RF) sites based on the at least one predicted association event. Finally, at least one of the plurality of RF sites is configured based on the predicted association event.