Wireless Access Point Proximity Prediction for Seamless Roaming
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Solution Overview
Problem
Existing wireless local area networks (WLANs) face inefficiencies in managing wireless communication between access points and stations due to increased density, leading to unnecessary overhead in connectivity transitions and network disruptions.
Innovation Solution
A wireless access point (WAP) equipped with a proximity circuit, dwell time circuit, and prediction circuit that accumulates historical records to predict the most probable connectivity options for stations, allowing for seamless transitions between communication channels based on predicted proximity and dwell times, reducing unnecessary network disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional connectivity management is used in dense WLAN deployments, then device compatibility and basic connectivity are maintained, but network overhead increases and connectivity transitions cause disruptions
Solution Approach 1:
The system performs preliminary actions by accumulating historical proximity records and predicting future connectivity options before transitions are needed. The prediction circuit analyzes past proximity data to determine likely future connectivity states, allowing the WAP to prepare and execute transitions proactively rather than reactively, thereby reducing disruptions and optimizing network resource usage.
2Productivity
If connectivity transitions are made frequently to optimize performance, then communication efficiency improves, but network disruptions and overhead increase
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual connectivity outcomes and comparing them with predicted options. The WAP accumulates historical records of proximity transitions and connectivity outcomes, using this feedback to refine future predictions and optimize transition timing, thereby improving communication efficiency while minimizing disruptions through data-driven decision making.
3Measurement precision
If historical data accumulation is performed for all stations, then prediction accuracy improves, but memory requirements and processing complexity increase
Solution Approach 1:
The system applies local quality by focusing data collection and processing on individual stations rather than treating all stations uniformly. The prediction circuit analyzes historical proximity records specific to each station's behavior patterns, enabling accurate predictions for each device while avoiding the need to process and store redundant data for all stations, thus reducing overall system complexity.
Data Source
AI summary
Systems and methods for a wireless device to determine whether to associate with a wireless access point in view of a historical location-based record to predict a probability for a future location state based on a historical dwell time of the historical location-based record. Some examples include a wireless device receiving information for a location with multiple access points, where the location has a plurality of rooms, and the information includes historical proximity records of wireless stations associated with each of the multiple access points, and current proximity metrics for at least one of the multiple access points.