Adaptive WLAN Steering Algorithm Controller
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Solution Overview
Problem
Steering algorithms in wireless local area networks (WLANs) are not standardized, leading to frequent and potentially disruptive steering events, with default settings being inadequate for diverse wireless stations and environments, often resulting in poor performance and user experience.
Innovation Solution
A steering algorithm controller that collects and analyzes channel utilization and performance data to assess algorithm performance, tracks wireless station behavior, and tunes algorithm parameters based on mobility and demand patterns to optimize radio interface selection, thereby improving user experience and reducing service disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If steering algorithms run continuously with default settings, then radio interface selection is performed, but frequent steering events cause service disruptions and poor user experience
Solution Approach 1:
The steering algorithm transitions from static default settings to dynamic adaptive settings that automatically adjust based on real-time wireless station behavior patterns, mobility characteristics, and network conditions, resolving the contradiction between active steering and service disruption
Solution Approach 2:
The steering algorithm performs self-tuning by automatically analyzing its own performance metrics and adjusting parameters without external intervention, enabling the system to optimize service continuity while maintaining effective radio interface selection
2Device complexity
If uniform default settings are applied to all wireless stations, then algorithm operation is simplified, but performance is inadequate for diverse station behaviors and environments
Solution Approach 1:
The steering algorithm implements location-specific and station-specific parameter settings by analyzing individual wireless station behaviors, mobility patterns, and environmental characteristics, allowing each station to receive optimized settings rather than uniform defaults
Solution Approach 2:
The algorithm dynamically changes operating parameters based on detected wireless station behaviors, mobility characteristics, and network conditions, transforming static default parameters into adaptive parameters that optimize performance for each specific scenario
3Productivity
If steering algorithm parameters are manually tuned, then performance can be optimized, but technician intervention is time-consuming and error-prone
Solution Approach 1:
The steering algorithm implements self-tuning capabilities by automatically monitoring performance metrics, analyzing wireless station behaviors, and adjusting its own parameters without requiring manual technician intervention, eliminating time loss while maintaining optimization
Solution Approach 2:
The algorithm incorporates feedback mechanisms that continuously monitor steering event outcomes and use this information to automatically adjust parameters, creating a closed-loop system that optimizes performance without external input
Data Source
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AI summary
A steering algorithm controller (100; 200) controls a steering algorithm (150; 250) selecting for one or more wireless station (171, 172) an optimal radio interface amongst available access points (151, 152, 153; 251) in a wireless local area network. The steering algorithm controller (100; 200) comprises: - data collection means (211, 212) collecting channel utilization and performance data for the wireless stations (171, 172) and the available radio interfaces; - steering algorithm performance assessment means (202) analysing steering events, analysing the channel utilization and performance data, and determining therefrom a performance score for the steering algorithm (150; 250); - wireless station behaviour tracking means (203) analysing mobility and service demand behaviour of the wireless stations (171, 172); - profile estimation means (204) determining behaviour profiles; and - steering algorithm tuning means (205) for tuning parameters of the steering algorithm (150; 250) based on the performance score and the behaviour profiles.