Access Point Selection Using Virtual Client Health Metrics
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Solution Overview
Problem
Conventional wireless networks rely solely on signal-to-noise ratio (SNR) information from received beacons for client device association, which can be inaccurate and incomplete, leading to suboptimal AP switching recommendations.
Innovation Solution
The introduction of a Virtual Client Health (VCH) metric, calculated based on uplink and downlink data rates and airtime utilization at each access point, provides a more comprehensive evaluation for improved AP selection, considering the number of client devices and airtime availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SNR information from received beacons is used for AP selection, then the selection process is simple, but the accuracy and completeness of the selection is poor
Solution Approach 1:
The patent segments the AP selection evaluation into multiple independent components: uplink data rate calculation, downlink data rate calculation, airtime utilization assessment, and client device count analysis. Each component is calculated separately using specific formulas, allowing comprehensive evaluation without excessive overall complexity. The segmentation enables precise measurement of each factor's contribution to AP suitability.
Solution Approach 2:
The patent introduces an intermediary metric system that translates raw network parameters (SNR, client count, airtime) into a unified evaluation framework. The intermediary calculations include effective uplink data rate, effective downlink data rate, and a composite AP suitability score. This intermediary layer bridges the gap between simple SNR measurements and accurate AP selection, maintaining computational feasibility while improving precision.
2Measurement precision
If comprehensive metrics including uplink data rate, downlink data rate, and airtime utilization are calculated, then the AP selection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary calculations of uplink and downlink data rates using established formulas that incorporate readily available parameters (SNR, client count, airtime utilization). These preliminary metrics are computed before the final AP comparison, allowing the system to prepare evaluation data in advance. This preliminary action reduces the computational burden during real-time decision-making, as the heavy lifting is done beforehand using cached or periodically updated values.
Solution Approach 2:
The patent transforms raw network parameters into changed-parameter forms that are more computationally efficient to process. For example, airtime utilization is converted into an effective data rate metric, and client device counts are transformed into load factor adjustments. These parameter changes maintain the comprehensive evaluation approach while reducing computational complexity through mathematical transformations that simplify subsequent comparisons.
3Productivity
If the number of connected client devices is considered in the evaluation, then the load balancing capability is improved, but the measurement complexity increases
Solution Approach 1:
The patent creates a universal metric framework where the client device count serves multiple evaluation purposes simultaneously. The same client count parameter is used to calculate airtime utilization, adjust effective data rates, and determine overall AP suitability. This multi-functional use of a single measurement simplifies the evaluation process compared to maintaining separate metrics for each aspect, reducing measurement complexity while maintaining comprehensive load balancing capability.
Data Source
AI summary
Example methods and apparatus to generate recommendation(s) for access point association by a client device are disclosed. An example method includes calculating an effective uplink data rate for the client with respect to a first access point based on a) an uplink data rate and b) a percentage of uplink airtime available to the client. The example method includes calculating a first effective downlink data rate with respect to the first access point based on a) a downlink data rate for and b) a percentage of downlink airtime available to the client. The example method includes computing a first metric for the first access point based on the first effective uplink data rate, the first effective downlink data rate, and a noise floor scaling factor. The example method includes generating an access point recommendation by comparing the first metric and a second metric for a second access point.