Radar Event Classification for DFS False Alarm Reduction
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Solution Overview
Problem
Conventional WiFi systems face challenges in accurately distinguishing between true and false radar events, leading to high false positive rates and network interference due to the enforcement of dynamic frequency selection (DFS) mechanisms, which are currently inefficient and computationally costly.
Innovation Solution
A decision intelligence (DI)-based framework that utilizes inferred false alarm rates and machine learning models to classify radar events, considering local factors like zip code, day of week, and time of day, to optimize network configurations and avoid false radar alarms, thereby enhancing DFS detection and reducing interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional DFS mechanisms are used to monitor radar activities, then WiFi systems can avoid interference with critical radio communications, but the false positive rate increases and channel selection is limited
Solution Approach 1:
The system performs preliminary actions by collecting historical radar event data and training machine learning models in advance to establish baseline false alarm rates for different locations and time periods. This preliminary training enables the system to distinguish true radar events from false alarms without limiting channel selection during actual operation.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring radar events, comparing them against predicted false alarm rates from the ML model, and adjusting DFS behavior accordingly. True radar events trigger channel switching while false alarms are filtered out, improving detection accuracy without unnecessary channel restrictions.
2Reliability
If high true positive rate is achieved for radar detection, then interference with critical communications is reduced, but computational cost and system complexity increase
Solution Approach 1:
The system introduces machine learning models as intermediaries between raw radar detection data and DFS decision-making. These ML models process historical data to predict false alarm patterns, serving as a mediator that simplifies the complexity of distinguishing true radar events from false alarms while maintaining high detection accuracy.
Solution Approach 2:
The system creates simplified representations of complex radar detection patterns by training ML models on historical data. These models copy and generalize from past radar event patterns to predict future false alarms, reducing the computational complexity of real-time radar detection while maintaining high accuracy.
3Reliability
If DFS channel monitoring is continuously performed, then interference with critical radio communications is avoided, but network downtime and user experience degradation occur due to false alarms
Solution Approach 1:
The system performs preliminary training of ML models using historical radar data to establish location-specific false alarm baselines before deployment. This advance preparation enables the system to operate with high network availability by accurately distinguishing true radar events from false alarms during continuous DFS monitoring, preventing unnecessary channel switches and network downtime.
4Productivity
If location-specific false alarm rates are used to optimize DFS behavior, then network performance is improved, but data processing and model training requirements increase
Solution Approach 1:
The system applies local quality by training separate ML models for different geographic locations, each capturing location-specific false alarm patterns. This localized approach improves network performance in each region by adapting to local radar environments while processing only relevant local historical data, rather than requiring global data processing for all locations.
Data Source
AI summary
Disclosed are computerized systems and methods for classification between radar events related to managing and controlling the network configuration and the connectivity of UE based therefrom. The disclosed framework operates to utilize inferred false alarm rates to determine or discern a classification and/or proportionality of the effects of factors of interest, which can impact a local network (e.g. zip code, day of week, time of day, location interference, and the like) to the false alarm rate. Accordingly, as discussed herein, the disclosed framework can compile controls and/or executable instructions that can manipulate, modify and/or optimize networks within particular regions of interest (or “decision regions”), such that, among other technical controls, can alter the DFS mode settings of APs, UEs and/or WiFi networks as a whole for particular locations.