RF Spectrum Capture Triggered by WiFi Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions struggle to efficiently track and troubleshoot changes in wireless network deployments due to environmental changes, requiring manual intervention and inefficient troubleshooting processes that do not scale well with large numbers of deployments.
Innovation Solution
A computing system dynamically captures RF spectrum data at a site based on anomalous events without human intervention, storing the data for future troubleshooting and root cause analysis, using a cloud-based network management system or local controller to determine the cause of the event.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual site survey tools are used to evaluate deployment environment, then deployment accuracy is improved, but time consumption and labor cost increase
Solution Approach 1:
The system enables automatic self-evaluation of the wireless deployment environment by having AP devices autonomously perform spectrum captures and generate assessment reports, eliminating the need for manual site surveys while maintaining evaluation accuracy
Solution Approach 2:
Manual mechanical site survey processes are replaced with automated electronic systems that use RF spectrum captures and algorithmic analysis to evaluate deployment environments, significantly reducing time consumption while preserving measurement precision
2Reliability
If existing troubleshooting tools are used to track environmental changes, then troubleshooting capability is maintained, but scalability deteriorates with large numbers of deployments
Solution Approach 1:
Manual troubleshooting processes are replaced with automated anomaly detection systems that use machine learning algorithms to analyze spectrum data and identify issues across large-scale deployments, maintaining troubleshooting capability while enabling scalability to thousands of sites
Solution Approach 2:
An automated intermediary system consisting of spectrum capture devices and analysis algorithms is introduced between the physical wireless environment and the troubleshooting process, enabling scalable monitoring and diagnosis across large numbers of deployments without requiring direct manual intervention at each site
3Reliability
If RF spectrum capture is performed continuously to capture all anomalies, then detection completeness is improved, but energy consumption and data storage requirements increase
Solution Approach 1:
Instead of continuous spectrum capture, the system employs periodic captures triggered by specific events or time intervals, maintaining anomaly detection completeness while significantly reducing energy consumption and data storage requirements through event-driven sampling
Solution Approach 2:
The system performs preliminary analysis of network parameters to identify anomalies before triggering full spectrum captures, enabling selective capture only when necessary, thus maintaining detection completeness while minimizing energy and storage resource usage
Data Source
AI summary
Techniques are described for dynamically invoking a radio frequency (RF) spectrum capture at a site based on detection of an anomalous event at a node or radio of an access point (AP) device at the site. For example, a system is configured to detect an anomalous event at a node of an AP device based on network data obtained for the AP device; based on the anomalous event, invoke an RF spectrum capture by one or more nodes of one or more AP devices at the site; store spectrum data obtained from the RF spectrum capture in association with the anomalous event; and determine a root cause of the anomalous event based on the spectrum data associated with the anomalous event and the network data from which the anomalous event was detected.