WLAN Interference Classification via Cloud Repository
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Access points in WLANs have limited memory capacity, preventing them from storing a sufficient number of classifiers to correctly identify new interference device types, leading to incorrect classification of interference signals and requiring periodic OS updates for new classifiers.
Innovation Solution
A centralized spectrum monitoring service in the cloud maintains a large repository of classifiers, allowing access points to forward unknown FFT patterns for matching and updating local classifiers dynamically, enabling real-time classification of interference sources without OS updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If access points store a large number of classifiers locally, then interference classification accuracy improves, but device memory capacity is exceeded
Solution Approach 1:
The patent introduces a centralized repository as an intermediary between access points and classifiers. The repository stores the complete set of classifiers centrally, while access points maintain only lightweight local classifiers. When classification is needed, access points query the centralized repository, which returns matching classifiers. This mediator architecture allows access points to achieve high classification accuracy without storing large numbers of classifiers locally, resolving the contradiction between classification accuracy and memory capacity.
2Adaptability or versatility
If access points update local classifiers periodically via OS updates, then new interference types are recognized, but network downtime increases
Solution Approach 1:
The system performs preliminary actions by pre-populating the centralized repository with a comprehensive database of classifiers covering various interference types before deployment. Access points are pre-configured with basic classification capabilities. When new interference types are detected, the system proactively queries the centralized repository and downloads updated classifiers during off-peak times or in the background, rather than requiring scheduled OS updates. This preliminary preparation and proactive updating approach enables continuous adaptation to new interference types while minimizing network downtime.
3Device complexity
If access points use only pre-defined local classifiers, then device complexity is reduced, but classification reliability decreases for unknown interference
Solution Approach 1:
The patent merges the local classifier system at access points with a centralized repository system. Access points maintain simple local classifiers for quick initial classification, reducing device complexity. When local classifiers fail to identify interference or encounter unknown types, the system automatically queries the centralized repository for additional classifiers. This combined architecture allows access points to keep their local systems simple while achieving high classification reliability for both known and unknown interference types through the centralized resource.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Example method includes: receiving, by an access point in a wireless local area network (WLAN), a plurality of wireless interference signals from an interfering device; deriving, by the access point, a fast Fourier transformation (FFT) pattern from the plurality of wireless interference signals received from the interfering device; transmitting, by the access point, the FFT pattern to a centralized repository that is remote to the access point; receiving, by the access point, a classifier from the centralized repository; and classifying, by the access point, the interfering device into a specific device type using the classifier received from the centralized repository based on the FFT pattern.