Centralized Wireless Access Point Awareness Layers for Transparent Automation
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Solution Overview
Problem
Existing wireless communication networks face inefficiencies in transmission medium usage, payload traffic maximization, and end-user experience due to sub-optimal network management and lack of flexibility in managing access points, particularly in complex environments where conventional solutions fail to provide intelligent management and scalability.
Innovation Solution
A centralized wireless network system with a central controller installs user space applications in access points to establish communication loops, classifies them into administrative groups based on KPIs, generates awareness layers using Explainable Artificial Intelligence (XAI), and determines network driving actions for optimized management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional black-box solutions are used for WLAN management, then automation is achieved, but transparency and confidence level deteriorate
Solution Approach 1:
The patent introduces an intermediary layer (the centralized controller with XAI module) between the ML/AI algorithms and the network management system. This intermediary translates the black-box decisions into interpretable insights, allowing automated operation while maintaining transparency through explainable AI techniques that reveal the rationale behind management decisions.
Solution Approach 2:
The system changes the state of information transparency by implementing multiple awareness layers (physical, data link, network, application) that progressively reveal more information about network conditions and decisions. This parameter change transforms the opaque black-box operation into a multi-layered transparent system where decisions can be traced and understood.
2Extent of automation
If ML/AI algorithms are made more sophisticated for complex WLAN environments, then automation capability improves, but maintainability and debugging capability deteriorate
Solution Approach 1:
The patent segments the complex ML/AI system into manageable awareness layers (physical, data link, network, application) and functional modules (data collection, processing, decision-making, execution). This segmentation allows maintainers to debug and repair specific layers independently without dealing with the entire sophisticated system at once, improving maintainability while preserving automation capability.
Solution Approach 2:
The XAI module serves as an intermediary that bridges the sophisticated ML/AI algorithms and the maintainers. It translates complex algorithmic decisions into human-understandable explanations, enabling easier debugging and maintenance of advanced automated systems by revealing the logic flow and decision rationale.
3Device complexity
If existing solutions are deployed as black-boxes without rationale provision, then device complexity is reduced, but adaptability to complex environments deteriorates
Solution Approach 1:
The patent adds a new dimension of interpretability and awareness to the system by implementing multiple awareness layers that provide comprehensive visibility into network conditions. This dimensional addition allows the system to adapt to complex environments through enhanced situational awareness while maintaining relatively simple deployment through centralized controller architecture.
Data Source
AI summary
This disclosure relates to method and system for managing a plurality of access points in a centralized wireless network. The method includes installing one or more user space applications in each of the plurality of access points; receiving in real-time a plurality of Key Performance Indicators (KPIs) from each of the plurality of access points through the one or more user space applications; classifying in real-time, each of the plurality of access points into a set of administrative groups based on the plurality of KPIs; and for an access point in each of the set of administrative groups, generating a set of awareness layers corresponding to the access point based on the plurality of KPIs through the one or more user space applications. Each of the set of awareness layers is a data representation corresponding to one or more of the plurality of KPIs.