Multi-Personality Access Points With Protocol-Specific ML Switching
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Solution Overview
Problem
Existing access points face challenges in configuring ML models that are effective across different network protocols, as models optimized for one protocol often perform poorly with others.
Innovation Solution
Access points are equipped with multi-personality capabilities, allowing them to dynamically switch between operating modes using combinations of network protocols, and are provided with ML models tailored to these modes by a controller accessing a repository, with the ability to refine and train these models based on local conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single ML model is used at access points, then device complexity is reduced, but performance across different network protocols deteriorates
Solution Approach 1:
The access point is designed with multi-personality capability to support multiple network protocols (Wi-Fi, cellular, wired Ethernet) simultaneously. The system maintains a library of protocol-specific ML models and dynamically selects the appropriate model based on the active protocol combination, enabling universal adaptability across different protocol environments without requiring separate physical devices for each protocol type.
Solution Approach 2:
The system dynamically switches between different ML models based on the current operating mode and protocol combination. The controller receives notifications of mode changes from access points and responds by providing the appropriate ML model, allowing the system to adapt in real-time to changing network conditions and protocol requirements without manual reconfiguration.
2Adaptability or versatility
If protocol-specific ML models are deployed, then performance and adaptability improve, but device complexity and model management burden increase
Solution Approach 1:
A controller acts as an intermediary between the ML model repository and multiple access points. The controller receives notifications from access points about their current operating modes, determines the appropriate ML model from the repository, and provides it to the access point. This intermediary architecture simplifies model management by centralizing the decision-making process and automating model distribution, reducing the burden on individual access points and administrators.
Solution Approach 2:
The system implements a feedback mechanism where access points notify the controller of their current operating mode, and the controller responds by providing the appropriate ML model. This closed-loop feedback ensures that access points always have the correct model for their current protocol combination, automatically adapting to changes in network conditions without manual intervention.
3Reliability
If ML models are trained centrally in the cloud, then model quality improves, but network traffic and processing time increase
Solution Approach 1:
The ML model training and deployment process is segmented into distinct phases: centralized model training at the cloud controller using aggregated data from multiple access points, followed by local deployment of the trained model to individual access points. This segmentation allows high-quality model training to occur centrally while enabling fast, local inference at the edge, minimizing network traffic during operation and reducing processing delays.
Data Source
AI summary
Access point devices can be equipped with Machine Learning (ML) models to improve access point device operations. An access point device operating mode can be sent to a controller. The access point device operating mode can indicate multiple network protocols employed at the access point device. The controller can provide a coarse ML model to the access point device, wherein the coarse ML model is based on the operating mode. The access point device can then use local network traffic data, processed by the access point device, to train and refine the coarse ML model, and the access point device can use the resulting trained ML model in connection with network traffic processing determinations.