Multi-Protocol Access Point ML Configuration for Local Traffic Control
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Solution Overview
Problem
Existing access points equipped with machine learning (ML) models face challenges as these models are often protocol-specific, failing to adapt effectively when switching between different network protocols like Wi-Fi and 5G, leading to suboptimal performance and inefficient data processing.
Innovation Solution
A system architecture that configures access points with ML models tailored to their specific operating modes, utilizing a controller to distribute and refine ML models based on the combination of network protocols used, enabling dynamic adjustment and local training to enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single ML model is deployed at an access point, then the device complexity is reduced, but the adaptability to different network protocols deteriorates
Solution Approach 1:
The access point is designed with multi-functionality to support multiple network protocols (Wi-Fi, 5G, CURBW) simultaneously, with each protocol having its own dedicated ML model. This allows the single access point device to adapt to different protocols without increasing overall device complexity, as the multi-protocol capability is built into the fundamental architecture.
Solution Approach 2:
The ML model functionality is segmented into protocol-specific models, where each network protocol has its own dedicated ML model tailored to its characteristics. This segmentation allows each model to be optimized for its specific protocol while the access point manages multiple models efficiently, resolving the contradiction between simplicity and adaptability.
2Adaptability or versatility
If protocol-specific ML models are deployed for each network protocol, then the adaptability to different protocols is improved, but the device complexity increases
Solution Approach 1:
A controller acts as an intermediary between the access point and the multiple protocol-specific ML models. The controller receives operating mode information from the access point, selects the appropriate ML model based on the active network protocols, and distributes it to the access point. This intermediary manages the complexity of handling multiple models, freeing the access point from direct model management responsibilities.
Solution Approach 2:
The system implements feedback mechanisms where the access point reports its operating mode (which network protocols are active) to the controller, and the controller responds by providing the appropriate ML model. This feedback loop ensures the correct model is deployed without requiring the access point to manually manage multiple models, reducing operational complexity.
3Ease of operation
If generic ML models are used across all protocols, then the ease of operation is improved, but the measurement precision of traffic control deteriorates
Solution Approach 1:
Each network protocol receives a locally optimized ML model that is specifically trained for its characteristics and traffic patterns. Instead of using a generic model for all protocols, the system deploys protocol-specific models that are tailored to the local requirements of each protocol, improving traffic control accuracy while maintaining ease of operation through automated model selection.
4Productivity
If access points process traffic locally with ML models, then the productivity is improved, but the loss of information to cloud servers increases
Solution Approach 1:
ML models are pre-deployed at the access point based on the active operating mode, enabling real-time local processing of network traffic without needing to continuously communicate with cloud servers. The preliminary deployment of appropriate models allows the access point to autonomously perform traffic control functions, improving productivity while minimizing data transmission to the cloud.
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.