Access Point Roaming via Server-Side Machine Learning Inference
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Solution Overview
Problem
Current wireless communication systems conforming to the IEEE802.11 standard lack effective frame configuration and data collection methods for implementing machine learning in roaming, which hinders optimal roaming decision-making.
Innovation Solution
A communication apparatus is designed to transmit specific information to a server for data collection and inference, allowing for the determination of necessary roaming and notification of a suitable roaming destination based on collected data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning is introduced to optimize roaming decisions, then communication quality and roaming efficiency are improved, but system complexity and infrastructure requirements worsen
Solution Approach 1:
The patent introduces a server as an intermediary component that handles machine learning inference for roaming decisions. The access point collects communication status data and transmits it to the server, which performs the complex inference processing and returns roaming recommendations. This mediator approach allows the AP to benefit from ML capabilities without incorporating complex ML hardware or software directly into the AP device.
Solution Approach 2:
The system divides the roaming optimization function into separate components: data collection at the access point, data transmission to server, inference processing at server, and result application at access point. This segmentation allows the complex ML inference to be performed centrally while keeping individual access points relatively simple, resolving the contradiction between improved productivity and reduced device complexity.
2Measurement precision
If comprehensive communication status data is collected for machine learning inference, then inference accuracy is improved, but data transmission load and processing time worsen
Solution Approach 1:
The patent transmits 'part or all of pieces of information' to the server, allowing flexibility in data quantity. The system can adjust the scope of transmitted data based on requirements, transmitting only necessary information for accurate inference while avoiding unnecessary data transmission that would increase time loss. This partial action approach balances inference accuracy with transmission efficiency.
3Device complexity
If traditional roaming methods are used without machine learning, then system simplicity is maintained, but roaming optimization and communication quality improvement are limited
Solution Approach 1:
The system implements a feedback loop where communication status data is continuously collected, transmitted to the server for inference, and the inference results are applied to make roaming decisions. This feedback mechanism enables the system to adapt to changing communication conditions and optimize roaming decisions based on actual performance data, thereby improving communication quality while maintaining reasonable system simplicity through the use of a centralized server.
Data Source
AI summary
An access point (AP) that performs a transmitting, to a server, part or all of pieces of information with the information included in an inference request, an acquiring a result of the inference of the quality of the communication, a determining whether to perform the roaming processing, and, a notifying, in a case where it is determined to perform the roaming processing, said other communication apparatus of a performance of the roaming processing.