WLAN Machine Learning Capability Exchange for Cross-Device Model Sharing
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Solution Overview
Problem
The IEEE 802.11 specifications lack support for machine learning techniques across devices, limiting their application in optimizing wireless communication features, such as enhanced distributed channel access and interference estimation.
Innovation Solution
A machine learning framework is introduced that allows wireless devices to communicate machine learning capabilities and models, enabling standardized exchange of machine learning models and techniques across devices, including access points and stations, to optimize features like EDCA, interference estimation, and rate adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary machine learning models are used by individual STAs or APs, then optimization of implementation-specific features is achieved, but standardization and cross-device compatibility are limited
Solution Approach 1:
The patent introduces a universal machine learning framework that enables multiple devices (STAs and APs) to share and utilize the same machine learning models. The framework defines standardized message types and capabilities fields that allow any device in the WLAN to exchange model information, making the system universally applicable across different hardware implementations while maintaining model compatibility.
Solution Approach 2:
The patent employs parameter-based capability indication where devices advertise their machine learning support through structured fields indicating model types, formats, and versions. By changing and standardizing these parameters (capability flags, model identifiers, format specifications), the system achieves cross-device compatibility without requiring complex proprietary implementations at each device.
2Productivity
If machine learning techniques are applied to optimize wireless communication features, then communication efficiency is improved, but standardization support is lacking
Solution Approach 1:
The patent implements preliminary capability advertisement where devices declare their machine learning support, model types, and format capabilities before actual model exchange or application. This preliminary action establishes a standardized framework that ensures reliable interoperability, allowing devices to confirm compatibility before utilizing machine learning techniques to improve communication efficiency.
Solution Approach 2:
The patent introduces standardized message structures and capability fields as intermediaries between machine learning models and wireless communication protocols. These intermediary elements (defined message types, capability indicators, model exchange protocols) provide the missing standardization layer that enables reliable machine learning application across different devices while maintaining protocol compatibility.
3Adaptability or versatility
If machine learning models are shared between devices, then model utilization flexibility is increased, but information exchange complexity increases
Solution Approach 1:
The patent segments the machine learning model exchange process into distinct standardized components: capability advertisement messages, model format specifications, version identifiers, and exchange protocols. By dividing the complex information exchange into structured segments with defined fields and formats, the system achieves flexible model sharing while managing complexity through standardization of each segment.
Data Source
AI summary
An apparatus has a memory and one or more processors coupled to the memory. The processor(s) is configured to transmit a first message indicating a first machine learning capability of the first wireless device. The processor(s) is also configured to receive, from a second wireless device, a second message indicating a second machine learning capability of the second wireless device. The processor(s) is further configured to communicate information associated with a machine learning model for use between the first wireless device and the second wireless device based at least in part on the second machine learning capability and the first machine learning capability. The processor(s) is also configured to communicate with the second wireless device based at least in part on the machine learning model.