WLAN Machine Learning Model Exchange for Device Interoperability

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Solution Overview

Problem

The IEEE 802.11 specifications lack support for the use of machine learning techniques across devices, limiting their application in optimizing wireless communication features, such as enhanced distributed channel access and interference estimation, which are typically left to proprietary models or heuristic algorithms.

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 information, including model types, structures, and parameters over an air interface, facilitating collaboration and optimization across devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If proprietary machine learning models are used by individual devices, then optimization of wireless communication features can be achieved, but device compatibility and standardized collaboration are limited

Engineering Contradiction:

Improveoptimization efficiencyVSAvoiddevice compatibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal machine learning framework that enables multiple devices to collaboratively optimize wireless communication features. The framework defines standardized interfaces and protocols that allow different devices with varying proprietary models to work together harmoniously, achieving both optimization efficiency and device compatibility

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If machine learning techniques are applied to wireless communications, then communication efficiency and performance can be improved, but standardization and interoperability are lacking

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidstandardization

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent transforms the parameter space by defining standardized machine learning interfaces, data formats, and communication protocols. This standardization enables reliable interoperability while preserving the flexibility to apply various machine learning techniques for improving communication efficiency

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If conventional heuristic algorithms are used for channel access and interference estimation, then implementation simplicity is maintained, but optimization performance is limited

Engineering Contradiction:

Improveimplementation simplicityVSAvoidoptimization performance

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces conventional heuristic algorithms with machine learning-based approaches for channel access and interference estimation. The standardized framework simplifies the implementation of these complex ML techniques by providing predefined interfaces and protocols, thereby maintaining ease of operation while significantly improving optimization performance

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12369023B2Machine learning framework for wireless local area networks (WLANs)

Publication Date: 2025.07.22 QUALCOMM INC

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.