Adaptive Beamforming Feedback Using ML Channel Classification

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Solution Overview

Problem

Existing wireless communication systems lack effective beamforming feedback mechanisms that adapt to varying channel conditions, leading to inefficiencies and increased overhead.

Innovation Solution

A wireless communication apparatus that utilizes machine learning to adjust beamforming feedback resources based on channel characteristics, employing algorithms to classify channels and optimize feedback frames, thereby reducing overhead and improving communication performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional beamforming feedback mechanisms are used, then beamforming can be performed, but overhead is increased and adaptability to varying channel conditions is poor

Engineering Contradiction:

Improveadaptability to channel conditionsVSAvoidfeedback overhead

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies parameter changes by using machine learning algorithms to dynamically adjust beamforming feedback parameters based on channel characteristics. The system classifies channel conditions and adapts feedback resources accordingly, changing the parameters of feedback transmission to match the actual channel state, thereby achieving both adaptability and reduced overhead.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system employs self-service by enabling the wireless communication apparatus to autonomously classify its own channel conditions and determine appropriate feedback resources using machine learning algorithms. The apparatus automatically adjusts its feedback behavior without requiring external control, making the system adaptive while minimizing overhead.

Inventive Principle:

Principle #25Self-service

2Productivity

If machine learning algorithms are applied to classify channels, then adaptability and communication performance are improved, but processing complexity increases

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidprocessing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical or rule-based channel classification systems with machine learning algorithms. This substitution enables more intelligent and accurate channel characterization, improving communication efficiency through better-adapted beamforming feedback while the algorithms handle the processing complexity automatically.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12537718B2Wireless communication apparatus for adaptive beamforming and operating method thereof

Publication Date: 2026.01.27 SAMSUNG ELECTRONICS CO LTD

AI summary

Provided is an operating method of a first apparatus communicating with a second apparatus in a wireless local area network (WLAN) system including the first apparatus and the second apparatus. The operating method of the first apparatus includes obtaining channel characteristic data with the second apparatus based on a null data packet (NDP) frame received from the second apparatus; generating beamforming feedback information and a feedback frame based on a class of a channel determined by applying a machine learning algorithm to the channel characteristic data; and transmitting the feedback frame to the second apparatus.