ML-Based Wireless Communication Resource Correlation

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Solution Overview

Problem

There is a lack of established techniques for effectively leveraging machine learning processing in mobile communication systems.

Innovation Solution

The proposed solution involves a communication method and apparatus that apply machine learning technology to wireless communication between user equipment and a base station. This is achieved by receiving radio signals via multiple communication resources, communicating information about correlated resources, and performing machine learning processing using these combinations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If machine learning processing is applied to wireless communication, then communication efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning processing is segmented into specific communication tasks such as channel state information feedback and reference signal processing. The communication apparatus performs ML processing selectively on particular signal processing functions rather than implementing a complete ML system across all communication operations, thereby improving communication efficiency while limiting the increase in overall system complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary information structure that indicates combinations of correlated communication resources. This intermediary representation serves as a bridge between the raw communication signals and the machine learning processing, enabling efficient ML-based analysis without requiring the full complexity of processing all individual communication resources separately.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If machine learning processing is applied to wireless communication, then accuracy of channel state information feedback is improved, but processing overhead increases

Engineering Contradiction:

Improveaccuracy of channel state information feedbackVSAvoidprocessing overhead

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent merges multiple correlated communication resources into combined representations that capture the essential information for channel state feedback. By processing combinations of resources rather than individual resources separately, the system achieves improved measurement precision while reducing the total number of processing operations required, thus lowering processing overhead.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The intermediary information indicating resource combinations serves as a simplified copy or representation of the full communication resource set. This copied structure enables machine learning processing to analyze channel state information with high accuracy without requiring access to or processing of every individual communication resource, thereby reducing processing overhead while maintaining precision.

Inventive Principle:

Principle #26Copying

3Productivity

If machine learning processing is applied to wireless communication, then communication efficiency is enhanced, but power consumption increases

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidpower consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent segments machine learning processing to apply only to specific communication tasks such as channel state information feedback and reference signal processing, rather than implementing ML across all communication functions. This selective application improves communication efficiency in critical paths while limiting the overall power consumption increase to only the necessary processing components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extracts and processes only the essential correlated information from communication resources through the intermediary structure, rather than performing ML processing on complete raw data sets. This extraction approach achieves improved communication efficiency while minimizing power consumption by processing only the necessary subset of information.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250168663A1Communication method and communication apparatus

Publication Date: 2025.05.22 KYOCERA CORP

AI summary

A communication method for applying a machine learning technology to wireless communication between a user equipment and a network node in a mobile communication system includes: receiving, by one communication apparatus among the user equipment and the network node, a radio signal transmitted via each of a plurality of communication resources of the other communication apparatus among the user equipment and the network node; communicating, by the one communication apparatus to the other communication apparatus, information indicating a combination of communication resources having a predetermined correlation among the plurality of communication resources; and performing machine learning processing using the combination.