Transformer ML Model for Channel Impairment Estimation

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Solution Overview

Problem

Conventional techniques for diagnosing channel impairments in communication channels require significant upfront resources and have limitations in accuracy and robustness, particularly with higher frequency bandwidths, leading to inefficient diagnosis and maintenance of communication links.

Innovation Solution

A transformer-based machine learning model is employed to estimate channel impairments by processing channel frequency response data, utilizing a pre-processing component, transformer encoder neural network, and multiclass classifier to generate a multi-dimensional embedding and predict channel impairment classes, which can be trained using real-world and simulated data to improve accuracy and robustness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional techniques are used for diagnosing channel impairments, then resource investment is significant, but diagnosis efficiency and accuracy are limited

Engineering Contradiction:

Improvechannel impairment diagnosis accuracyVSAvoiddiagnosis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces conventional mechanical testing and measurement systems with an AI-based diagnostic system. The AI model processes channel frequency response data to identify impairments, substituting physical testing equipment and manual analysis with intelligent algorithms that operate virtually, thereby reducing both time and resource requirements while maintaining or improving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a virtual copy of the channel through frequency response measurements and uses this digital representation for AI-based analysis. Instead of physically testing and manipulating the actual channel, the system copies channel characteristics into data form that can be processed efficiently by AI algorithms, enabling rapid diagnosis without physical intervention.

Inventive Principle:

Principle #26Copying

2Reliability

If conventional techniques are used for channel impairment diagnosis, then upfront resource investment is significant, but reliability is limited

Engineering Contradiction:

Improvechannel impairment diagnosis reliabilityVSAvoidresource investment

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The AI-based system enables self-service diagnostics where the channel impairment identification is performed automatically without requiring extensive human expertise or manual testing procedures. The system serves itself by autonomously processing frequency response data and generating impairment diagnoses, reducing the need for specialized personnel and equipment while improving consistency and reliability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent transforms the diagnostic approach by changing from physical parameter measurements requiring expensive equipment to processing of frequency response parameters through AI algorithms. This parameter transformation allows the same diagnostic functionality to be achieved with computational resources rather than physical testing resources, improving reliability while reducing investment requirements.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If conventional techniques are used, then handling of corrupted data and high-frequency bandwidths is difficult, but the system becomes less robust

Engineering Contradiction:

Improverobustness to corrupted data and high-frequency bandwidthsVSAvoidchannel impairment classification accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The AI model is trained in advance on diverse datasets including corrupted data and high-frequency channel responses. This preliminary training equips the system with pre-learned patterns for handling various data quality issues and frequency ranges, enabling it to maintain high classification accuracy when encountering corrupted data or high-frequency bandwidths during actual operation without requiring additional processing complexity.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240031837A1Apparatus and method for channel impairment estimations using transformer-based machine learning models

Publication Date: 2024.01.25 NOKIA SOLUTIONS & NETWORKS OY

AI summary

An apparatus, method and computer program provide for obtaining channel response data including a channel frequency response of a channel over a frequency spectrum, wherein the channel frequency response is generated in response to a transmission over the channel or a simulation thereof; and generating an indication of channel impairments in response to applying the channel response data to a transformer-based machine-learning (ML) model trained to predict a channel impairment estimate.