RF Transceiver Calibration via AI Model Transfer

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Solution Overview

Problem

Existing technologies face challenges in efficiently calibrating radio frequency (RF) frontends in base stations, particularly due to variations in RF hardware and channel conditions, which limits their ability to generalize and maintain optimal performance across different environments.

Innovation Solution

The method involves transferring a core AI model trained on data from a first RF transceiver to a second RF transceiver, followed by applying transfer learning using data from the second transceiver to adapt the model. This process includes identifying the number of layers and neurons in the model, as well as updating interconnections and coefficients, to calibrate the second RF transceiver.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional calibration methods are used for each RF transceiver, then calibration accuracy can be maintained, but calibration time and computational resources increase significantly

Engineering Contradiction:

Improvecalibration accuracyVSAvoidcalibration time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary calibration using a reference RF transceiver to generate a core model that captures universal calibration characteristics. This pre-trained model is then transferred to new transceivers, eliminating the need for complete recalibration and significantly reducing calibration time while maintaining accuracy through transfer learning adaptation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The calibration model trained on a reference transceiver is copied and transferred to new RF transceivers. Instead of creating new calibration models for each transceiver, the system replicates the core model structure and adapts it through transfer learning, reducing computational resources and time required for calibration.

Inventive Principle:

Principle #26Copying

2Measurement precision

If complete model training is performed for each new RF transceiver, then model accuracy can be optimized, but computational costs and training time increase

Engineering Contradiction:

Improvemodel accuracyVSAvoidcomputational cost

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

Instead of performing complete model training for each new transceiver, the system applies partial training through transfer learning. The pre-trained core model provides a strong foundation, and only minor adaptations are needed for new transceivers, significantly reducing computational costs while maintaining model accuracy.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The core model is preliminarily trained on a reference transceiver before deployment. This pre-training establishes a foundation that can be transferred to multiple new transceivers, avoiding the need to perform complete training from scratch for each device and reducing overall computational requirements.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If manual calibration intervention is used, then calibration precision can be maintained, but human error and operational complexity increase

Engineering Contradiction:

Improvecalibration precisionVSAvoidoperational complexity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs automated calibration using the transferred core model and transfer learning, eliminating the need for manual calibration intervention. The automated process maintains precision while reducing operational complexity and the risk of human error by allowing the system to self-calibrate new transceivers.

Inventive Principle:

Principle #25Self-service

4Ease of operation

If RF transceivers are calibrated independently without knowledge transfer, then calibration simplicity is maintained, but calibration efficiency and performance decrease

Engineering Contradiction:

Improvecalibration simplicityVSAvoidcalibration efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The calibration approach creates a universal core model that can be applied across multiple RF transceiver types and configurations. This single model serves multiple functions by being transferred and adapted to different transceivers, improving calibration efficiency and productivity while maintaining simplicity through the standardized transfer learning process.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250047318A1Using Prior Knowledge to Calibrate a Radio Frequency Frontend

Publication Date: 2025.02.06 DELL PROD LP

AI summary

A method can comprise, as part of initially deploying a second radio frequency (RF) transceiver, transferring, by a system, a core model to the second RF transceiver, the core model having been trained based on a training process comprising training an artificial intelligence model for a first RF transceiver, based on first data that is measured for the first RF transceiver. The method can further comprise applying, by the system, transfer learning on the core model at the second RF transceiver based on second data that is measured for the second RF transceiver, to produce a trained model. The method can further comprise calibrating, by the system, the second RF transceiver based on an output of the trained model to produce a calibrated second RF transceiver. The method can further comprise transmitting, by the system, RF information via the calibrated second RF transceiver.