Generative Channel Model for Adaptive Reference Signal Configuration

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Solution Overview

Problem

Existing channel estimation methods in wireless communication systems face inefficiencies due to delays in reconfiguring reference signals and DMRS configurations, particularly in scenarios with sparse or rapidly changing channel conditions, leading to suboptimal communication performance.

Innovation Solution

Employing a generative channel model (GCM) associated with a digital twin (DT) to predict channel conditions, enabling efficient configuration of downlink reference signals and DMRS configurations based on machine-learning algorithms, thereby optimizing channel estimation and reducing overhead.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If traditional channel estimation methods are used with periodic reference signals, then system compatibility is maintained, but delays occur in reconfiguring reference signals and DMRS configurations when channel conditions change rapidly

Engineering Contradiction:

Improvechannel estimation speedVSAvoidreconfiguration delay

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The system performs preliminary channel estimation using downlink reference signals before actual data transmission. The GCM is pre-configured with digital twin information to predict channel conditions, allowing the system to prepare channel estimates in advance and reduce reconfiguration delays when conditions change.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a digital twin copy of the wireless channel environment to simulate and predict channel conditions. This virtual copy allows the system to test and optimize channel estimation parameters without affecting real-time communication, enabling faster adaptation to changing conditions.

Inventive Principle:

Principle #26Copying

2Measurement precision

If reference signal overhead is increased to improve channel estimation accuracy in sparse channel conditions, then measurement precision improves, but signal overhead increases

Engineering Contradiction:

Improvechannel estimation accuracyVSAvoidreference signal overhead

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system dynamically changes parameters of downlink reference signals based on channel conditions. The GCM analyzes channel state information and adjusts reference signal density, frequency, and time resources accordingly, maintaining high estimation accuracy in sparse conditions while reducing overhead in stable conditions.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The reference signal configuration transitions from static to dynamic, where the GCM continuously monitors channel conditions and adapts the density and distribution of reference signals in real-time, optimizing the balance between measurement precision and overhead.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If machine learning algorithms are integrated into channel estimation to handle rapidly changing conditions, then adaptability improves, but device complexity increases

Engineering Contradiction:

Improveadaptation to channel conditionsVSAvoidcomputation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The GCM acts as an intermediary between the physical channel environment and the channel estimation algorithm. It processes raw channel state information and outputs optimized parameters for reference signal configuration, simplifying the overall system architecture while maintaining high adaptability to changing conditions.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If downlink reference signal configuration is optimized based on GCM predictions, then productivity improves through reduced overhead, but measurement precision may be compromised in rapidly changing conditions

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidchannel estimation accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements a feedback loop where channel estimation results are continuously monitored and fed back to the GCM. This feedback mechanism allows the system to detect when predictions diverge from actual conditions, triggering increased reference signal density to maintain measurement precision while preserving overall communication efficiency.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12574266B2Reference signal configuration using a generative channel model

Publication Date: 2026.03.10 QUALCOMM INC

AI summary

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a generative channel model (GCM) that outputs channel information pertaining to a digital twin (DT). The UE may receive a configuration of a downlink reference signal. The UE may perform channel estimation using the configuration of the downlink reference signal. The UE may transmit a report, wherein the report includes a precoding indicator, and wherein at least one of computation of the precoding indicator, or a channel estimation algorithm for the channel estimation, uses the GCM. Numerous other aspects are described.