Digital Twin Positioning Model Updates for Sparse UE Location Data
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately determining the location of user equipment (UEs) due to limited data density and spatial coverage, which affects the performance of positioning technologies, particularly in environments with complex structures.
Innovation Solution
A digital twin model is trained using a combination of channel measurements from real-world UEs and a digital twin representation of the environment, enabling improved location determination by updating and applying this model based on location information from UEs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional field data collection methods are used for training positioning models, then real-world measurement data can be obtained, but the data density and spatial coverage are limited
Solution Approach 1:
The patent creates a digital twin representation of the physical environment that copies and virtualizes the spatial structure, building materials, and geometric characteristics. This digital copy enables unlimited data generation without requiring additional physical field collection, thereby increasing data density while avoiding the complexity of expanding physical data collection operations.
Solution Approach 2:
The digital twin model is pre-trained using a combination of real field measurements and synthesized data from the digital twin representation before actual positioning operations occur. This preliminary training allows the model to learn from high-density data in advance, improving positioning accuracy without requiring dense real-time field data collection during deployment.
2Measurement precision
If more field data collection is performed to improve positioning accuracy, then positioning performance improves, but the spatial coverage and time required for data collection increase
Solution Approach 1:
The digital twin creates a virtual copy of the environment that can be queried and measured instantaneously without requiring physical travel or time-consuming field surveys. The digital twin representation stores spatial and geometric data that can be retrieved on-demand, providing unlimited spatial coverage without the time loss associated with expanding physical data collection operations.
Solution Approach 2:
Positioning data is collected and processed in advance during the digital twin creation phase, before actual positioning services are deployed. This preliminary data preparation eliminates the need for time-consuming field data collection during operational phases, as the model is already trained on comprehensive data from the digital twin representation.
3Adaptability or versatility
If digital twin model is updated with location information from UEs, then model generalization and spatial coverage improve, but computing resources and processing complexity increase
Solution Approach 1:
The system receives location information feedback from UEs and uses this feedback to update and refine the digital twin model. This feedback mechanism allows the model to adapt to real-world conditions and improve its generalization capability. The feedback loop enables continuous improvement of positioning accuracy while maintaining manageable processing complexity through targeted model updates rather than complete retraining.
Solution Approach 2:
The digital twin model transitions from a static representation to a dynamic, continuously evolving model that adapts to changing environmental conditions and UE locations. The model dynamically updates its parameters based on incoming location data, improving generalization without requiring complete model regeneration. This dynamic approach manages processing complexity by updating only necessary model parameters rather than reprocessing the entire dataset.
Data Source
AI summary
Disclosed are techniques for communication. In an aspect, a network node obtains a measurement report for a user equipment (UE), the measurement report including at least one or more channel measurements obtained by the UE of one or more reference signals. The network node may then apply a digital twin model to the one or more channel measurements to determine a location of the UE, wherein the digital twin model is trained based on (1) a first dataset of channel measurements obtained by a set of UEs and (2) a second dataset of channel measurements modeled by a digital twin representation of an environment in which the set of UEs is located.