Digital Twin Beam Selection for Autonomous Vehicle mmWave Links
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Solution Overview
Problem
Autonomous vehicles face challenges in selecting optimal beam directions for millimeter wave communication when encountering new or altered environments, such as temporary obstructions, due to unpredictable wireless propagation conditions that cannot be fully characterized without new RF information, leading to erroneous deep learning predictions and increased latency in beam selection.
Innovation Solution
A system utilizing a network edge server to store a multiverse of digital twins representing different beam directions, allowing the autonomous vehicle to identify the strongest signal by comparing sensor data with stored data and selecting the appropriate twin for beam direction data, thereby correcting erroneous predictions and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If deep learning models are used to predict beam directions, then beam selection speed is improved, but prediction accuracy deteriorates in new or altered environments
Solution Approach 1:
The system creates digital twins (virtual copies) of the physical environment including base stations, obstructions, and propagation conditions. These digital twins are stored in a multiverse database and used to simulate and predict beam directions without requiring actual RF measurements, thereby maintaining speed while improving reliability in new environments.
Solution Approach 2:
The system pre-generates multiple digital twins representing different environmental conditions and beam configurations before actual beam selection is needed. When the vehicle encounters a new environment, the pre-computed digital twins can be quickly matched and used for accurate prediction without real-time computation delays.
2Reliability
If exhaustive beam sweeping is performed to ensure accurate beam selection, then beam selection accuracy is improved, but latency increases significantly
Solution Approach 1:
Instead of performing exhaustive physical beam sweeping, the system uses digital twins (virtual copies) to simulate and identify optimal beam directions. This virtual simulation provides accurate beam selection without the time penalty of physical sweeping through all possible beam directions.
Solution Approach 2:
The system replaces the mechanical beam sweeping process with a computational simulation using digital twins. The physical act of sweeping through all beam directions is substituted with virtual environment modeling and ray-tracing simulations, dramatically reducing latency while maintaining accuracy.
3Measurement precision
If new RF information is collected to characterize unpredictable propagation conditions, then environmental characterization accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system creates digital twins that copy and simulate RF propagation conditions without requiring actual RF measurements. By modeling the electromagnetic environment virtually using sensor data and ray-tracing, the system achieves accurate environmental characterization while avoiding the complexity of RF data collection and processing.
Solution Approach 2:
The system introduces digital twins as an intermediary between physical sensors and beam selection decisions. Instead of directly processing complex RF information, the sensors feed data into the digital twin model, which then provides simplified propagation condition predictions for beam selection.
Data Source
AI summary
Systems and methods are provided for selecting a beam direction of mm wave transmission for use by a vehicle operating within a network environment. The systems and methods include collecting data from a plurality of sensors on the vehicle to detect obstructions and other changes to an environment of the vehicle and of a base station that communicates with the vehicle. The systems and methods further include a library of digital twins, which are a virtual representation of the real-world environment, to identify beam directions of varying signal-to-noise ratios. The systems and methods further include determining from the data collected from the plurality of sensors a preferred digital twin and using the beam direction data corresponding to the digital twin to determine a beam direction for a transmitter of the vehicle to communicate with a receiver of the base station.