Radio-Aware Digital Twin for Wireless Resource Allocation
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Solution Overview
Problem
Current control channel signaling in wireless networks is inefficient, leading to high overhead and end-to-end latency due to frequent resource allocation and scheduling adjustments, especially in scenarios with high mobility and unpredictable traffic patterns.
Innovation Solution
Implementing a radio-aware digital twin that collects location information and trajectory data of user devices to generate optimized resource allocation parameter sets, reducing the need for frequent control signaling by predicting channel conditions and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If frequent resource allocation and scheduling adjustments are made in control channels, then system adaptability to high mobility and unpredictable traffic patterns is improved, but control channel overhead increases
Solution Approach 1:
The patent applies preliminary action by predicting future channel conditions and resource requirements in advance using machine learning models, allowing the system to pre-determine resource allocation parameters. This eliminates the need for frequent real-time control signaling adjustments, as the predicted parameters are already optimized for future conditions, thereby reducing control channel overhead while maintaining system adaptability.
Solution Approach 2:
The patent creates a virtual copy of the wireless communication environment through digital twin technology and machine learning models. This virtual model replicates channel conditions, user device behaviors, and network states, allowing the system to simulate and predict future scenarios without requiring actual real-time control signaling. The copy enables accurate predictions with minimal overhead.
2Adaptability or versatility
If frequent resource allocation and scheduling adjustments are made in control channels, then system adaptability to high mobility and unpredictable traffic patterns is improved, but end-to-end latency increases
Solution Approach 1:
The patent performs resource allocation predictions in advance using machine learning models that analyze historical and real-time data. By determining optimal resource allocation parameters before they are needed, the system eliminates the latency associated with frequent real-time control signaling exchanges. The predicted parameters are ready for immediate execution, reducing end-to-end latency while maintaining adaptability to mobility and traffic patterns.
Solution Approach 2:
The patent introduces machine learning models and digital twin technology as intermediaries between the physical wireless environment and the control signaling system. These intermediaries process and predict future states, translating complex real-time variations into pre-computed resource allocation parameters. This intermediary layer filters out the need for frequent direct control signaling, thereby reducing latency while preserving system adaptability.
3Reliability
If traditional control signaling methods are used, then reliability of control channel operations is maintained, but spectral efficiency deteriorates
Solution Approach 1:
The patent uses digital twin technology to create a virtual replica of the wireless communication system, including channel conditions, user device states, and network configurations. This virtual model allows the system to predict future resource allocation needs with high accuracy, enabling reliable control decisions to be made in advance. By relying on the accurate virtual copy rather than frequent real-time signaling, the system maintains control channel reliability while significantly improving spectral efficiency through reduced overhead.
Solution Approach 2:
The patent fundamentally changes the parameters of control signaling by transitioning from frequent real-time updates to periodic predictions based on machine learning models. The resource allocation parameters are determined based on predicted future conditions rather than current instantaneous states, changing the temporal and informational characteristics of control signaling. This parameter change maintains reliability through accurate predictions while improving spectral efficiency by reducing signaling frequency.
Data Source
AI summary
The apparatus may collect location information of at least one user device at at least one time instant over a time interval, for each of the at least one user device: obtain an estimate of at least one parameter relating to the collected location information, generate by using the obtained estimate of the at least one parameter, at least one resource allocation parameter set each comprising at least one resource allocation parameter, wherein each of the at least one resource allocation parameter set is associated with corresponding one of the at least one time instant, generate a resource allocation configuration comprising the at least one resource allocation parameter set, and transmit the resource allocation configuration to the at least one user device. Apparatuses, methods, and computer programs are disclosed.