Invariant Shadow Fading Data for Efficient Radio Network Simulation
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Solution Overview
Problem
Current simulation techniques for generating radio test data in mobile radio environments are computationally intensive and lack accurate 3D models, failing to account for both fixed and transitory obstructions, leading to incorrect evaluation and training of optimization components in radio access networks.
Innovation Solution
A testing system utilizes real mobile radio data and network topology data to generate invariant shadow fading data through machine learning feature extraction, creating a realistic discoverable spatiotemporal signature for training a machine learning model to manage network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ray-tracing simulation technique is used to generate radio test data, then the data can account for fixed and transitory obstructions, but the computing resources and time consumption increase significantly
Solution Approach 1:
The system performs preliminary extraction of invariant shadow fading components from historical mobile radio data and pre-generates stochastic data representing fixed obstructions. This preliminary action creates a reusable spatiotemporal signature that can be applied during testing without requiring real-time ray-tracing simulations, thus reducing time consumption while maintaining accuracy.
Solution Approach 2:
Instead of performing computationally intensive ray-tracing simulations during actual testing, the system creates simplified copies or representations of the radio environment using extracted invariant shadow fading data and stochastic obstruction models. These copies replicate the essential characteristics of the physical environment enabling accurate testing without the computational burden of full-scale simulations.
2Measurement precision
If ray-tracing simulation technique is used to generate radio test data, then the data can account for fixed and transitory obstructions, but the computing resources increase significantly
Solution Approach 1:
The system performs preliminary extraction of invariant shadow fading components from historical mobile radio data and pre-generates stochastic data representing fixed obstructions. This preliminary action creates a reusable spatiotemporal signature that can be applied during testing without requiring real-time ray-tracing simulations, thus reducing computing resources while maintaining accuracy.
Solution Approach 2:
Instead of performing computationally intensive ray-tracing simulations during actual testing, the system creates simplified copies or representations of the radio environment using extracted invariant shadow fading data and stochastic obstruction models. These copies replicate the essential characteristics of the physical environment enabling accurate testing without the computational burden of full-scale simulations.
3Use of energy by moving object
If simplified simulation technique is used to generate radio test data, then the computing resources are conserved, but the data fails to account for obstructions accurately
Solution Approach 1:
The system enables the mobile radio data itself to provide the obstruction information needed for accurate simulation. By extracting invariant shadow fading components directly from historical mobile radio measurements, the system self-generates the necessary environmental data without requiring external detailed 3D models or computationally intensive ray-tracing, thus achieving both resource efficiency and accuracy.
4Measurement precision
If detailed 3D models of the mobile radio environment are created to improve simulation accuracy, then the measurement precision improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential invariant shadow fading components from historical mobile radio data, separating these invariant characteristics from the variable components. This extraction approach obtains the necessary obstruction information without requiring complete detailed 3D models of the entire environment, thus reducing system complexity while maintaining the accuracy needed for testing optimization components.
Data Source
AI summary
A device may receive real mobile radio data identifying measurements of radio transmissions of base stations and user devices of a mobile radio environment in a geographical area, and may receive network topology data associated with the geographical area. The device may utilize, based on the network topology data, a machine learning feature extraction approach to generate a representation of invariant aspects of spatiotemporal predictable components of the real mobile radio data, and may generate, based on the representation of invariant aspects, stochastic data that includes a probability that a radio signal will be obstructed. The device may utilize the stochastic data to identify a realistic discoverable spatiotemporal signature, and may train or evaluate a system to manage performance of a mobile radio network based on the realistic discoverable spatiotemporal signature.