Generative Channel Transformation for Wireless Propagation
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Solution Overview
Problem
Conventional wireless channel models suffer from a 'sim-to-real gap' due to lack of physical consistency and site-specificity, leading to discrepancies between simulated and actual channel measurements.
Innovation Solution
A processor-implemented method using machine learning models to transform simulated channel information into more accurate 'real' channel estimates, combining ray tracing simulations with real measurement data to learn a transformation that bridges the simulation gap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ray tracing simulations are used to generate channel models, then physical consistency and site-specificity are improved, but the simulation-to-reality gap increases due to deterministic nature and modeling deficiencies
Solution Approach 1:
A neural network transformation model is introduced as an intermediary between the deterministic ray tracing simulator and the real-world channel measurements. This mediator learns the mapping between simulated and real channel characteristics, transforming the deterministic simulation outputs into stochastic channel models that better match real measurements, thereby reducing the simulation-to-reality gap while maintaining physical consistency
Solution Approach 2:
The patent transforms the deterministic channel parameters from ray tracing simulations into stochastic channel models by learning appropriate parameter transformations through neural networks. This changes the nature of the output from fixed deterministic values to probabilistic distributions that capture real-world variability, bridging the gap between simulation and reality
2Measurement precision
If stochastic channel models are used, then measurement precision is improved, but physical consistency and site-specificity deteriorate
Solution Approach 1:
The system performs preliminary ray tracing simulations to generate deterministic channel models, which are then processed through a neural network transformation model. This preliminary action provides a structured, physically-consistent foundation that is subsequently transformed into stochastic models, ensuring both physical consistency and measurement precision are achieved
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved wireless channel modeling. A set of simulated channel information for a wireless signal propagating in a simulated physical space is generated, and a set of latent tensors is generated based on the set of simulated channel information using a transformation machine learning model. A channel estimate is generated based on the set of latent tensors using a decoder machine learning model. One or more actions are taken based on the channel estimate.