Differentiable Radio Map Ray Tracing With Path Replay
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Solution Overview
Problem
Conventional automatic differentiation methods for ray tracing in digital twins face high computational complexity and memory consumption due to the need to store information for each ray bounce and path, limiting their scalability in simulating radio wave propagation.
Innovation Solution
The use of path replay backpropagation technique, represented using the Stokes-Müller formalism, where only loss, loss gradients, and path retracing information are stored, allowing gradients to be computed efficiently in a second forward pass, reducing memory requirements and improving scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional automatic differentiation is used to compute gradients for all ray paths, then accurate gradient computation is achieved, but memory consumption and computational complexity increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for gradient computation (loss values and path metadata) while discarding redundant intermediate ray tracing data. This selective extraction enables accurate gradient computation through replay backpropagation without storing complete ray path information, thereby reducing memory requirements and computational complexity.
Solution Approach 2:
The patent performs preliminary actions by storing only loss values and path metadata during the forward pass, then replaying these minimal stored elements during the backward pass to compute gradients. This preliminary selective storage approach enables accurate gradient computation without requiring extensive memory for complete ray path data.
2Measurement precision
If information for each ray intersection is stored for backpropagation, then correct gradients are computed, but memory consumption increases with the number of ray bounces and paths
Solution Approach 1:
The patent extracts only the essential loss values and path metadata needed for gradient computation, eliminating the need to store complete ray intersection information for every bounce and path. This selective extraction maintains gradient accuracy while dramatically reducing memory consumption.
Solution Approach 2:
The patent discards most intermediate ray tracing information during the forward pass, retaining only minimal loss values and path metadata. These discarded elements are recovered through replay backpropagation, where the minimal stored data is reused to compute gradients, thereby reducing memory requirements while maintaining computational accuracy.
3Manufacturing precision
If conventional backpropagation is used with full path information, then accurate scene property calibration is achieved, but the system does not scale with increased ray bounces and paths
Solution Approach 1:
The patent extracts only the essential loss values and path metadata required for calibration, eliminating the need to store complete ray path information. This enables accurate scene property calibration while providing scalability, as the system can handle increased ray bounces and paths without proportional increases in memory requirements.
Solution Approach 2:
The patent changes the parameters stored during the forward pass from complete ray path information to minimal loss values and path metadata. This parameter transformation enables the system to scale with increased ray bounces and paths while maintaining calibration accuracy through replay backpropagation.
Data Source
AI summary
Embodiments of the present disclosure relate to differentiation of ray tracing of radio maps. Systems and methods are disclosed for using path replay backpropagation to efficiently compute a radio map. In an embodiment, an electric field of a propagating wave and its interaction with the environment is represented using the Stokes-Müller formalism. Instead of storing information needed for conventional backpropagation during the forward pass, in an embodiment, only the loss, loss gradients, and optionally information needed to retrace the paths that contribute to the loss are stored because replay backpropagation propagates gradients in a second forward pass. The loss gradients from the first forward pass are used during the second forward pass when paths are replayed to accumulate the loss gradients with additional gradients resulting from interactions with scattering surfaces.