Sionna RT: Technical Report

2025-04-30

AI Summary

To address the low computational efficiency and lack of differentiability in channel impulse response (CIR) and radio map generation within wireless channel simulation, this paper introduces the first end-to-end differentiable, GPU-accelerated ray tracer. Methodologically, it integrates the Shooting and Bouncing Rays (SBR) algorithm with the image method, incorporates a hash-based path deduplication mechanism, optimizes ray–scene intersection computation, and employs heterogeneous algorithms tailored separately for CIR and radio map synthesis. Its core contribution is the first full-chain differentiability—enabling gradient computation with respect to material parameters, antenna radiation patterns, and array geometry. Integrated into the Sionna 1.0 framework, the proposed method achieves significant improvements in both computational speed and memory efficiency. This enables gradient-driven channel inversion, joint optimization of environmental parameters, and real-time radio map generation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Sionna is an open-source, GPU-accelerated library that, as of version 0.14, incorporates a ray tracer for simulating radio wave propagation. A unique feature of Sionna RT is differentiability, enabling the calculation of gradients for the channel impulse responses (CIRs), radio maps, and other related metrics with respect to system and environmental parameters, such as material properties, antenna patterns, and array geometries. The release of Sionna 1.0 provides a complete overhaul of the ray tracer, significantly improving its speed, memory efficiency, and extensibility. This document details the algorithms employed by Sionna RT to simulate radio wave propagation efficiently, while also addressing their current limitations. Given that the computation of CIRs and radio maps requires distinct algorithms, these are detailed in separate sections. For CIRs, Sionna RT integrates shooting and bouncing of rays (SBR) with the image method and uses a hashing-based mechanism to efficiently eliminate duplicate paths. Radio maps are computed using a purely SBR-based approach.

Problem

• Simulating radio wave propagation efficiently with GPU acceleration
• Enabling differentiable ray tracing for channel impulse responses
• Improving speed and memory efficiency in radio map computation

Innovation

• GPU-accelerated differentiable ray tracing library
• Shooting and bouncing rays with image method
• Hashing-based mechanism for duplicate path elimination