AI 中文总结
研究针对数字孪生信道构建中传统光线追踪串行路径搜索复杂度过高的问题，提出DeepRT引擎，通过构建包围体层次结构、执行SBR算法及并行批处理IM求解器实现加速，在效率和准确性上优于其他工具。
AI 中文摘要
数字孪生信道（DTC）旨在建立物理无线信道的实时数字对应物，以再现和预测特定地点的传播特性。光线追踪（RT）作为一种用于现实传播场景的高精度信道计算方法，是DTC构建的关键推动因素。然而，传统RT在串行路径搜索工作流程下具有高复杂性。本文提出了DeepRT引擎（DeepRT-E），一种具有三阶段物理启发式流水线的并行RT加速架构，用于实时DTC构建。首先，DeepRT-E构建包围体层次结构（BVH）来划分场景并减少冗余光线-表面相交。其次，通过光线级并行追踪框架执行发射和反射光线（SBR）算法，以识别候选表面序列并修剪镜像法（IM）的搜索空间。最后，并行批处理IM求解器细化保留的候选者以进行精确的传播路径恢复。仿真结果表明，DeepRT-E将运行时间减少了96.3%，收敛误差仅为0.001dB，在效率和准确性方面优于Wireless InSite和Sionna。
英文摘要
Digital twin channel (DTC) aims to establish a real-time digital counterpart of physical wireless channels for reproducing and predicting site-specific propagation characteristics. As a high-precision channel computation method for realistic propagation scenarios, ray tracing (RT) serves as a key enabler for DTC construction. However, conventional RT suffers from high complexity under serial path-searching workflows. This letter proposes DeepRT Engine (DeepRT-E), a parallel RT acceleration architecture with a three-stage physically-inspired pipeline for real-time DTC construction. Firstly, DeepRT-E constructs a bounding volume hierarchy (BVH) to partition the scene and reduce redundant ray-surface intersections. Secondly, the shooting and bouncing rays (SBR) algorithm is executed through a ray-level parallel tracing framework to identify candidate surface sequences and prune the search space of the image method (IM). Finally, a parallel batched IM solver refines the retained candidates for accurate propagation-path recovery. Simulation results show that DeepRT-E reduces runtime by 96.3% and achieves a converged error of only 0.001 dB, outperforming Wireless InSite and Sionna in efficiency and accuracy.
