用于高效无线电传播建模的变换不变生成式射线路径采样

Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling

March 2, 2026

作者: Jérome Eertmans, Enrico M. Vitucci, Vittorio Degli-Esposti, Nicola Di Cicco, Laurent Jacques, Claude Oestges

cs.AI

摘要

射线追踪技术虽已成为精确无线电波传播建模的标准方法，但其计算复杂度呈指数级增长——候选路径数量随物体数量呈相互作用阶数的幂次增长。这一瓶颈限制了其在大规模或实时场景中的应用，迫使传统工具依赖启发式方法来减少候选路径数量，但可能牺牲精度。为突破此局限，我们提出一个综合性机器学习辅助框架，通过生成流网络以智能采样替代穷举式路径搜索。将此类生成模型应用于该领域面临重大挑战，尤其是有效路径稀缺导致的奖励稀疏问题，在复杂环境中评估高阶相互作用时易引发收敛失败和平凡解。为确保稳健学习与高效探索，本框架包含三大核心架构组件：首先采用经验回放缓冲区捕获并保留稀有有效路径；其次采用均匀探索策略以提升泛化能力，防止模型对简单几何结构过拟合；第三应用基于物理规则的动作掩码策略，在模型评估前过滤物理不可行路径。实验验证表明，所提模型在GPU上较穷举搜索加速高达10倍，CPU上加速达1000倍，同时保持高覆盖精度并能成功发现复杂传播路径。完整源代码、测试案例及教程详见https://github.com/jeertmans/sampling-paths。

English

Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of candidate paths scales with the number of objects raised to the power of the interaction order. This bottleneck limits its use in large-scale or real-time applications, forcing traditional tools to rely on heuristics to reduce the number of path candidates at the cost of potentially reduced accuracy. To overcome this limitation, we propose a comprehensive machine-learning-assisted framework that replaces exhaustive path searching with intelligent sampling via Generative Flow Networks. Applying such generative models to this domain presents significant challenges, particularly sparse rewards due to the rarity of valid paths, which can lead to convergence failures and trivial solutions when evaluating high-order interactions in complex environments. To ensure robust learning and efficient exploration, our framework incorporates three key architectural components. First, we implement an experience replay buffer to capture and retain rare valid paths. Second, we adopt a uniform exploratory policy to improve generalization and prevent the model from overfitting to simple geometries. Third, we apply a physics-based action masking strategy that filters out physically impossible paths before the model even considers them. As demonstrated in our experimental validation, the proposed model achieves substantial speedups over exhaustive search -- up to 10times faster on GPU and 1000times faster on CPU -- while maintaining high coverage accuracy and successfully uncovering complex propagation paths. The complete source code, tests, and tutorial are available at https://github.com/jeertmans/sampling-paths.