标题： LoRaFlow：使用校正流的高质量信号重建 显示英文标题

标题： LoRaFlow: High-Quality Signal Reconstruction using Rectified Flow

摘要： LoRa技术对于低功耗广域网络至关重要，但在极低信噪比（SNR）下性能会显著下降。 我们提出了LoRaFlow，这是一种使用修正流的新方法，在具有挑战性的噪声条件下重建高质量的LoRa信号。 与现有的专注于分类的神经增强方法不同，LoRaFlow恢复信号本身，同时保持与标准去色散算法的兼容性。 我们的方法结合了混合神经网络架构、合成数据生成和鲁棒增强策略。 这种对LoRa基础设施的微创增强可能在不彻底改造现有系统的情况下扩展其操作范围和可靠性。 LoRaFlow为恶劣环境中的稳健物联网通信打开了新的可能性，其核心方法可以推广以支持各种通信技术。 显示英文摘要

摘要： LoRa technology, crucial for low-power wide-area networks, faces significant performance degradation at extremely low signal-to-noise ratios (SNRs). We present LoRaFlow, a novel approach using rectified flow to reconstruct high-quality LoRa signals in challenging noise conditions. Unlike existing neural-enhanced methods focused on classification, LoRaFlow recovers the signal itself, maintaining compatibility with standard dechirp algorithms. Our method combines a hybrid neural network architecture, synthetic data generation, and robust augmentation strategies. This minimally invasive enhancement to LoRa infrastructure potentially extends operational range and reliability without overhauling existing systems. LoRaFlow opens new possibilities for robust IoT communications in harsh environments and its core methodology can be generalized to support various communication technologies.