发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ALF潜空间流模型，利用频谱图锚定生成无线信号，实现高保真长程一致生成，TSTR准确率84.5%，内存减少1.9倍，计算加速100倍。
AI 中文摘要
无线时间序列生成对于新兴应用非常有用，这些应用将推动机器学习任务在下一代网络中的采用和集成，例如共享频谱频段中的波形分类以及通过集成感知与通信对物理世界的解读。与通用时间序列不同，无线信号具有时频二重性，其中频域带宽约束隐式地对时域序列的演化施加了潜在的结构约束。在本文中，我们开发了锚定潜流（ALF），一种用于标签条件信号生成的潜空间流模型，该模型利用了这一二重性。我们的流模型通过频谱图进行“推理”，频谱图是信号的二维表示，通过对连续重叠的信号段应用加窗快速傅里叶变换，并将其频域功率分量映射到时间上而构建。具体地，我们通过一个训练时的辅助目标来塑造流模型的潜空间，该目标由（潜）频谱图监督（即通过频谱图进行锚定）。通过引导流场通过频谱图进行推理，生成的序列在时间上保持长程一致性，并显著提高了推理时的信号质量。实验上，ALF在各种信号长度、波形类别、信噪比（SNR）和信道条件下展示了最先进的生成质量和重建误差，在合成数据上训练、真实数据上测试（TSTR）的准确率达到84.5%，与现有模型相比平均内存减少1.9倍，平均计算速度提升100倍。我们的设计还支持下游任务，包括风格迁移和数据增强，并对无线社区具有广泛的实用性。
英文摘要
Wireless time-series generation is useful for emerging applications that will drive the adoption and integration of machine learning tasks within next-generation networks, such as waveform classification in shared spectrum bands and interpretation of the physical world through integrated sensing and communications. Unlike a generic time series, wireless signals have time-frequency duality, where a frequency domain bandwidth constraint implicitly imposes latent structural constraints on the evolution of the time-domain sequence. In this paper, we develop Anchored Latent Flow (ALF), a latent space flow model for label-conditioned signal generation that exploits this duality. Our flow model "reasons'" through a spectrogram, which is a 2-D representation of the signal constructed by applying a windowed Fast Fourier Transform to successive, overlapping signal segments and mapping their frequency-domain power components across time. Specifically, we shape the latent space of the flow model through a training-time auxiliary objective that is supervised by the (latent) spectrogram (i.e., anchoring through the spectrogram). By guiding the flow field to reason through a spectrogram, the generated sequence maintains long-range coherence across time and greatly improves the signal quality at inference. Empirically, ALF exhibits state-of-the-art generation quality and reconstruction error across various signal lengths, waveform classes, signal-to-noise ratio (SNR), and channel conditions, reaching a train on synthetic, test on real (TSTR) accuracy of 84.5%, a 1.9X average memory reduction, and 100X average compute speedup over existing models. Our design further enables downstream tasks, including style transfer and data augmentation, and has broad utility for the wireless community.
