Realizing the 6G vision of artificial intelligence (AI) and integrated sensing and communication (ISAC) critically requires large-scale real-world channel datasets for channel modeling and data-driven AI models. However, traditional frequency-domain channel sounding methods suffer from low efficiency due to a prohibitive number of frequency points to avoid delay ambiguity. This paper proposes a novel channel sounding framework involving sparse nonuniform sampling along with a likelihood-rectified space-alternating generalized expectation-maximization (LR-SAGE) algorithm for multipath component extraction. This framework enables the acquisition of channel datasets that are tens or even hundreds of times larger within the same channel measurement duration, thereby providing the massive data required to harness the full potential of AI scaling laws. Specifically, we propose a Parabolic Frequency Sampling (PFS) strategy that non-uniformly distributes frequency points, effectively eliminating delay ambiguity while reducing sampling overhead by orders of magnitude. To efficiently extract multipath components (MPCs) from the channel data measured by PFS, we develop a LR-SAGE algorithm, rectifying the likelihood distortion caused by nonuniform sampling and molecular absorption effect. Simulation results and experimental validation at 280--300~GHz confirm that the proposed PFS and LR-SAGE algorithm not only achieve 50$\times$ faster measurement, a 98\% reduction in data volume and a 99.96\% reduction in post-processing computational complexity, but also successfully captures MPCs and channel characteristics consistent with traditional exhaustive measurements, demonstrating its potential as a fundamental enabler for constructing the massive ISAC datasets required by AI-native 6G systems.

翻译：实现6G人工智能（AI）与通感一体化（ISAC）愿景，亟需大规模真实世界信道数据集以支持信道建模与数据驱动的AI模型。然而，传统频域信道探测方法因需避免时延模糊而要求海量频点，导致测量效率低下。本文提出一种新型信道探测框架，结合稀疏非均匀采样与用于多径分量提取的似然校正空间交替广义期望最大化（LR-SAGE）算法。该框架可在相同信道测量时长内获取规模扩大数十甚至数百倍的信道数据集，从而为充分发挥AI扩展定律的潜力提供所需的海量数据。具体而言，我们提出一种抛物线型频率采样（PFS）策略，通过非均匀分布频点，在有效消除时延模糊的同时将采样开销降低数个数量级。为从PFS测量的信道数据中高效提取多径分量（MPCs），我们开发了LR-SAGE算法，校正由非均匀采样与分子吸收效应引起的似然失真。在280-300 GHz频段的仿真结果与实验验证表明，所提出的PFS与LR-SAGE算法不仅实现了50倍测量加速、98%的数据量缩减和99.96%的后处理计算复杂度降低，还能准确捕获与传统穷举测量一致的多径分量与信道特性，证明了其作为构建AI原生6G系统所需海量ISAC数据集的关键使能技术的潜力。