为6G实现大规模信道探测：一种用于稀疏采样和多径分量提取的框架
Enabling Large-Scale Channel Sounding for 6G: A Framework for Sparse Sampling and Multipath Component Extraction
AI总结：
本文提出了一种用于6G的信道探测框架，通过稀疏非均匀采样和改进的LR-SAGE算法，实现大规模信道数据采集与多径分量提取。
摘要：
实现6G愿景中人工智能（AI）和集成感知与通信（ISAC）的关键在于获取大规模真实世界信道数据集用于信道建模和数据驱动的AI模型。然而，传统频域信道探测方法由于需要避免时延模糊的大量频点而效率低下。本文提出了一种新颖的信道探测框架，包含稀疏非均匀采样以及一种修正似然的空间交替广义期望最大化（LR-SAGE）算法用于多径分量提取。该框架能够在相同信道测量时间内获取数十甚至上百倍更大的信道数据集，从而提供大规模数据以充分利用AI扩展定律。具体而言，我们提出了一种抛物线频率采样（PFS）策略，非均匀分布频点，有效消除时延模糊并减少采样开销。为了高效地从PFS测量的信道数据中提取多径分量（MPCs），我们开发了LR-SAGE算法，修正非均匀采样和分子吸收效应导致的似然扭曲。仿真结果和280-300GHz的实验验证表明，所提出的PFS和LR-SAGE算法不仅实现了50倍更快的测量速度、数据量减少98%以及后处理计算复杂度减少99.96%，还成功捕捉到与传统穷举测量一致的MPCs和信道特性，证明了其作为构建AI原生6G系统所需大规模ISAC数据集基础要素的潜力。
英文摘要：
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.
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