[论文解读] Enabling Large-Scale Channel Sounding for 6G: A Framework for Sparse Sampling and Multipath Component Extraction

Yi Chen, Li Ming+1 more|arXiv (Cornell University)|Feb 5, 2026

Advanced Wireless Communication Technologies被引用 0

一句话总结

论文提出了一种稀疏采样框架（Parabolic Frequency Sampling）和一个似然修正的SAGE（LR-SAGE）算法，以实现面向6G ISAC的大规模、MHz–THz 通道测量，显著提升数据量并缩短测量与处理时间。在280–300 GHz 的实验验证表明在保持MPC精度的同时实现了显著的加速和数据量减少。

ABSTRACT

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$ imes$ 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.

研究动机与目标

• Motivate the need for large-scale real-world ISAC channel datasets to advance 6G AI and sensing+communication integration.
• Address inefficiencies in traditional wideband VNA-based channel sounding that cause long measurement durations due to delay ambiguity.
• Develop a complete sparse channel sounding framework combining nonuniform frequency sampling with robust MPC extraction.

提出的方法

• Propose Parabolic Frequency Sampling (PFS) to non-uniformly distribute frequency points and eliminate delay ambiguity.
• Develop LR-SAGE, a likelihood-rectified SAGE algorithm that accounts for nonuniform sampling and molecular absorption in MPC extraction.
• Model the joint spatio-frequency channel with path-specific molecular-absorption gains and derive a single-path likelihood for delay estimation.
• Analyze uniform, coprime, and nested sampling schemes and quantify their unambiguous delay ranges and distortion under molecular absorption.
• Validate the framework with a THz channel sounding testbed at 280–300 GHz showing large-scale data acquisition and MPC extraction performance.

实验结果

研究问题

• RQ1 How does nonuniform frequency sampling affect delay ambiguity and MPC extractability in mmWave/THz channel sounding?
• RQ2 Can a likelihood-rectified SAGE approach accurately extract MPCs under nonuniform sampling and molecular absorption?
• RQ3 What is the achievable acceleration and data-volume reduction of sparse sampling versus traditional uniform sampling in THz channel sounding?
• RQ4 Does the proposed PFS/LR-SAGE framework reproduce MPCs comparable to exhaustive measurements?
• RQ5 How can the framework enable large-scale ISAC datasets for AI-native 6G systems?

主要发现

• PFS eliminates delay ambiguity and reduces sampling overhead by orders of magnitude compared to uniform sampling.
• LR-SAGE rectifies likelihood distortions from nonuniform sampling and molecular absorption, improving MPC extraction over standard SAGE or IDFT.
• THz experiments (280–300 GHz) demonstrate 50× faster measurement, 98% reduction in data volume, and 99.96% reduction in post-processing complexity while preserving MPCs and channel characteristics.
• The framework enables acquisition of channel datasets tens to hundreds of times larger within the same measurement duration.
• The approach supports building massive ISAC datasets required by AI-native 6G systems by accelerating data collection and reducing computational demands.