Enabling Large-Scale Channel Sounding for 6G: A Framework for Sparse Sampling and Multipath Component Extraction

2026-02-05

AI Summary

This work addresses the inefficiency of conventional frequency-domain channel sounding, which requires dense frequency sampling to avoid time-delay ambiguity and thus struggles to meet the demand for massive real-world channel data in 6G AI-native systems. To overcome this limitation, the paper proposes a novel, highly efficient channel sounding framework that introduces a parabolic frequency sampling (PFS) strategy to eliminate delay ambiguity. Furthermore, it designs a likelihood-corrected spatial alternating generalized expectation-maximization (LR-SAGE) algorithm to effectively mitigate likelihood distortions caused by non-uniform sampling and molecular absorption. Experimental validation in the 280–300 GHz band demonstrates that the proposed method achieves a 50× acceleration, reduces data volume by 98%, and lowers post-processing complexity by 99.96%, while accurately reproducing the channel characteristics obtained by traditional approaches.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Efficient ML / Green AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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$\times$ 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.

Problem

• channel sounding
• 6G
• delay ambiguity
• large-scale dataset
• multipath component extraction

Innovation

• sparse sampling
• multipath component extraction
• nonuniform frequency sampling
• LR-SAGE algorithm
• 6G channel sounding