Cross-Field Channel Parameter Estimation and Channel Characterization at THz Bands in Indoor Scenarios
arXiv:2607.13535
The paper presents indoor terahertz (260‑380 GHz) channel measurements with a virtual uniform linear array and introduces a cross‑field SAGE algorithm that jointly estimates near‑field and far‑field multipath components using Bayesian phase‑curvature classification and visibility region tracking.
Abstract
The terahertz (THz) frequency band offers the potential for ultra-high data rate transmission in future wireless communication systems. To extend the transmission distance and enhance spectral efficiency, the deployment of large-scale antenna arrays emerges as a promising solution in the THz band. This paper targets the critical challenge of cross-field (hybrid near-field/far-field) channel parameter estimation and channel characterization in such configurations. We first establish a 260-380 GHz virtual uniform linear array (ULA) measurement framework in an indoor scenario, capturing high-resolution channel transfer functions (CTFs) that reveal spatial non-stationarity and cross-field wavefront characteristics. Building upon these empirical observations, we propose a cross-field space-alternating generalized expectation-maximization (SAGE) algorithm that discriminatively estimates near-field and far-field multipath components (MPCs) via Bayesian phase-curvature classification, while explicitly tracking spatial birth-death phenomena through visibility region estimation. Analysis of the measurement data validates the algorithm's effectiveness in resolving cross-field MPCs and quantifies that near-field MPCs account for over 90% of total MPCs at 2 m transmission distance (380 GHz). We observe that spatial non-stationarity intensifies as the carrier frequency increases and the transmission distance decreases. These findings offer quantitative guidelines for channel modeling and system design in wireless THz communication systems.
13 pages, 19 figures