DSS-o-SAGE: Direction-Scan Sounding-Oriented SAGE Algorithm for Channel Parameter Estimation in mmWave and THz Bands

TL;DR

This work tackles high-resolution MPC parameter estimation for mmWave/THz direction-scan sounding by addressing phase instability that arises across scanning directions. It introduces the DSS-o-SAGE algorithm, which models scanning-direction-dependent phases and uses a coarse-to-fine search with partial data to dramatically reduce computation while preserving accuracy. The method is validated through synthetic simulations and a real indoor-corridor measurement at 300 GHz, where it outperforms baseline noise-elimination and conventional SAGE variants, yielding more reliable MPC separation and channel characteristics. The findings show near-field effects are weak under narrow DSS beams, enabling effective far-field approximations, and demonstrate meaningful improvements in path loss, delay spread, and angular spreads essential for THz system design.

Abstract

Investigation of millimeter (mmWave) and Terahertz (THz) channels relies on channel measurements and estimation of multi-path component (MPC) parameters. As a common measurement technique in the mmWave and THz bands, direction-scan sounding (DSS) resolves angular information and increases the measurable distance. Through mechanical rotation, the DSS creates a virtual multi-antenna sounding system, which however incurs signal phase instability and large data sizes, which are not fully considered in existing estimation algorithms and thus make them ineffective. To tackle this research gap, in this paper, a DSS-oriented space-alternating generalized expectation-maximization (DSS-o-SAGE) algorithm is proposed for channel parameter estimation in mmWave and THz bands. To appropriately capture the measured data in mmWave and THz DSS, the phase instability is modeled by the scanning-direction-dependent signal phases. Furthermore, based on the signal model, the DSS-o-SAGE algorithm is developed, which not only addresses the problems brought by phase instability, but also achieves ultra-low computational complexity by exploiting the narrow antenna beam property of DSS. Simulations in synthetic channels are conducted to demonstrate the efficacy of the proposed algorithm and explore the applicable region of the far-field approximation in DSS-o-SAGE. Last but not least, the proposed DSS-o-SAGE algorithm is applied in real measurements in an indoor corridor scenario at 300~GHz. Compared with results using the baseline noise-elimination method, the channel is characterized more correctly and reasonably based on the DSS-o-SAGE.

Figures (10)

• Figure 1: Schematic diagram for mmWave and THz DSS.
• Figure 2: Picture of the conducted experiment.
• Figure 3: The rotator used for channel measurements.
• Figure 4: Signal phase in different scanning directions.
• Figure 5: The flowchart of the DSS-o-SAGE algorithm.