Alternative Channel Charting Techniques in Cellular Wireless Communications

TL;DR

This work evaluates conventional AoA estimators—Bartlett, MVDR, and Minimum Norm—in the context of channel charting and compares them with model-based distance estimators (ISQ, LR) and MUSIC-based approaches. It introduces a MUSIC-based framework for jointly estimating AoA and distance (MM) and analyzes two low-complexity alternatives (JM and RS) for charting, assessing TW/CT correlations and runtime. Key findings show that while MUSIC-based joint estimation delivers the best channel-chart fidelity, RS offers a practical, lower-complexity option, and that the conventional AoA methods can still be competitive under certain conditions. The results demonstrate robust charting performance across LOS and NLOS scenarios, with phase noise and SNR variations highlighting the practical trade-offs for real-time cellular deployments.

Abstract

We investigate the use of conventional angle of arrival (AoA) algorithms the Bartlett's algorithm, the Minimum Variance Distortion Response (MVDR or Capon) algorithm, and the Minimum Norm algorithm for estimating the AoA $θ$ together with our previously introduced algorithms linear regression (LR), inverse of the root sum squares of channel coefficients (ISQ), as well as a novel use of the MUSIC algorithm for estimating the distance from the base station, $ρ$ in the context of channel charting. We carry out evaluations in terms of the visual quality of the channel charts, the dimensionality reduction performance measures trustworthiness (TW) and connectivity (CT), as well as the execution time of the algorithms. We find that although the Bartlett's algorithm, MVDR, and Minimum Norm algorithms have sufficiently close performance to techniques we studied earlier, the Minimum Norm algorithm has significantly higher computational complexity than the other two. Previously, we found that the use of the MUSIC algorithm for estimation of both $θ$ and $ρ$ has a very high performance. In this paper, we investigated and quantified the performance of the Bartlett algorithm in its use for estimating both $θ$ and $ρ$, similar to the our previously introduced technique of using MUSIC for estimating both.

Figures (17)

• Figure 1: Summary of channel charting via dimensionality reduction b1.
• Figure 2: Angle of arrival $(\theta)$ relation with phase.
• Figure 3: Phase change across subcarriers with distance.
• Figure 4: 3D environment.
• Figure 5: 3D environment.