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Model-Based Approaches to Channel Charting

Amr Aly, Ender Ayanoglu

TL;DR

The paper presents four model-based channel-charting methods—ISQ, LR, MM, and RS—for direct UE coordinate estimation from CSI, with MUSIC-based AOA estimation and novel $\rho$ estimation, and compares them to PCA, SM, AE, plus JM and RS variants. It demonstrates substantial improvements in channel-chart continuity and trustworthiness over conventional dimensionality-reduction techniques, with MM delivering the strongest performance at the cost of higher complexity, and RS offering an RTL-friendly alternative. The analysis covers runtime, scalability, SNR robustness, and phase-noise effects, showing that the model-based approaches can operate in real time without extensive training. Overall, the work provides practical, high-performance, model-based channel-charting pathways suitable for real-time localization and handover support in modern wireless systems.

Abstract

We present new ways of producing a channel chart [1] employing model-based approaches. We estimate the angle of arrival theta and the distance rho between the base station and the user equipment by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon's method (SM), and autoencoder (AE) [1]. We show that ISQ, LR, and MM surpass PCA, SM, and AE in performance. We also compare our algorithm MM with an algorithm from the literature that uses the MUSIC algorithm jointly on theta and rho. We call this algorithm the JM algorithm. JM performs very slightly better than MM but at a substantial increase in complexity. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM and JM algorithms. Unlike MUSIC, RS does not employ eigenvalue and eigenvector analysis. Thus, it is more suitable for direct register transfer logic (RTL) implementation.

Model-Based Approaches to Channel Charting

TL;DR

The paper presents four model-based channel-charting methods—ISQ, LR, MM, and RS—for direct UE coordinate estimation from CSI, with MUSIC-based AOA estimation and novel estimation, and compares them to PCA, SM, AE, plus JM and RS variants. It demonstrates substantial improvements in channel-chart continuity and trustworthiness over conventional dimensionality-reduction techniques, with MM delivering the strongest performance at the cost of higher complexity, and RS offering an RTL-friendly alternative. The analysis covers runtime, scalability, SNR robustness, and phase-noise effects, showing that the model-based approaches can operate in real time without extensive training. Overall, the work provides practical, high-performance, model-based channel-charting pathways suitable for real-time localization and handover support in modern wireless systems.

Abstract

We present new ways of producing a channel chart [1] employing model-based approaches. We estimate the angle of arrival theta and the distance rho between the base station and the user equipment by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon's method (SM), and autoencoder (AE) [1]. We show that ISQ, LR, and MM surpass PCA, SM, and AE in performance. We also compare our algorithm MM with an algorithm from the literature that uses the MUSIC algorithm jointly on theta and rho. We call this algorithm the JM algorithm. JM performs very slightly better than MM but at a substantial increase in complexity. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM and JM algorithms. Unlike MUSIC, RS does not employ eigenvalue and eigenvector analysis. Thus, it is more suitable for direct register transfer logic (RTL) implementation.
Paper Structure (23 sections, 10 equations, 21 figures, 18 tables, 4 algorithms)

This paper contains 23 sections, 10 equations, 21 figures, 18 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Contributions of the Paper
  3. Channel Models
  4. Estimating the Coordinates ${\theta}$ and ${\rho}$
  5. Estimating $\theta$ Using MUSIC
  6. Estimating $\rho$
  7. Estimating $\rho$ Using ISQ
  8. Estimating $\rho$ Using LR
  9. Estimating $\rho$ Using MUSIC
  10. JM Algorithm
  11. RS Algorithm
  12. Simulation Environment
  13. Performance Comparison and Complexity Analysis
  14. Measures for Channel Charting: Continuity and Trustworthiness
  15. LR and ISQ Performance Comparison
  16. ...and 8 more sections

Figures (21)

  • Figure 1: Summary of channel charting via dimensionality reduction b1.
  • Figure 2: Angle of arrival $(\theta)$ relation with phase.
  • Figure 3: Correlation of real $\rho$ vs estimated $\rho$ under the channel model QNLOS.
  • Figure 4: Phase change across subcarriers with distance.
  • Figure 5: MUSIC algorithm to find $\theta$ and $\rho$. The combination is called the MM algorithm..
  • ...and 16 more figures