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Semi-Gridless Variational Bayes Channel Estimation in XL-MIMO: Near-Field Modeling and Inference

Van-Chung Luu, Toan-Van Nguyen, Nuria González-Prelcic, Duy H. N. Nguyen

TL;DR

This work reformulates the near-field channel model for both uniform linear arrays and uniform planar arrays into separate direction-of-arrival (DoAs) and distance components and develops a semi-gridless variational Bayesian algorithm with efficient update rules that enables accurate channel reconstruction.

Abstract

Extremely large antenna arrays and high-frequency operation are two key technologies that advance performance metrics such as higher data rates, lower latency, and wider coverage in sixth-generation communications. However, the adoption of these technologies fundamentally changes the characteristics of wavefronts, forcing communication systems to operate in the near-field region. The transition from planar far-field communications to spherical near-field propagation necessitates novel channel estimation algorithms to fully exploit the unique features of spherical wavefronts for advanced transceiver design. To this end, we propose a novel semi-gridless channel estimation approach based on a variational Bayesian (VB) inference framework. Specifically, we reformulate the near-field channel model for both uniform linear arrays and uniform planar arrays into separate direction-of-arrival (DoAs) and distance components. Building on these new representations, we employ a gridless approach for DoAs estimation using a von Mises distribution, and a coarse-to-fine grid search for distance estimation. We then develop a semi-gridless variational Bayesian (SG-VB) algorithm with efficient update rules that enables accurate channel reconstruction. Simulation results validate the effectiveness of the proposed SG-VB algorithm, demonstrating enhanced near-field channel reconstruction accuracy and superior estimation performance for both DoAs and distance components embedded in near-field channels.

Semi-Gridless Variational Bayes Channel Estimation in XL-MIMO: Near-Field Modeling and Inference

TL;DR

This work reformulates the near-field channel model for both uniform linear arrays and uniform planar arrays into separate direction-of-arrival (DoAs) and distance components and develops a semi-gridless variational Bayesian algorithm with efficient update rules that enables accurate channel reconstruction.

Abstract

Extremely large antenna arrays and high-frequency operation are two key technologies that advance performance metrics such as higher data rates, lower latency, and wider coverage in sixth-generation communications. However, the adoption of these technologies fundamentally changes the characteristics of wavefronts, forcing communication systems to operate in the near-field region. The transition from planar far-field communications to spherical near-field propagation necessitates novel channel estimation algorithms to fully exploit the unique features of spherical wavefronts for advanced transceiver design. To this end, we propose a novel semi-gridless channel estimation approach based on a variational Bayesian (VB) inference framework. Specifically, we reformulate the near-field channel model for both uniform linear arrays and uniform planar arrays into separate direction-of-arrival (DoAs) and distance components. Building on these new representations, we employ a gridless approach for DoAs estimation using a von Mises distribution, and a coarse-to-fine grid search for distance estimation. We then develop a semi-gridless variational Bayesian (SG-VB) algorithm with efficient update rules that enables accurate channel reconstruction. Simulation results validate the effectiveness of the proposed SG-VB algorithm, demonstrating enhanced near-field channel reconstruction accuracy and superior estimation performance for both DoAs and distance components embedded in near-field channels.
Paper Structure (15 sections, 57 equations, 11 figures, 3 tables, 2 algorithms)

This paper contains 15 sections, 57 equations, 11 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Channel Modeling
  3. System Model
  4. ULA Near-Field Channel Modeling
  5. UPA Near-Field Channel Modeling
  6. Variational Inference of Near-Field Parameters
  7. Background on Variational Bayes Inference
  8. Proposed SG-VB for ULA Channel Estimation
  9. Proposed SG-VB for UPA Channel Estimation
  10. Computational Complexity Analysis
  11. Simulation Results
  12. ULA Channel Estimation
  13. UPA Channel Estimation
  14. Conclusions
  15. Some Results on the von Mises Distribution

Figures (11)

  • Figure 1: Illustration of the considered near-field system.
  • Figure 2: NMSE vs. SNR for different grid size.
  • Figure 3: NMSE vs. SNR when $L=6$ and $N=256$.
  • Figure 4: NMSE vs. Distance for ULA with $L=6$, SNR = 20 dB, and $N=256$.
  • Figure 5: MSE of angle vs. SNR for ULA with $L=6$, $N=256$.
  • ...and 6 more figures

Theorems & Definitions (1)

  • Remark 1