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Sparse Estimation for XL-MIMO with Unified LoS/NLoS Representation

Xu Shi, Xuehan Wang, Jingbo Tan, Jintao Wang

Abstract

Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimension beyond conventional beamspace. In this paper, we first present one concise closed-form channel formulation for extremely large-scale multiple-input multiple-output (XL-MIMO). All line-of-sight (LoS) and non-line-of-sight (NLoS) paths, far-field and near-field scenarios, and XL-MIMO and XL-MISO channels are unified under the framework, where additional Vandermonde windowing matrix is exclusively considered for LoS path. Under this framework, we further propose one low-complexity unified LoS/NLoS orthogonal matching pursuit (XL-UOMP) algorithm for XL-MIMO channel estimation. The simulation results demonstrate the superiority of the proposed algorithm on both estimation accuracy and pilot consumption.

Sparse Estimation for XL-MIMO with Unified LoS/NLoS Representation

Abstract

Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimension beyond conventional beamspace. In this paper, we first present one concise closed-form channel formulation for extremely large-scale multiple-input multiple-output (XL-MIMO). All line-of-sight (LoS) and non-line-of-sight (NLoS) paths, far-field and near-field scenarios, and XL-MIMO and XL-MISO channels are unified under the framework, where additional Vandermonde windowing matrix is exclusively considered for LoS path. Under this framework, we further propose one low-complexity unified LoS/NLoS orthogonal matching pursuit (XL-UOMP) algorithm for XL-MIMO channel estimation. The simulation results demonstrate the superiority of the proposed algorithm on both estimation accuracy and pilot consumption.
Paper Structure (6 sections, 1 theorem, 17 equations, 3 figures, 1 algorithm)

This paper contains 6 sections, 1 theorem, 17 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. LoS/NLoS Channel Approximation and Analysis
  4. Unified LoS/NLoS Sparse Estimation
  5. Simulation Results
  6. Conclusion

Key Result

Theorem 1

The LoS channel $\bm H^\text{L}$ contains full rank $\min \{N_\text{T},N_\text{R}\}$ mathematically, but the number of available pipelines here can be approximated as $\omega_0N_\text{T}N_\text{R}$.

Figures (3)

  • Figure 1: Block diagram of near-field XL-MIMO system model.
  • Figure 2: Model loss for the second-order Taylor expansion-based generalized-polar-domain approximation.
  • Figure 3: NMSE against SNR and pilot length: (a) with pilot length fixed as 25; (b) with SNR fixed as $20 \text{dB}$.

Theorems & Definitions (1)

  • Theorem 1