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Near-Field Channel Modeling for Electromagnetic Information Theory

Zhongzhichao Wan, Jieao Zhu, Linglong Dai

TL;DR

This paper develops a near-field channel model for electromagnetic information theory by treating the scattering channel as a non-stationary Gaussian random field and deriving an analytical correlation function based on EM scattering theory. It analyzes how scatterer size and geometry affect the DoF and validates the model against CDL and ray-tracing statistics, showing good fit with few parameters. A near-field channel estimation scheme leveraging the EM prior model is proposed, yielding significant performance gains over LS, OMP, and subspace-based methods in simulations. The work provides a physically grounded framework for understanding near-field EIT channels and offers a practical estimator that incorporates electromagnetic priors to improve estimation accuracy.

Abstract

Electromagnetic information theory (EIT) is one of the emerging topics for 6G communication due to its potential to reveal the performance limit of wireless communication systems. For EIT, the research foundation is reasonable and accurate channel modeling. Existing channel modeling works for EIT in non-line-of-sight (NLoS) scenario focus on far-field modeling, which can not accurately capture the characteristics of the channel in near-field. In this paper, we propose the near-field channel model for EIT based on electromagnetic scattering theory. We model the channel by using non-stationary Gaussian random fields and derive the analytical expression of the correlation function of the fields. Furthermore, we analyze the characteristics of the proposed channel model, e.g., channel degrees of freedom (DoF). Finally, we design a channel estimation scheme for near-field scenario by integrating the electromagnetic prior information of the proposed model. Numerical analysis verifies the correctness of the proposed scheme and shows that it can outperform existing schemes like least square (LS) and orthogonal matching pursuit (OMP).

Near-Field Channel Modeling for Electromagnetic Information Theory

TL;DR

This paper develops a near-field channel model for electromagnetic information theory by treating the scattering channel as a non-stationary Gaussian random field and deriving an analytical correlation function based on EM scattering theory. It analyzes how scatterer size and geometry affect the DoF and validates the model against CDL and ray-tracing statistics, showing good fit with few parameters. A near-field channel estimation scheme leveraging the EM prior model is proposed, yielding significant performance gains over LS, OMP, and subspace-based methods in simulations. The work provides a physically grounded framework for understanding near-field EIT channels and offers a practical estimator that incorporates electromagnetic priors to improve estimation accuracy.

Abstract

Electromagnetic information theory (EIT) is one of the emerging topics for 6G communication due to its potential to reveal the performance limit of wireless communication systems. For EIT, the research foundation is reasonable and accurate channel modeling. Existing channel modeling works for EIT in non-line-of-sight (NLoS) scenario focus on far-field modeling, which can not accurately capture the characteristics of the channel in near-field. In this paper, we propose the near-field channel model for EIT based on electromagnetic scattering theory. We model the channel by using non-stationary Gaussian random fields and derive the analytical expression of the correlation function of the fields. Furthermore, we analyze the characteristics of the proposed channel model, e.g., channel degrees of freedom (DoF). Finally, we design a channel estimation scheme for near-field scenario by integrating the electromagnetic prior information of the proposed model. Numerical analysis verifies the correctness of the proposed scheme and shows that it can outperform existing schemes like least square (LS) and orthogonal matching pursuit (OMP).
Paper Structure (19 sections, 4 theorems, 45 equations, 15 figures, 2 algorithms)

This paper contains 19 sections, 4 theorems, 45 equations, 15 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Prior works
  3. Our contributions
  4. Organization and notation
  5. Electromagnetic Model for Scattering Field
  6. Channel Model Based on Non-Stationary Random Fields
  7. Mathematical derivation of the analytical model
  8. Numerical verification of the accuracy of the analytical model
  9. Characteristics of the Proposed Channel Model
  10. One realization of the random field
  11. Fitness to the statistics of practical model
  12. Impact of the scatterer size on the channel
  13. Channel DoF of the proposed model
  14. Channel Estimation based on the Proposed Model
  15. LS channel estimation
  16. ...and 4 more sections

Key Result

Lemma 1

Assuming that $r_s \ll d$, the correlation function of the channel can be approximated by where

Figures (15)

  • Figure 1: The three-dimensional near-field statistical channel modeling where the scatterers are located in solid circles.
  • Figure 2: The rotated coordinate system with ${\bf T}\hat{\boldsymbol{\mu}} = \hat{\bf e}_x$.
  • Figure 3: The correlation function plotted from the approximated analytical expression.
  • Figure 4: Comparison between the field correlation of CDL-A, CDL-D and the proposed coupling model.
  • Figure 5: The loss function degrades when iteration number increases.
  • ...and 10 more figures

Theorems & Definitions (5)

  • Lemma 1: Correlation function of the channel in weak near-field
  • Remark 1
  • Lemma 2: Extension of Rayleigh distance considering scatterer size
  • Lemma 3: Estimated error when using the proposed scheme
  • Corollary 1