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Near and Far Field Model Mismatch: Implications on 6G Communications, Localization, and Sensing

Ahmed Elzanaty, Jiuyu Liu, Anna Guerra, Francesco Guidi, Yi Ma, Rahim Tafazolli

TL;DR

This study seeks to revolve around the challenges faced by system designers, offering insights about the balance between model accuracy and achievable performance, and conducts a numerical performance analysis to verify the impact of the mismatch between NF and FF models.

Abstract

The upcoming 6G technology is expected to operate in near-field (NF) radiating conditions thanks to high-frequency and electrically large antenna arrays. Although several studies have already addressed this possibility, it is worth noting that NF models introduce higher complexity, the justification for which is not always evident in terms of performance improvements. This article investigates the implications of the mismatch between NF and far-field (FF) models concerning communication, localization, and sensing systems. Such disparity can lead to a degradation of performance metrics such as sensing and localization accuracy and communication efficiency. By exploring the effects of mismatches between NF and FF models, this study seeks to revolve around the challenges faced by system designers, offering insights about the balance between model accuracy and achievable performance. Finally, we conduct a numerical performance analysis to verify the impact of the mismatch between NF and FF models.

Near and Far Field Model Mismatch: Implications on 6G Communications, Localization, and Sensing

TL;DR

This study seeks to revolve around the challenges faced by system designers, offering insights about the balance between model accuracy and achievable performance, and conducts a numerical performance analysis to verify the impact of the mismatch between NF and FF models.

Abstract

The upcoming 6G technology is expected to operate in near-field (NF) radiating conditions thanks to high-frequency and electrically large antenna arrays. Although several studies have already addressed this possibility, it is worth noting that NF models introduce higher complexity, the justification for which is not always evident in terms of performance improvements. This article investigates the implications of the mismatch between NF and far-field (FF) models concerning communication, localization, and sensing systems. Such disparity can lead to a degradation of performance metrics such as sensing and localization accuracy and communication efficiency. By exploring the effects of mismatches between NF and FF models, this study seeks to revolve around the challenges faced by system designers, offering insights about the balance between model accuracy and achievable performance. Finally, we conduct a numerical performance analysis to verify the impact of the mismatch between NF and FF models.
Paper Structure (29 sections, 4 figures)

This paper contains 29 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Near-Field Models and Algorithms for Localization and Communications
  3. Definition and Characteristics
  4. Models for Array Signal Processing
  5. Algorithms for Near-Field Localization, Communications, and Sensing
  6. Localization & Sensing Algorithms
  7. Communications Algorithms
  8. Model Mismatch: Implications and Quantification
  9. Model Mismatch in Localization and Sensing Systems
  10. Mismatch Challenges in Localization and Sensing Systems
  11. Metrics for Localization and Sensing Model Mismatch
  12. Intuition Metrics
  13. Ad-hoc Metrics
  14. Metrics for Ultimate Performance Bounds
  15. Case Studies for Mismatch Analysis in Localization and Sensing
  16. ...and 14 more sections

Figures (4)

  • Figure 1: A practical example of an ELAA channel with spherical wavefront and a mix of LoS and NLoS links. The spatial inconsistency on the network side introduces significant diversity in channel spatial distribution, characterized as spatial non-stationarity.
  • Figure 2: Visualization of the localization model mismatch errors, for different UE positions, i.e., the normalized absolute difference between the localization error bound using the FF MCRLB and the CRLB of the true NF model in dB. The red solid line indicates the Fraunhofer distance.
  • Figure 3: Visualization of channel estimation model mismatch errors, for different UE positions, i.e., normalized relative MSE in dB, using the FF identity covariance matrix and the true NF covariance matrix inside the LMMSE.
  • Figure 4: Model mismatch of received SNR between NF and FF channel models to achieve a SER of $0.001$, for different UE positions. Note that the $y$-axis does not have a uniform scale in meters, leading to abrupt jumps in model mismatch errors at certain points.