Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Near or far: On determining the appropriate channel estimation strategy in cross-field communication

Simon Tarboush, Anum Ali, Tareq Y. Al-Naffouri

TL;DR

This work tackles cross-field channel estimation for ultra-massive MIMO with an AoSA architecture at sub-THz/THz frequencies by online deciding whether near-field SWM or far-field HSPWM should be used for channel estimation. It introduces a beam-training–based region metric $ eta$ and fuses multiple decisions with a hidden Markov model, using Viterbi inference to improve region detection. Offline training determines a threshold $ gamma_{N-F}$ for near- vs far-field classification, while online, the HMM exploits temporal observations to maintain high decision accuracy even at low SNR. Results demonstrate that the proposed approach increases decision success rates and robustly identifies the correct estimation strategy across varying aperture configurations and noise levels.

Abstract

The use of ultra-massive multiple-input multiple-output and high-frequency large bandwidth systems is likely in the next-generation wireless communication systems. In such systems, the user moves between near- and far-field regions, and consequently, the channel estimation will need to be carried out in the cross-field scenario. Channel estimation strategies have been proposed for both near- and far-fields, but in the cross-field problem, the first step is to determine whether the near- or far-field is applicable so that an appropriate channel estimation strategy can be employed. In this work, we propose using a hidden Markov model over an ensemble of region estimates to enhance the accuracy of selecting the actual region. The region indicators are calculated using the pair-wise power differences between received signals across the subarrays within an array-of-subarrays architecture. Numerical results show that the proposed method achieves a high success rate in determining the appropriate channel estimation strategy.

Near or far: On determining the appropriate channel estimation strategy in cross-field communication

TL;DR

This work tackles cross-field channel estimation for ultra-massive MIMO with an AoSA architecture at sub-THz/THz frequencies by online deciding whether near-field SWM or far-field HSPWM should be used for channel estimation. It introduces a beam-training–based region metric and fuses multiple decisions with a hidden Markov model, using Viterbi inference to improve region detection. Offline training determines a threshold for near- vs far-field classification, while online, the HMM exploits temporal observations to maintain high decision accuracy even at low SNR. Results demonstrate that the proposed approach increases decision success rates and robustly identifies the correct estimation strategy across varying aperture configurations and noise levels.

Abstract

The use of ultra-massive multiple-input multiple-output and high-frequency large bandwidth systems is likely in the next-generation wireless communication systems. In such systems, the user moves between near- and far-field regions, and consequently, the channel estimation will need to be carried out in the cross-field scenario. Channel estimation strategies have been proposed for both near- and far-fields, but in the cross-field problem, the first step is to determine whether the near- or far-field is applicable so that an appropriate channel estimation strategy can be employed. In this work, we propose using a hidden Markov model over an ensemble of region estimates to enhance the accuracy of selecting the actual region. The region indicators are calculated using the pair-wise power differences between received signals across the subarrays within an array-of-subarrays architecture. Numerical results show that the proposed method achieves a high success rate in determining the appropriate channel estimation strategy.
Paper Structure (9 sections, 6 equations, 2 figures, 1 table)

This paper contains 9 sections, 6 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. System and Channel Model
  3. AoSA Architecture
  4. Channel Models
  5. SWM
  6. HSPWM
  7. Proposed Model Selection Strategy
  8. Simulation Results and Discussion
  9. Conclusion

Figures (2)

  • Figure 1: Illustration of the cross-field channel estimation problem, depicting various channel models suitable for different distances. The transmitter and receiver employ an UM-MIMO AoSA architecture in a sub-THz/THz-band communication system.
  • Figure 2: Analysis of the proposed selection metric $\eta$ versus distance in the offline training stage for various $N_\mathrm{T} \times N_\mathrm{R}$ Tx-Rx configurations and different SNR values: (a) $1024 \times 64$ with a Tx uses $Q_\mathrm{T}/\bar{Q}_\mathrm{T} = 4/256$ SAs/AEs, (b) $1024 \times 64$ with a Rx uses $Q_\mathrm{R}/\bar{Q}_\mathrm{R} = 4/16$ SAs/AEs, (c) changing $N_\mathrm{R}$ by increasing the Rx AEs within each Rx SAs for a fixed Tx configuration with $N_\mathrm{T}=1024$ and $Q_\mathrm{T}/\bar{Q}_\mathrm{T} = 4/256$ SAs/AEs, and (d) changing $N_\mathrm{T}$ by increasing the Tx AEs within each Tx SAs for a fixed Rx configuration with $N_\mathrm{R}=64$ and $Q_\mathrm{R}/\bar{Q}_\mathrm{R} = 4/16$ SAs/AEs.