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Terahertz Beamforming and Group Sparse Channel Estimation Relying on Low-Resolution ADCs in MU Hybrid MIMO systems

Abhisha Garg, Suraj Srivastava, Akash Kumar, Nimish Yadav, Aditya Jagannatham, Lajos Hanzo

Abstract

A unified beamforming and channel estimation framework relying on Bayesian learning is conceived. Recognizing the limitations imposed by low-resolution analog-to-digital converter (ADCs) and frequency-dependent propagation effects occurring in the Terahertz (THz) band, we formulate a dual-wideband channel model incorporating root raised cosine (RRC) pulse shaping. To address the non-linear distortions introduced by low-resolution ADCs, Bussgang decomposition is employed, leading to a tractable linearized inference process. By leveraging the shared sparsity inherent in a multi-user (MU) scenario of THz systems, we propose a Hierarchical Bayesian Group-sparse Regression (HBG-SR) based channel learning technique that exploits the group-sparse structure of THz band channels. The estimated dominant angle-of-arrival/ angle-of-departure (AoA/AoD) indices are then exploited for appropriately configuring the true-time-delay (TTD) elements in the hybrid transceiver, enabling precise beam alignment across subcarriers and the effective compensation of the beam-squint effect occurring in wideband THz systems. Extensive simulation results validate the efficiency of the proposed channel estimator and the TTD-aided beamforming architecture, highlighting their robustness and performance gains under practical wideband THz system constraints.

Terahertz Beamforming and Group Sparse Channel Estimation Relying on Low-Resolution ADCs in MU Hybrid MIMO systems

Abstract

A unified beamforming and channel estimation framework relying on Bayesian learning is conceived. Recognizing the limitations imposed by low-resolution analog-to-digital converter (ADCs) and frequency-dependent propagation effects occurring in the Terahertz (THz) band, we formulate a dual-wideband channel model incorporating root raised cosine (RRC) pulse shaping. To address the non-linear distortions introduced by low-resolution ADCs, Bussgang decomposition is employed, leading to a tractable linearized inference process. By leveraging the shared sparsity inherent in a multi-user (MU) scenario of THz systems, we propose a Hierarchical Bayesian Group-sparse Regression (HBG-SR) based channel learning technique that exploits the group-sparse structure of THz band channels. The estimated dominant angle-of-arrival/ angle-of-departure (AoA/AoD) indices are then exploited for appropriately configuring the true-time-delay (TTD) elements in the hybrid transceiver, enabling precise beam alignment across subcarriers and the effective compensation of the beam-squint effect occurring in wideband THz systems. Extensive simulation results validate the efficiency of the proposed channel estimator and the TTD-aided beamforming architecture, highlighting their robustness and performance gains under practical wideband THz system constraints.
Paper Structure (23 sections, 71 equations, 5 figures, 10 tables, 3 algorithms)

This paper contains 23 sections, 71 equations, 5 figures, 10 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Review of existing works
  3. Contributions
  4. Notations
  5. SC-FDE based MU THz Hybrid MIMO System Model
  6. MU THz dual-wideband channel model
  7. PSF-based dual-wideband channel
  8. MU sparse channel estimation model
  9. Hierarchical Bayesian Group Sparse Regression based channel estimation
  10. MU Bayesian Cramér-Rao lower bound
  11. Computational complexity
  12. TTD based Hybrid Combiner design
  13. Generation of frequency dependent beamformers
  14. Calculation of optimal time delays
  15. Minimum number of TD elements required to mitigate the beam-squint effect
  16. ...and 8 more sections

Figures (5)

  • Figure 1: Schematic diagram of SC-FDE based MU THz hybrid MIMO systems with low-resolution ADCs
  • Figure 2: Frame structure utilized for wideband MU THz hybrid MIMO system using SC-FDE
  • Figure 3: $\left(a\right)$ Normalized array gain vs physical direction corresponding to considered scenario $\left(b\right)$ Spectral efficiency in (bits/sec/Hz) vs SNR for the proposed and existing state-of-the-art approaches
  • Figure 4: $\left(a\right)$ NMSE vs SNR comparison for the proposed and existing state-of-the-art approaches $\left(b\right)$ BER vs SNR comparison for the proposed and existing state-of-the-art approaches $\left(c\right)$ Effect of low-resolution ADCs on the proposed HBG-SR channel learning technique
  • Figure 5: Performance comparison for the proposed HBG-SR technique with RRC-PSF and Rect-PSF based dual-wideband channel formulations $\left(a\right)$ NMSE vs SNR $\left(b\right)$ BER vs SNR $\left(c\right)$ Spectral efficiency with different transceiver schemes.