Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Semi-Blind Channel Estimation and Hybrid Receiver Beamforming in the Tera-Hertz Multi-User Massive MIMO Uplink

Abhisha Garg, Suraj Srivastava, Varsha Dubey, Aditya Jagannatham, Lajos Hanzo

Abstract

We develop a pragmatic multi-user (MU) massive multiple-input multiple-output (MIMO) channel model tailored to the THz band, encompassing factors such as molecular absorption, reflection losses and multipath diffused ray components. Next, we propose a novel semi-blind based channel state information (CSI) acquisition technique i.e. MU whitening decorrelation semi-blind (MU-WD-SB) that exploits the second order statistics corresponding to the unknown data symbols along with pilot vectors. A constrained Cramer-Rao Lower Bound (C-CRLB) is derived to bound the normalized mean square error (NMSE) performance of the proposed semi-blind learning technique. Our proposed scheme efficiently reduces the training overheads while enhancing the overall accuracy of the channel learning process. Furthermore, a novel hybrid receiver combiner framework is devised for MU THz massive MIMO systems, leveraging multiple measurement vector based sparse Bayesian learning (MMV-SBL) that relies on the estimated CSI acquired through our proposed semi-blind technique relying on low resolution analog-to-digital converters (ADCs). Finally, we propose an optimal hybrid combiner based on MMV-SBL, which directly reduces the MU interference. Extensive simulations are conducted to evaluate the performance gain of the proposed MU-WD-SB scheme over conventional training-based and other semi-blind learning techniques for a practical THz channel obtained from the high-resolution transmission (HITRAN) database. The metrics considered for quantifying the improvements include the NMSE, bit error rate (BER) and spectral-efficiency (SE).

Semi-Blind Channel Estimation and Hybrid Receiver Beamforming in the Tera-Hertz Multi-User Massive MIMO Uplink

Abstract

We develop a pragmatic multi-user (MU) massive multiple-input multiple-output (MIMO) channel model tailored to the THz band, encompassing factors such as molecular absorption, reflection losses and multipath diffused ray components. Next, we propose a novel semi-blind based channel state information (CSI) acquisition technique i.e. MU whitening decorrelation semi-blind (MU-WD-SB) that exploits the second order statistics corresponding to the unknown data symbols along with pilot vectors. A constrained Cramer-Rao Lower Bound (C-CRLB) is derived to bound the normalized mean square error (NMSE) performance of the proposed semi-blind learning technique. Our proposed scheme efficiently reduces the training overheads while enhancing the overall accuracy of the channel learning process. Furthermore, a novel hybrid receiver combiner framework is devised for MU THz massive MIMO systems, leveraging multiple measurement vector based sparse Bayesian learning (MMV-SBL) that relies on the estimated CSI acquired through our proposed semi-blind technique relying on low resolution analog-to-digital converters (ADCs). Finally, we propose an optimal hybrid combiner based on MMV-SBL, which directly reduces the MU interference. Extensive simulations are conducted to evaluate the performance gain of the proposed MU-WD-SB scheme over conventional training-based and other semi-blind learning techniques for a practical THz channel obtained from the high-resolution transmission (HITRAN) database. The metrics considered for quantifying the improvements include the NMSE, bit error rate (BER) and spectral-efficiency (SE).
Paper Structure (23 sections, 77 equations, 5 figures, 5 tables, 2 algorithms)

This paper contains 23 sections, 77 equations, 5 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Review of existing works
  3. Novel contributions of the paper
  4. Organization of the paper
  5. Notation:
  6. MU Massive MIMO THz System and Channel Models
  7. MU massive MIMO THz channel model
  8. Pilot transmission
  9. Conventional ML-based channel estimation
  10. Semi-Blind Estimation using our RALS-SB approach
  11. MU THz WD-SB Technique for CSI estimation in MU THz massive MIMO systems
  12. Constrained CRLB for the MU-WD-SB scheme
  13. MSE performance enhancement for the MU-WD-SB framework
  14. Computational Complexity
  15. Spatially sparse combining for MU THz massive MIMO systems
  16. ...and 8 more sections

Figures (5)

  • Figure 1: Block diagram of a MU THz massive MIMO system.
  • Figure 2: Frame structures of THz ML and SB channel estimation schemes
  • Figure 3: NMSE versus SNR comparison between conventional MU-THz-ML estimation scheme and MU-WD-SB scheme $\left(a\right)$ NMSE vs SNR for $N_{BS}\in \left\{32,64,128\right\}$ with $\mathbf{W}$ perfectly known along with the CRLB plots. $\left(b\right)$ NMSE vs SNR for $N\in \left\{100, 400, 1500\right\}$ with $\mathbf{W}$ perfectly and imperfectly known $\left(c\right)$ NMSE versus number of pilot beams for SNR$\in \left\{-5,5\right\}$dB.
  • Figure 4: Comparison plots between different channel estimation schemes $\left(a\right)$ NMSE versus SNR comparison between MU-THz-ML, MU-RALS-SB and the proposed MU-WD-SB scheme. $\left(b\right)$ BER vs SNR plot compared with ML estimation scheme along with $\mathbf{W}$ perfectly and imperfectly known. $\left(c\right)$ NMSE vs SNR comparison for the MU THz massive MIMO system considering low-resolution ADCs.
  • Figure 5: $\left(a\right)$ Empirical CDF for channel estimation error of MU-THz-ML and proposed MU-WD-SB with perfect and imperfect $\mathbf{W}$$\left(b\right)$ Spectral Efficiency vs SNR comparison for the MU massive MIMO THz system with different frequencies and distances $\left(c\right)$ Spectral Efficiency vs number of BS $N_{BS}$ antennas with different pilots.