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Channel Estimation in MIMO Systems Aided by Microwave Linear Analog Computers (MiLACs)

Qiaosen Zhang, Matteo Nerini, Bruno Clerckx

TL;DR

This work tackles channel estimation for MiLAC-aided MIMO systems by designing fully analog LS and MMSE estimators. For LS, it selects a training matrix with $\mathbf{X}=\sqrt{P_T/N_T}\mathbf{I}_{N_T}$ and derives $\mathbf{F}_t$ and $\mathbf{G}_t$ so that the $t$-th column of $\hat{\mathbf{H}}_{\text{LS}}$ is read directly at the receive RF chains, with MiLAC admittance design guaranteeing exact implementation. For MMSE, it adopts a canonical correlated model, diagonalizes the virtual channel via $\mathbf{H}=\mathbf{U}_R\mathbf{H}_v\mathbf{U}_T^H$, and constructs diagonal training in the eigen-domain, enabling analog-domain MMSE with the same MSE as digital MMSE. Numerical results show that MiLAC-aided LS/MMSE achieve identical performance to their digital counterparts while reducing online computation, RF-chain count, ADC/DAC resolution, and PAPR. This demonstrates MiLACs as a practical path toward real-time, energy-efficient CSI acquisition for future gigantic MIMO systems.

Abstract

Microwave linear analog computers (MiLACs) have recently emerged as a promising solution for future gigantic multiple-input multiple-output (MIMO) systems, enabling beamforming with greatly reduced hardware and computational cost. However, channel estimation for MiLAC-aided systems remains an open problem. Conventional least squares (LS) and minimum mean square error (MMSE) estimation rely on intensive digital computation, which undermines the benefits offered by MiLACs. In this letter, we propose efficient LS and MMSE channel estimation schemes for MiLAC-aided MIMO systems. By designing training precoders and combiners implemented by MiLACs, both LS and MMSE estimation are performed fully in the analog domain, achieving identical performance to their digital counterparts while significantly reducing computational complexity, transmit RF chains, analog-to-digital/digital-to-analog converters (ADCs/DACs) resolution requirements, and peak-to-average power ratio (PAPR). Numerical results verify the effectiveness and advantages of the proposed schemes.

Channel Estimation in MIMO Systems Aided by Microwave Linear Analog Computers (MiLACs)

TL;DR

This work tackles channel estimation for MiLAC-aided MIMO systems by designing fully analog LS and MMSE estimators. For LS, it selects a training matrix with and derives and so that the -th column of is read directly at the receive RF chains, with MiLAC admittance design guaranteeing exact implementation. For MMSE, it adopts a canonical correlated model, diagonalizes the virtual channel via , and constructs diagonal training in the eigen-domain, enabling analog-domain MMSE with the same MSE as digital MMSE. Numerical results show that MiLAC-aided LS/MMSE achieve identical performance to their digital counterparts while reducing online computation, RF-chain count, ADC/DAC resolution, and PAPR. This demonstrates MiLACs as a practical path toward real-time, energy-efficient CSI acquisition for future gigantic MIMO systems.

Abstract

Microwave linear analog computers (MiLACs) have recently emerged as a promising solution for future gigantic multiple-input multiple-output (MIMO) systems, enabling beamforming with greatly reduced hardware and computational cost. However, channel estimation for MiLAC-aided systems remains an open problem. Conventional least squares (LS) and minimum mean square error (MMSE) estimation rely on intensive digital computation, which undermines the benefits offered by MiLACs. In this letter, we propose efficient LS and MMSE channel estimation schemes for MiLAC-aided MIMO systems. By designing training precoders and combiners implemented by MiLACs, both LS and MMSE estimation are performed fully in the analog domain, achieving identical performance to their digital counterparts while significantly reducing computational complexity, transmit RF chains, analog-to-digital/digital-to-analog converters (ADCs/DACs) resolution requirements, and peak-to-average power ratio (PAPR). Numerical results verify the effectiveness and advantages of the proposed schemes.
Paper Structure (12 sections, 22 equations, 2 figures)

This paper contains 12 sections, 22 equations, 2 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. MiLAC-aided LS Channel Estimation
  4. Training Precoder and Combiner Design
  5. MiLAC Admittance Matrix Design
  6. Comparison with Digital LS Channel Estimation
  7. MiLAC-aided MMSE Channel Estimation
  8. Channel Model
  9. Training Precoder and Combiner Design
  10. Comparison with Digital MMSE Channel Estimation
  11. Numerical Results
  12. Conclusion

Figures (2)

  • Figure 1: MiLAC-aided MIMO system model.
  • Figure 2: Comparison of digital and MiLAC-aided channel estimation in an $N_R \times N_T$ MIMO system: (a) NMSE versus SNR; (b) computational complexity versus number of receive antennas.