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Binary Hypothesis Testing-Based Low-Complexity Beamspace Channel Estimation for mmWave Massive MIMO Systems

Hanyoung Park, Ji-Woong Choi

TL;DR

A low-complexity channel denoiser based on Bayesian binary hypothesis testing and beamspace sparsity that achieves channel estimation accuracy that is comparable to that of complex iterative or learning-based approaches.

Abstract

Millimeter-wave (mmWave) communications have gained attention as a key technology for high-capacity wireless systems, owing to the wide available bandwidth. However, mmWave signals suffer from their inherent characteristics such as severe path loss, poor scattering, and limited diffraction, which necessitate the use of large antenna arrays and directional beamforming, typically implemented through massive MIMO architectures. Accurate channel estimation is critical in such systems, but its computational complexity increases proportionally with the number of antennas. This may become a significant burden in mmWave systems where channels exhibit rapid fluctuations and require frequent updates. In this paper, we propose a low-complexity channel denoiser based on Bayesian binary hypothesis testing and beamspace sparsity. By modeling each sparse beamspace component as a mixture of signal and noise under a Bernoulli-complex Gaussian prior, we formulate a likelihood ratio test to detect signal-relevant elements. Then, a hard-thresholding rule is applied to suppress noise-dominant components in the noisy channel vector. Despite its extremely low computational complexity, the proposed method achieves channel estimation accuracy that is comparable to that of complex iterative or learning-based approaches. This effectiveness is supported by both theoretical analysis and numerical evaluation, suggesting that the method can be a viable option for mmWave systems with strict resource constraints.

Binary Hypothesis Testing-Based Low-Complexity Beamspace Channel Estimation for mmWave Massive MIMO Systems

TL;DR

A low-complexity channel denoiser based on Bayesian binary hypothesis testing and beamspace sparsity that achieves channel estimation accuracy that is comparable to that of complex iterative or learning-based approaches.

Abstract

Millimeter-wave (mmWave) communications have gained attention as a key technology for high-capacity wireless systems, owing to the wide available bandwidth. However, mmWave signals suffer from their inherent characteristics such as severe path loss, poor scattering, and limited diffraction, which necessitate the use of large antenna arrays and directional beamforming, typically implemented through massive MIMO architectures. Accurate channel estimation is critical in such systems, but its computational complexity increases proportionally with the number of antennas. This may become a significant burden in mmWave systems where channels exhibit rapid fluctuations and require frequent updates. In this paper, we propose a low-complexity channel denoiser based on Bayesian binary hypothesis testing and beamspace sparsity. By modeling each sparse beamspace component as a mixture of signal and noise under a Bernoulli-complex Gaussian prior, we formulate a likelihood ratio test to detect signal-relevant elements. Then, a hard-thresholding rule is applied to suppress noise-dominant components in the noisy channel vector. Despite its extremely low computational complexity, the proposed method achieves channel estimation accuracy that is comparable to that of complex iterative or learning-based approaches. This effectiveness is supported by both theoretical analysis and numerical evaluation, suggesting that the method can be a viable option for mmWave systems with strict resource constraints.

Paper Structure

This paper contains 18 sections, 4 theorems, 74 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Prior Works
  3. Contributions
  4. Paper Outline
  5. System Model
  6. Hypothesis Testing-Based Beamspace Channel Estimation
  7. Simulation Results
  8. Simulation Configuration
  9. Complexity Analysis
  10. Performance Evaluation
  11. Conclusion
  12. Derivation of Hypothesis Testing Threshold
  13. Derivation of Theoretical $P_{FA}$ and $P_D$
  14. Proofs
  15. Proof of Theorem \ref{['thm:bound']}
  16. ...and 3 more sections

Key Result

Theorem 1

Given the proposed channel estimation algorithm and the aforementioned system model, the MSE of the estimated channel is approximately expressed as: where MSE is defined as

Figures (6)

  • Figure 1: Estimated activity rate depending on SNR. (a) synthetic channel (b) LoS (c) NLoS.
  • Figure 2: MSE depending on SNR compared to baselines. (a) LoS (b) NLoS.
  • Figure 3: Post-equalization BER depending on SNR compared to baselines. (a) LoS (b) NLoS.
  • Figure 4: MSE depending on cost $C$. (a) LoS (b) NLoS.
  • Figure 5: MSE depending on the noise power estimation error. (a) LoS (b) NLoS.
  • ...and 1 more figures

Theorems & Definitions (8)

  • Theorem 1: Expected MSE
  • proof
  • Corollary 1
  • proof
  • Corollary 2
  • proof
  • Theorem 2
  • proof