Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

TL;DR

This paper introduces adaptive algorithms for channel estimation in 1-bit mmWave massive MIMO systems, enhancing accuracy by automatically tuning parameters based on channel conditions, demonstrated through simulations.

Contribution

The paper presents a novel sparsity-adaptive beamspace channel estimation method with a SURE-based tuning stage for 1-bit mmWave MIMO systems, improving accuracy and efficiency.

Findings

Enhanced channel estimation accuracy with 1-bit measurements.

Automatic parameter tuning improves robustness across channel conditions.

Algorithms are computationally efficient and effective in simulations.

Abstract

We propose sparsity-adaptive beamspace channel estimation algorithms that improve accuracy for 1-bit data converters in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. Our algorithms include a tuning stage based on Stein's unbiased risk estimate (SURE) that automatically selects optimal denoising parameters depending on the instantaneous channel conditions. Simulation results with line-of-sight (LoS) and non-LoS mmWave massive MIMO channel models show that our algorithms improve channel estimation accuracy with 1-bit measurements in a computationally-efficient manner.