Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems
Information Theory
2020-06-02 v1 Signal Processing
math.IT
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.
Cite
@article{arxiv.2006.00169,
title = {Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems},
author = {Alexandra Gallyas-Sanhueza and Seyed Hadi Mirfarshbafan and Ramina Ghods and Christoph Studer},
journal= {arXiv preprint arXiv:2006.00169},
year = {2020}
}
Comments
Presented at IEEE SPAWC 2020