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

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...

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Bibliographic Details
Main Authors: Gallyas-Sanhueza, Alexandra, Mirfarshbafan, Seyed Hadi, Ghods, Ramina, Studer, Christoph
Format: Conference Proceeding
Language:English
Subjects:
Channel estimation
Complexity theory
MIMO communication
Noise reduction
Quantization (signal)
Signal processing algorithms
Wireless communication
Online Access:Request full text
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Summary: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.
ISSN:1948-3252
DOI:10.1109/SPAWC48557.2020.9154213