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arXiv · 2006.00169

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

Alexandra Gallyas-Sanhueza·Seyed Hadi Mirfarshbafan·Ramina Ghods·Christoph Studer
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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.

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Alexandra Gallyas-Sanhueza, Seyed Hadi Mirfarshbafan, Ramina Ghods, Christoph Studer. 2020-05-30. Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems. https://arxiv.org/abs/2006.00169

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