Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems
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.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2020
- DOI:
- 10.48550/arXiv.2006.00169
- arXiv:
- arXiv:2006.00169
- Bibcode:
- 2020arXiv200600169G
- Keywords:
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- Computer Science - Information Theory;
- Electrical Engineering and Systems Science - Signal Processing
- E-Print:
- Presented at IEEE SPAWC 2020