Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO
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ID: 283365
2019
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Abstract
We develop a two-stage deep learning pipeline architecture to estimate the
uplink massive MIMO channel with one-bit ADCs. This deep learning pipeline is
composed of two separate generative deep learning models. The first one is a
supervised learning model and designed to compensate for the quantization loss.
The second one is an unsupervised learning model and optimized for denoising.
Our results show that the proposed deep learning-based channel estimator can
significantly outperform other state-of-the-art channel estimators for one-bit
quantized massive MIMO systems. In particular, our design provides 5-10 dB gain
in channel estimation error. Furthermore, it requires a reasonable amount of
pilots, on the order of 20 per coherence time interval.
| Reference Key |
andrews2019twostage
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|---|---|
| Authors | Eren Balevi; Jeffrey G. Andrews |
| Journal | arXiv |
| Year | 2019 |
| DOI |
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