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One-Bit OFDM Receivers via Deep Learning
Date
2019-06-01
Author
Balevi, Eren
Andrews, Jeffrey G.
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver-specifically, an autoencoderjointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.
Subject Keywords
Deep learning
,
OFDM
,
channel estimation
,
data detection
,
one-bit quantization
,
MIMO SYSTEMS
,
CHANNEL ESTIMATION
,
DETECTOR
,
UPLINK
URI
https://hdl.handle.net/11511/100217
Journal
IEEE TRANSACTIONS ON COMMUNICATIONS
DOI
https://doi.org/10.1109/tcomm.2019.2903811
Collections
Department of Electrical and Electronics Engineering, Article
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BibTeX
E. Balevi and J. G. Andrews, “One-Bit OFDM Receivers via Deep Learning,”
IEEE TRANSACTIONS ON COMMUNICATIONS
, vol. 67, no. 6, pp. 4326–4336, 2019, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100217.