  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
greatly reduces complexity and power consumption, but makes accurate channel
estimation and data detection difficult. This is particularly true for
multicarrier waveforms, which have high peak-to-average 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
(DNN) that can be trained with a reasonable number of pilots. After channel
estimation, a neural network-based receiver -- specifically, an autoencoder --
jointly 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 (LS) 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.
