  In this work, we develop DeepWiPHY, a deep learning-based architecture to
replace the channel estimation, common phase error (CPE) correction, sampling
rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based
orthogonal frequency division multiplexing (OFDM) receivers. We first train
DeepWiPHY with a synthetic dataset, which is generated using representative
indoor channel models and includes typical radio frequency (RF) impairments
that are the source of nonlinearity in wireless systems. To further train and
evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based
data collection testbed composed of Universal Software Radio Peripherals
(USRPs) and commercially available IEEE 802.11ax products. The comprehensive
evaluation of DeepWiPHY with synthetic and real-world datasets (110 million
synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that,
even without fine-tuning the neural network's architecture parameters,
DeepWiPHY achieves comparable performance to or outperforms the conventional
WLAN receivers, in terms of both bit error rate (BER) and packet error rate
(PER), under a wide range of channel models, signal-to-noise (SNR) levels, and
modulation schemes.
