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In this work, we develop DeepWiPHY, a deep learning-based architecture toreplace the channel estimation, common phase error (CPE) correction, samplingrate offset (SRO) correction, and equalization modules of IEEE 802.11ax basedorthogonal frequency division multiplexing (OFDM) receivers. We first trainDeepWiPHY with a synthetic dataset, which is generated using representativeindoor channel models and includes typical radio frequency (RF) impairmentsthat are the source of nonlinearity in wireless systems. To further train andevaluate DeepWiPHY with real-world data, we develop a passive sniffing-baseddata collection testbed composed of Universal Software Radio Peripherals(USRPs) and commercially available IEEE 802.11ax products. The comprehensiveevaluation of DeepWiPHY with synthetic and real-world datasets (110 millionsynthetic 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 conventionalWLAN 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, andmodulation schemes.

DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems