Deep Learning Based OFDM Physical-Layer Receiver for Extreme Mobility
Pihlajasalo, Jaakko; Korpi, Dani; Honkala, Mikko; Huttunen, Janne M.J.; Riihonen, Taneli; Talvitie, Jukka; Uusitalo, Mikko A.; Valkama, Mikko (2022)
Huttunen, Janne M.J.
Uusitalo, Mikko A.
Matthews, Michael B.
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In this paper, we propose a machine learning (ML) aided physical layer receiver technique for demodulating OFDM signals that are subject to very high Doppler effects and the corresponding distortion in the received signal. Specifically, we develop a deep learning based convolutional neural network receiver system that absorbs proper two-dimensional received signal entities in time and frequency, while containing convolutional neural network layers to efficiently and reliably demodulate the bits - when properly trained - despite the substantial Doppler distortion. Representative set of numerical results is provided, in the context of 5G NR mobile communication network and corresponding base-station demodulation performance for uplink. The obtained results show that the proposed receiver system is able to clearly outperform classical LMMSE receivers that operate on subcarrier level and neglect the Doppler-induced intercarrier interference (ICI). Additionally, the proposed ML receiver has the advantage over ICI cancellation based receivers in terms of the reference signal overhead. This paper provides the description of the method and vast set of numerical results in 5G NR network context.
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