On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals

• URL: http://arxiv.org/abs/2303.06438v1
• Date: Sat, 11 Mar 2023 16:29:13 GMT
• Title: On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals
• Authors: Gary C.F. Lee and Amir Weiss and Alejandro Lancho and Yury Polyanskiy and Gregory W. Wornell
• Abstract summary: We study the single-channel source separation problem involving frequency-division multiplexing (OFDM) signals. We propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures.
• Score: 104.11663769306566
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We study the single-channel source separation problem involving orthogonal frequency-division multiplexing (OFDM) signals, which are ubiquitous in many modern-day digital communication systems. Related efforts have been pursued in monaural source separation, where state-of-the-art neural architectures have been adopted to train an end-to-end separator for audio signals (as 1-dimensional time series). In this work, through a prototype problem based on the OFDM source model, we assess -- and question -- the efficacy of using audio-oriented neural architectures in separating signals based on features pertinent to communication waveforms. Perhaps surprisingly, we demonstrate that in some configurations, where perfect separation is theoretically attainable, these audio-oriented neural architectures perform poorly in separating co-channel OFDM waveforms. Yet, we propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures, that can confer about 30 dB improvement in performance.

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