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

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.

References in corpus (5)

• Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
• Deep Learning for Audio Signal Processing
• Sudo rm -rf: Efficient Networks for Universal Audio Source Separation
• Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
• Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation

Cited by in corpus (1)

• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge