Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals

TL;DR

This paper explores data-driven deep learning techniques for blind synchronization and interference rejection in digital communication signals, emphasizing the importance of domain knowledge and temporal structure capture for improved performance.

Contribution

It introduces a domain-informed neural network design that outperforms standard methods by leveraging communication-specific domain knowledge and high-resolution temporal structures.

Findings

Neural network design improves interference rejection performance.

Capturing nonstationarities enhances synchronization accuracy.

Domain knowledge significantly boosts data-driven communication signal processing.

Abstract

We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. In particular, we assume knowledge on the generation process of one of the signals, dubbed signal of interest (SOI), and no knowledge on the generation process of the second signal, referred to as interference. This form of the single-channel source separation problem is also referred to as interference rejection. We show that capturing high-resolution temporal structures (nonstationarities), which enables accurate synchronization to both the SOI and the interference, leads to substantial performance gains. With this key insight, we propose a domain-informed neural network (NN) design that is able to improve upon both "off-the-shelf" NNs and classical detection and interference rejection methods, as demonstrated in our simulations. Our findings highlight the key role communication-specific domain knowledge plays in the development of data-driven approaches that hold the promise of unprecedented gains.