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

Alejandro Lancho, Amir Weiss, Gary C.F. Lee, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)2296-2302
Number of pages7
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: 4 Dec 20228 Dec 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Blind synchronization
  • deep neural network
  • interference rejection
  • source separation
  • supervised learning

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