TY - GEN
T1 - Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
AU - Lancho, Alejandro
AU - Weiss, Amir
AU - Lee, Gary C.F.
AU - Tang, Jennifer
AU - Bu, Yuheng
AU - Polyanskiy, Yury
AU - Wornell, Gregory W.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Blind synchronization
KW - deep neural network
KW - interference rejection
KW - source separation
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85146949104&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM48099.2022.10001513
DO - 10.1109/GLOBECOM48099.2022.10001513
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AN - SCOPUS:85146949104
T3 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
SP - 2296
EP - 2302
BT - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Global Communications Conference, GLOBECOM 2022
Y2 - 4 December 2022 through 8 December 2022
ER -
