Learning Physical-Layer Communication with Quantized Feedback
Artikel i vetenskaplig tidskrift, 2020

Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work has shown that practical implementations of this approach require a feedback signal from the receiver to the transmitter. In this paper, we study the impact of quantized feedback on data-driven learning of physical-layer communication. A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for nonstationary signal distributions. The method is evaluated for linear and nonlinear channels. Simulation results show that feedback quantization does not appreciably affect the learning process and can lead to similar performance as compared to the case where unquantized feedback is used for training, even with 1-bit quantization. In addition, it is shown that learning is surprisingly robust to noisy feedback where random bit flips are applied to the quantization bits.

physical-layer

feedback quantization

noisy feedback

Data-driven optimization

nonstationary distribution

Författare

Jinxiang Song

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Bile Peng

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Christian Häger

Duke University

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Anant Sahai

University of California at Berkeley

IEEE Transactions on Communications

0090-6778 (ISSN)

Vol. 68 1 645-653 8891726

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Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Telekommunikation

Sannolikhetsteori och statistik

Signalbehandling

DOI

10.1109/TCOMM.2019.2951563

Mer information

Senast uppdaterat

2020-12-17