Learning Physical-Layer Communication with Quantized Feedback
Journal article, 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.

noisy feedback


Data-driven optimization

nonstationary distribution

feedback quantization


Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Bile Peng

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Christian Häger

Duke University

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Anant Sahai

University of California at Berkeley

IEEE Transactions on Communications

0090-6778 (ISSN) 15580857 (eISSN)

Vol. 68 1 645-653 8891726

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Areas of Advance

Information and Communication Technology

Subject Categories


Probability Theory and Statistics

Signal Processing



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