Learning Physical-Layer Communication with Quantized Feedback
Journal article, 2019

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.

feedback quantization

physical-layer

noisy feedback

nonstationary distribution

Data-driven optimization

Author

Jinxiang Song

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Bile Peng

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Christian Häger

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Henk Wymeersch

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Anant Sahai

University of California at Berkeley

IEEE Transactions on Communications

0090-6778 (ISSN)

Unlocking the Full-dimensional Fiber Capacity

Knut and Alice Wallenberg Foundation, 2019-07-01 -- 2024-06-30.

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Probability Theory and Statistics

Signal Processing

DOI

10.1109/TCOM.2019.2951563

More information

Latest update

12/13/2019