Learning Physical-Layer Communication with Quantized Feedback

Jinxiang Song, Bile Peng, Christian Hager, Henk Wymeersch, Anant Sahai
2019 IEEE Transactions on Communications  
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
more » ... he 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. I. INTRODUCTION As communication systems become more complex, physical-layer design, i.e., devising optimal transmission and detection methods, has become harder as well. This is true not only in wireless communication, where hardware impairments and quantization have increasingly become a limitation on the achievable performance, but also in optical communication, for which the nonlinear nature of the channel precludes the use of standard approaches. This has led to a new line of research on physical-layer communication where transmission and detection methods are learned from data. The general idea is to regard the transmitter and receiver as parameterized functions (e.g., neural networks) and find good parameter configurations using large-scale gradient-based optimization approaches from machine learning. Data-driven methods have mainly focused on learning receivers assuming a given transmitter and channel, e.g., for MIMO detection [1] or decoding [2]. These methods have led to algorithms that either perform better or exhibit lower complexity than model-based algorithms. More recently, endto-end learning of both the transmitter and receiver has been proposed for various physical-layer applications including wireless [3], [4], nonlinear optical [5]-[7], and visible light communication [8].
doi:10.1109/tcomm.2019.2951563 fatcat:j35q6g2rxbhqhgtfhjewl4yph4