# Learning Physical-Layer Communication with Quantized Feedback

## TL;DR

This paper investigates how quantized feedback impacts data-driven physical-layer communication, proposing a novel quantization method that maintains high performance even with minimal feedback bits and noisy conditions.

## Contribution

It introduces a new quantization technique tailored for feedback signals in physical-layer learning, demonstrating robustness and effectiveness with very low-bit feedback.

## Key findings

- Quantized feedback has minimal impact on learning performance.
- 1-bit feedback can achieve excellent communication results.
- Learning remains robust under noisy, bit-flipped feedback.

## Abstract

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 in 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 non-stationary 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 excellent performance, 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.