# Learning Physical-Layer Communication with Quantized Feedback

arXiv: https://arxiv.org/abs/1904.09252 · HTML: https://mlsanity.com/paper/1904.09252/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Deep Reinforcement Learning-Based DRAM Equalizer Parameter Optimization Using Latent Representations](https://mlsanity.com/paper/2507.02365/) (2025)
- [Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization](https://mlsanity.com/paper/2603.04768/) (2026)

## Abstract (arXiv)

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.