# Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis

arXiv: https://arxiv.org/abs/2504.15514 · HTML: https://mlsanity.com/paper/2504.15514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes](https://mlsanity.com/paper/2609.00715/) (2026)

## Abstract (arXiv)

Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approaches in the so-called "two-way" setting where two users jointly encode messages and feedback for each other over a shared channel. In this work, we present a general architecture for ML-based two-way feedback coding, and show how several popular one-way schemes can be converted to the two-way setting through our framework. We compare such schemes against their one-way counterparts, revealing error-rate benefits of ML-based two-way coding in certain signal-to-noise ratio (SNR) regimes. We then analyze the tradeoffs between error performance and computational overhead for three state-of-the-art neural network coding models instantiated in the two-way paradigm.