# Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches

arXiv: https://arxiv.org/abs/2401.00477 · HTML: https://mlsanity.com/paper/2401.00477/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Deepcode: Feedback Codes via Deep Learning](https://mlsanity.com/paper/1807.00801/) (2018)
- [Deep Extended Feedback Codes](https://mlsanity.com/paper/2105.01365/) (2021)
- [All you need is feedback: Communication with block attention feedback codes](https://mlsanity.com/paper/2206.09457/) (2022)
- [Linear Coding for AWGN channels with Noisy Output Feedback via Dynamic Programming](https://mlsanity.com/paper/2103.10807/) (2021)
- [Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning](https://mlsanity.com/paper/2304.13178/) (2023)
- [Linear Coding for Gaussian Two-Way Channels](https://mlsanity.com/paper/2210.16569/) (2022)

## 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)

Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users. We first formulate general encoding/decoding functions, where the user cooperation is captured by the coupling of user encoding processes. The coupling effect renders the encoder/decoder design non-trivial, requiring effective decoding to capture this effect, as well as efficient power management at the encoders within power constraints. To address these challenges, we propose two different two-way coding strategies: linear coding and learning-based coding. For linear coding, we propose optimal linear decoding and discuss new insights on encoding regarding user cooperation to balance reliability. We then propose an efficient algorithm for joint encoder/decoder design. For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture, where we propose interactive RNNs and a power control layer for encoding, and we incorporate bi-directional RNNs with an attention mechanism for decoding. Through simulations, we show that our two-way coding methodologies outperform conventional channel coding schemes (that do not utilize user cooperation) significantly in sum-error performance. We also demonstrate that our linear coding excels at high signal-to-noise ratios (SNRs), while our RNN-based coding performs best at low SNRs. We further investigate our two-way coding strategies in terms of power distribution, two-way coding benefit, different coding rates, and block-length gain.