# MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes

arXiv: https://arxiv.org/abs/2609.00715 · HTML: https://mlsanity.com/paper/2609.00715/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels](https://mlsanity.com/paper/1911.03038/) (2019)
- [Deepcode: Feedback Codes via Deep Learning](https://mlsanity.com/paper/1807.00801/) (2018)
- [Hyper-Graph-Network Decoders for Block Codes](https://mlsanity.com/paper/1909.09036/) (2019)
- [CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes](https://mlsanity.com/paper/2405.01033/) (2024)
- [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)
- [AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications](https://mlsanity.com/paper/2205.14955/) (2022)
- [Deep Learning for Radio Resource Allocation in Multi-Cell Networks](https://mlsanity.com/paper/1808.00667/) (2018)
- [Key Focus Areas and Enabling Technologies for 6G](https://mlsanity.com/paper/2412.07029/) (2024)
- [DRF Codes: Deep SNR-Robust Feedback Codes](https://mlsanity.com/paper/2112.11789/) (2021)
- [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)
- [LightCode: Light Analytical and Neural Codes for Channels with Feedback](https://mlsanity.com/paper/2403.10751/) (2024)
- [Accelerating Error Correction Code Transformers](https://mlsanity.com/paper/2410.05911/) (2024)
- [Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches](https://mlsanity.com/paper/2401.00477/) (2023)
- [Waveform Learning under Phase Noise Impairment for Sub-THz Communications](https://mlsanity.com/paper/2406.18578/) (2024)
- [Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis](https://mlsanity.com/paper/2504.15514/) (2025)
- [Transformer-Based Decoding in Concatenated Coding Schemes Under Synchronization Errors](https://mlsanity.com/paper/2511.00999/) (2025)

## Abstract (arXiv)

Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are usually achieved in idealized settings with perfect feedback. Over the last few years, machine learning-based schemes have been shown to be promising solutions for implementing feedback-based codes, particularly when combined with short-block-length open-loop error correcting codes (ECCs) in a concatenated coding structure. However, existing ML-based feedback schemes remain agnostic to the outer code's structure, potentially misallocating feedback resources on error patterns already correctable by the outer ECC. To address this, we propose MaskCode, a Transformer-based inner feedback code for concatenated coding systems, which explicitly incorporates structural knowledge of the outer linear block code into the inner feedback encoder design via two synergistic mechanisms: 1) a soft syndrome-based input that informs the encoder about potential parity constraint violations, and 2) a code-aware attention mask derived from the Tanner graph. We further show that end-to-end training with a differentiable belief propagation (BP) decoder offers no additional gain, as MaskCode's structure-aware design already internalizes the structural knowledge of the outer code; in fact, backpropagation through the iterative BP decoder introduces gradient explosion, which degrades rather than improves performance. Extensive evaluations on BCH and LDPC outer codes demonstrate that MaskCode consistently outperforms all baselines, achieving up to 1.5 dB SNR gain.