MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes
Abstract
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.
Related
- Feedback Coding Enables Inference-Time Covert Agentic Communication
- Variable-Length Feedback Coding With Universal Decoding and Bandit Channel Selection
- Persistence, Exposure, and Credit Routing in Linear Temporal Predictive Coding
- Feedback Stabilization for Sampled Linear Systems with Control-linear Noise
- Linear Coding of LTI Sources Over Vector Gaussian Channels: A Majorization Approach
BibTeX
@misc{jang2026maskcode,
title = {{MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes}},
author = {Jonggyu Jang and Hongjae Nam and Vishrant Tripathi and David J. Love and Hyun Jong Yang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.00715},
eprint = {2609.00715},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.00715},
}
RIS
TY - UNPB
TI - MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes
AU - Jonggyu Jang
AU - Hongjae Nam
AU - Vishrant Tripathi
AU - David J. Love
AU - Hyun Jong Yang
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.00715
UR - https://arxiv.org/abs/2609.00715
AB - 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.
ER -