# MIND: Model Independent Neural Decoder

arXiv: https://arxiv.org/abs/1903.02268 · HTML: https://mlsanity.com/paper/1903.02268/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks](https://mlsanity.com/paper/2105.00459/) (2021)
- [A Channel Coding Benchmark for Meta-Learning](https://mlsanity.com/paper/2107.07579/) (2021)
- [Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation](https://mlsanity.com/paper/2110.00414/) (2021)
- [Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization](https://mlsanity.com/paper/2108.00785/) (2021)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)
- [Calibrating AI Models for Wireless Communications via Conformal Prediction](https://mlsanity.com/paper/2212.07775/) (2022)
- [Interpreting Training Aspects of Deep-Learned Error-Correcting Codes](https://mlsanity.com/paper/2305.04347/) (2023)
- [Decoding for Punctured Convolutional and Turbo Codes: A Deep Learning Solution for Protocols Compliance](https://mlsanity.com/paper/2502.15475/) (2025)
- [On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations](https://mlsanity.com/paper/2602.01582/) (2026)
- [Score Based Error Correcting Code Decoder](https://mlsanity.com/paper/2605.28358/) (2026)

## Abstract (arXiv)

Standard decoding approaches rely on model-based channel estimation methods to compensate for varying channel effects, which degrade in performance whenever there is a model mismatch. Recently proposed Deep learning based neural decoders address this problem by leveraging a model-free approach via gradient-based training. However, they require large amounts of data to retrain to achieve the desired adaptivity, which becomes intractable in practical systems. In this paper, we propose a new decoder: Model Independent Neural Decoder (MIND), which builds on the top of neural decoders and equips them with a fast adaptation capability to varying channels. This feature is achieved via the methodology of Model-Agnostic Meta-Learning (MAML). Here the decoder: (a) learns a "good" parameter initialization in the meta-training stage where the model is exposed to a set of archetypal channels and (b) updates the parameter with respect to the observed channel in the meta-testing phase using minimal adaptation data and pilot bits. Building on top of existing state-of-the-art neural Convolutional and Turbo decoders, MIND outperforms the static benchmarks by a large margin and shows minimal performance gap when compared to the neural (Convolutional or Turbo) decoders designed for that particular channel. In addition, MIND also shows strong learning capability for channels not exposed during the meta training phase.