# MIND: Model Independent Neural Decoder

## TL;DR

MIND is a neural decoder that uses meta-learning to quickly adapt to different channels with minimal data, outperforming static decoders and approaching the performance of channel-specific neural decoders.

## Contribution

The paper introduces MIND, a neural decoder utilizing MAML for rapid adaptation to varying channels, reducing retraining data needs and improving robustness.

## Key findings

- MIND outperforms static neural decoders significantly.
- MIND approaches the performance of channel-specific neural decoders.
- MIND generalizes well to unseen channels.

## Abstract

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.