# Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes

arXiv: https://arxiv.org/abs/1807.00747 · HTML: https://mlsanity.com/paper/1807.00747/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-Implementation](https://mlsanity.com/paper/2304.05042/) (2023)
- [Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS](https://mlsanity.com/paper/2602.20361/) (2026)

## Abstract (arXiv)

We demonstrate that error correcting codes (ECCs) can be used to construct a labeled data set for finetuning of "trainable" communication systems without sacrificing resources for the transmission of known symbols. This enables adaptive systems, which can be trained on-the-fly to compensate for slow fluctuations in channel conditions or varying hardware impairments. We examine the influence of corrupted training data and show that it is crucial to train based on correct labels. The proposed method can be applied to fully end-to-end trained communication systems (autoencoders) as well as systems with only some trainable components. This is exemplified by extending a conventional OFDM system with a trainable pre-equalizer neural network (NN) that can be optimized at run time.