# Structured Superposition of Autoencoders for UEP Codes at Intermediate Blocklengths

arXiv: https://arxiv.org/abs/2508.07487 · HTML: https://mlsanity.com/paper/2508.07487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning Based Successive Interference Cancellation for the Non-Orthogonal Downlink](https://mlsanity.com/paper/2207.14468/) (2022)
- [Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication](https://mlsanity.com/paper/2203.07703/) (2022)
- [A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design](https://mlsanity.com/paper/2306.13423/) (2023)
- [Joint Source-Channel Coding System for 6G Communication: Design, Prototype and Future Directions](https://mlsanity.com/paper/2310.01024/) (2023)

## Built on by (most cited first, 1 of 1)

- [Learning Binary Autoencoder-Based Codes with Progressive Training](https://mlsanity.com/paper/2511.09221/) (2025)

## Abstract (arXiv)

Unequal error protection (UEP) coding that enables differentiated reliability levels within a transmitted message is essential for modern communication systems. Autoencoder (AE)-based code designs have shown promise in the context of learned equal error protection (EEP) coding schemes. However, their application to UEP remains largely unexplored, particularly at intermediate blocklengths, due to the increasing complexity of AE-based models. Inspired by the proven effectiveness of superposition coding and successive interference cancellation (SIC) decoding in conventional UEP schemes, we propose a structured AE-based architecture that extends AE-based UEP codes to substantially larger blocklengths while maintaining efficient training. By structuring encoding and decoding into smaller AE subblocks, our method provides a flexible framework for fine-tuning UEP reliability levels while adapting to diverse system parameters. Numerical results show that the proposed approach improves over established achievability bounds of randomized superposition coding-based UEP schemes with SIC decoding, making the proposed structured AE-based UEP codes a scalable and efficient solution for next-generation networks.