# Learning Robust Representations for Communications over Interference-limited Channels

arXiv: https://arxiv.org/abs/2410.19767 · HTML: https://mlsanity.com/paper/2410.19767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 13 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Interference-Aware Constellation Design for Z-Interference Channels with Imperfect CSI](https://mlsanity.com/paper/2303.08312/) (2023)
- [Deep Learning Based Near-Orthogonal Superposition Code for Short Message Transmission](https://mlsanity.com/paper/2111.03263/) (2021)
- [Learning Robust Representations for Communications over Noisy Channels](https://mlsanity.com/paper/2409.01129/) (2024)

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

- [Deep Learning-Based Multi-User Communication Design for Dense IoT Networks: Interference-Aware Finite-Blocklength Communication and Preliminary MIMO Extensions](https://mlsanity.com/paper/2608.22923/) (2026)

## Abstract (arXiv)

In the context of cellular networks, users located at the periphery of cells are particularly vulnerable to substantial interference from neighbouring cells, which can be represented as a two-user interference channel. This study introduces two highly effective methodologies, namely TwinNet and SiameseNet, using autoencoders, tailored for the design of encoders and decoders for block transmission and detection in interference-limited environments. The findings unambiguously illustrate that the developed models are capable of leveraging the interference structure to outperform traditional methods reliant on complete orthogonality. While it is recognized that systems employing coordinated transmissions and independent detection can offer greater capacity, the specific gains of data-driven models have not been thoroughly quantified or elucidated. This paper conducts an analysis to demonstrate the quantifiable advantages of such models in particular scenarios. Additionally, a comprehensive examination of the characteristics of codewords generated by these models is provided to offer a more intuitive comprehension of how these models achieve superior performance.