# Interference-Aware Super-Constellation Design for NOMA

arXiv: https://arxiv.org/abs/2503.07509 · HTML: https://mlsanity.com/paper/2503.07509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 10 Mar 2025.

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

- [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)
- [SVD-Embedded Deep Autoencoder for MIMO Communications](https://mlsanity.com/paper/2111.02359/) (2021)
- [A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design](https://mlsanity.com/paper/2306.13423/) (2023)
- [Modulation and Coding for NOMA and RSMA](https://mlsanity.com/paper/2410.00239/) (2024)
- [Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI](https://mlsanity.com/paper/2310.15027/) (2023)

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

- [End-to-End NOMA with Perfect and Quantized CSI Over Rayleigh Fading Channels](https://mlsanity.com/paper/2602.13446/) (2026)

## Abstract (arXiv)

Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique. However, its implementation with finite-alphabet inputs faces challenges. Particularly, due to inter-user interference, superimposed constellations may have overlapping symbols leading to high bit error rates when successive interference cancellation (SIC) is applied. To tackle the issue, this paper employs autoencoders to design interference-aware super-constellations. Unlike conventional methods where superimposed constellation may have overlapping symbols, the proposed autoencoder-based NOMA (AE-NOMA) is trained to design super-constellations with distinguishable symbols at receivers, regardless of channel gains. The proposed architecture removes the need for SIC, allowing maximum likelihood-based approaches to be used instead. The paper presents the conceptual architecture, loss functions, and training strategies for AE-NOMA. Various test results are provided to demonstrate the effectiveness of interference-aware constellations in improving the bit error rate, indicating the adaptability of AE-NOMA to different channel scenarios and its promising potential for implementing NOMA systems