# A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design

arXiv: https://arxiv.org/abs/2306.13423 · HTML: https://mlsanity.com/paper/2306.13423/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## 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 Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges](https://mlsanity.com/paper/1609.06261/) (2016)
- [Deep Learning Based Successive Interference Cancellation for the Non-Orthogonal Downlink](https://mlsanity.com/paper/2207.14468/) (2022)
- [Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems](https://mlsanity.com/paper/2002.08710/) (2020)

## Built on by (most cited first, 5 of 5)

- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [Modulation and Coding for NOMA and RSMA](https://mlsanity.com/paper/2410.00239/) (2024)
- [Interference-Aware Super-Constellation Design for NOMA](https://mlsanity.com/paper/2503.07509/) (2025)
- [Structured Superposition of Autoencoders for UEP Codes at Intermediate Blocklengths](https://mlsanity.com/paper/2508.07487/) (2025)
- [End-to-End NOMA with Perfect and Quantized CSI Over Rayleigh Fading Channels](https://mlsanity.com/paper/2602.13446/) (2026)

## Abstract (arXiv)

End-to-end design of communication systems using deep autoencoders (AEs) is gaining attention due to its flexibility and excellent performance. Besides single-user transmission, AE-based design is recently explored in multi-user setup, e.g., for designing constellations for non-orthogonal multiple access (NOMA). In this paper, we further advance the design of AE-based downlink NOMA by introducing weighted loss function in the AE training. By changing the weight coefficients, one can flexibly tune the constellation design to balance error probability of different users, without relying on explicit information about their channel quality. Combined with the SICNet decoder, we demonstrate a significant improvement in achievable levels and flexible control of error probability of different users using the proposed weighted AE-based framework.