# Modulation and Coding for NOMA and RSMA

arXiv: https://arxiv.org/abs/2410.00239 · HTML: https://mlsanity.com/paper/2410.00239/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 288 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 30 of 49)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel](https://mlsanity.com/paper/2211.09920/) (2022)
- [Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels](https://mlsanity.com/paper/1911.04970/) (2019)
- [An Overview of 5G Advanced Evolution in 3GPP Release 18](https://mlsanity.com/paper/2201.01358/) (2022)
- [TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade](https://mlsanity.com/paper/2112.13187/) (2021)
- [Grant-free Non-orthogonal Multiple Access for IoT: A Survey](https://mlsanity.com/paper/1910.06529/) (2019)
- [Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends](https://mlsanity.com/paper/2201.03192/) (2022)
- [Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges](https://mlsanity.com/paper/1609.06261/) (2016)
- [Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G](https://mlsanity.com/paper/2107.03059/) (2021)
- [Beyond Dirty Paper Coding for Multi-Antenna Broadcast Channel with Partial CSIT: A Rate-Splitting Approach](https://mlsanity.com/paper/1912.05409/) (2019)
- [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)
- [Rate Splitting for MIMO Wireless Networks: A Promising PHY-Layer Strategy for LTE Evolution](https://mlsanity.com/paper/1603.01217/) (2016)
- [Millimeter-Wave NOMA with User Grouping, Power Allocation and Hybrid Beamforming](https://mlsanity.com/paper/1907.12708/) (2019)
- [Rate-Splitting Multiple Access for Multi-antenna Downlink Communication Systems: Spectral and Energy Efficiency Tradeoff](https://mlsanity.com/paper/2001.03206/) (2020)
- [Benchmarking End-to-end Learning of MIMO Physical-Layer Communication](https://mlsanity.com/paper/2005.09718/) (2020)
- [Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO](https://mlsanity.com/paper/2209.08456/) (2022)
- [On the Downlink Performance of RSMA-based UAV Communications](https://mlsanity.com/paper/2011.05019/) (2020)
- [Rate-Splitting Multiple Access for Multi-Antenna Joint Radar and Communications](https://mlsanity.com/paper/2103.07914/) (2021)
- [Deep Learning-based Codebook Design for Code-domain Non-Orthogonal Multiple Access Approaching Single-User Bit Error Rate Performance](https://mlsanity.com/paper/2104.00818/) (2021)
- [Rate-Splitting Multiple Access for Uplink Massive MIMO With Electromagnetic Exposure Constraints](https://mlsanity.com/paper/2212.07028/) (2022)
- [Interference-Aware Constellation Design for Z-Interference Channels with Imperfect CSI](https://mlsanity.com/paper/2303.08312/) (2023)
- [Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges](https://mlsanity.com/paper/2305.07069/) (2023)
- [Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/1909.11074/) (2019)
- [Rate Splitting for Multi-Antenna Downlink: Precoder Design and Practical Implementation](https://mlsanity.com/paper/2002.07225/) (2020)
- [Multi-Objective DNN-based Precoder for MIMO Communications](https://mlsanity.com/paper/2007.02896/) (2020)
- [Deep Polar Codes](https://mlsanity.com/paper/2308.03004/) (2023)

## Built on by (most cited first, 4 of 4)

- [Interference-Aware Super-Constellation Design for NOMA](https://mlsanity.com/paper/2503.07509/) (2025)
- [End-to-End NOMA with Perfect and Quantized CSI Over Rayleigh Fading Channels](https://mlsanity.com/paper/2602.13446/) (2026)
- [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)
- [Quantum-Aided Active Device Detection in Energy-Harvesting Symbiotic Radio Networks](https://mlsanity.com/paper/2609.26565/) (2026)

## Abstract (arXiv)

Next-generation multiple access (NGMA) serves as an umbrella term for transmission schemes distinct from conventional orthogonal methods. A key candidate of NGMA, non-orthogonal multiple access (NOMA), emerges as a solution to enhance connectivity by allowing multiple users to share time, frequency, and space concurrently. However, NOMA faces challenges in implementation, particularly in canceling inter-user interference. In this paper, we discuss the principles behind NOMA and review conventional NOMA methods. Then, to address these challenges, we present asynchronous transmission and interference-aware modulation techniques, enabling decoding without successive interference cancellation. The goal is to design constellations that dynamically adapt to interference, minimizing bit error rates (BERs) and enhancing user throughput in the presence of inter-user, inter-carrier, and inter-cell interference. The traditional link between minimizing BER and increasing spectral efficiency is explored, with deep autoencoders for end-to-end communication emerging as a potential solution to improve BERs. Interference-aware modulation can revolutionize constellation design for non-orthogonal channels. Rate-splitting multiple access (RSMA) is another promising interference management technique in multi-user systems. In addition to addressing challenges in finite-alphabet NOMA, this paper offers new insights and provides an overview of code-domain NOMA, trellis-coded NOMA, and RSMA as key NGMA candidates. We also discuss the evolution of channel coding toward low-latency communication and examine modulation and coding schemes in 5G networks. Finally, we highlight future research directions, emphasizing their importance for realizing NOMA from concept to functional technology.