# Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis

arXiv: https://arxiv.org/abs/1906.04474 · HTML: https://mlsanity.com/paper/1906.04474/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access](https://mlsanity.com/paper/2205.00849/) (2022)
- [Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach](https://mlsanity.com/paper/2306.12977/) (2023)
- [A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access](https://mlsanity.com/paper/2307.08822/) (2023)
- [Meta-Learning Based Optimization for Large Scale Wireless Systems](https://mlsanity.com/paper/2407.01823/) (2024)

## Abstract (arXiv)

Considering a two-user multi-antenna Broadcast Channel, this paper shows that linearly precoded Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers is a flexible framework for non-orthogonal transmission that generalizes, and subsumes as special cases, four seemingly different strategies, namely Space Division Multiple Access (SDMA) based on linear precoding, Orthogonal Multiple Access (OMA), Non- Orthogonal Multiple Access (NOMA) based on linearly precoded superposition coding with SIC, and physical-layer multicasting. The paper studies the sum-rate and shows analytically how RS unifies, outperforms, and specializes to SDMA, OMA, NOMA, and multicasting as a function of the disparity of the channel strengths and the angle between the user channel directions.