Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends
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Abstract (arXiv)
Rate-splitting multiple access (RSMA) has emerged as a novel, general, and powerful framework for the design and optimization of non-orthogonal transmission, multiple access (MA), and interference management strategies for future wireless networks. Through information and communication theoretic analysis, RSMA has been shown to be optimal (from a Degrees-of-Freedom region perspective) in several transmission scenarios. Compared to the conventional MA strategies used in 5G, RSMA enables spectral efficiency (SE), energy efficiency (EE), coverage, user fairness, reliability, and quality of service (QoS) enhancements for a wide range of network loads (including both underloaded and overloaded regimes) and user channel conditions. Furthermore, it enjoys a higher robustness against imperfect channel state information at the transmitter (CSIT) and entails lower feedback overhead and complexity. Despite its great potential to fundamentally change the physical (PHY) layer and media access control (MAC) layer of wireless communication networks, RSMA is still confronted with many challenges on the road towards standardization. In this paper, we present the first comprehensive overview on RSMA by providing a survey of the pertinent state-of-the-art research, detailing its architecture, taxonomy, and various appealing applications, as well as comparing with existing MA schemes in terms of their overall frameworks, performance, and complexities. An in-depth discussion of future RSMA research challenges is also provided to inspire future research on RSMA-aided wireless communication for beyond 5G systems.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jan 2022.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 4; 2025: 2; 2026: 1.
11 papers.
- 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
- 2022Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMOcited by 4
- 2024Modulation and Coding for NOMA and RSMAcited by 4
- 2022Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Accesscited by 3
- 2024Prompt-Assisted Semantic Interference Cancellation on Moderate Interference Channelscited by 2
- 2022Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmissioncited by 1
- 2023A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Accesscited by 1
- 2024Meta-Learning Based Optimization for Large Scale Wireless Systemscited by 1
- 2025A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISscited by 1
- 2025LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networkscited by 1
- 2026Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learningcited by 0
- 2026Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning8 Sep 2026
- 2025LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks23 Jul 2025
- 2025A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs7 May 2025
- 2024Modulation and Coding for NOMA and RSMA30 Sep 2024
- 2024Prompt-Assisted Semantic Interference Cancellation on Moderate Interference Channels8 Aug 2024
- 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions5 Jul 2024
- 2024Meta-Learning Based Optimization for Large Scale Wireless Systems1 Jul 2024
- 2023A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access17 Jul 2023
- 2022Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO18 Sep 2022
- 2022Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission3 Jun 2022
- 2022Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access2 May 2022