Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA

By Yijie Mao, Bruno Clerckx, Victor O. K. Li. EURASIP Journal on Wireless Communications and Networking, 2017

676 citations recorded by the source corpus.

Space-division multiple access (SDMA) utilizes linear precoding to separate users in the spatial domain and relies on fully treating any residual multi-user interference as noise. Non-orthogonal multiple access (NOMA) uses linearly precoded superposition coding with successive interference cancellation (SIC) to superpose users in the power domain and relies on user grouping and ordering to enforce some users to fully decode and cancel interference created by other users. In this paper, we argue that to efficiently cope with the high throughput, heterogeneity of quality of service (QoS), and massive connectivity requirements of future multi-antenna wireless networks, multiple access design needs to depart from those two extreme interference management strategies, namely fully treat interference as noise (as in SDMA) and fully decode interference (as in NOMA). Considering a multiple-input single-output broadcast channel, we develop a novel multiple access framework, called rate-splitting multiple access (RSMA). RSMA is a more general and more powerful multiple access for downlink multi-antenna systems that contains SDMA and NOMA as special cases. RSMA relies on linearly precoded rate-splitting with SIC to decode part of the interference and treat the remaining part of the interference as noise. This capability of RSMA to partially decode interference and partially treat interference as noise enables to softly bridge the two extremes of fully decoding interference and treating interference as noise and provides room for rate and QoS enhancements and complexity reduction. The three multiple access schemes are compared, and extensive numerical results show that RSMA provides a smooth transition between SDMA and NOMA and outperforms them both in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths, and qualities of channel state information at the transmitter). Moreover, RSMA provides rate and QoS enhancements over NOMA at a lower computational complexity for the transmit scheduler and the receivers (number of SIC layers).

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0bcc8d0e3fa8f71601ab2cfe9e353ed9abf0ac50

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Citations in the corpus

A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs (2024, methodology): “In cases where the access point is equipped with multiple antennas, RSMA may be more efficient in some cases [71].”
Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis (2019, methodology): “5 but now the Weighted Minimum Mean Square Error (WMMSE) precoding optimization framework for RS developed in [3], [7] is adopted.”
Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends (2022, methodology): “The classical approach for dealing with this practical limitation of imperfect CSIT takes a “robustification” stance where the precoders for SDMA and NOMA that have been designed under the assumption of perfect CSIT are tweaked to account for imperfect CSIT [10], [18], [26].”
Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs (2018, background): “Such a rate-splitting strategy has been shown to outperform conventional linear precoding in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths and qualities of Channel State Information at the Transmitte”
A Primer on Rate-Splitting Multiple Access: Tutorial, Myths, and Frequently Asked Questions (2022, background): “This calls for MA schemes that depart from those two extreme interference management strategies, namely fully treat interference as noise and fully decode interference [8].”