Energy Efficiency of Rate-Splitting Multiple Access, and Performance Benefits over SDMA and NOMA

Abstract

Rate-Splitting Multiple Access (RSMA) is a general and powerful multiple access framework for downlink multiantenna systems, and contains Space-Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA) as special cases. RSMA relies on linearly precoded rate-splitting with Successive Interference Cancellation (SIC) to decode part of the interference and treat the remaining part of the interference as noise. Recently, RSMA has been shown to outperform both SDMA and NOMA rate-wise 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 was shown to provide spectral efficiency and QoS enhancements over NOMA at a lower computational complexity for the transmit scheduler and the receivers. In this paper, we build upon those results and investigate the energy efficiency of RSMA compared to SDMA and NOMA. Considering a multiple-input single-output broadcast channel, we show that RSMA is more energy-efficient than SDMA and NOMA in a wide range of user deployments (with a diversity of channel directions and channel strengths). We conclude that RSMA is more spectrally and energy-efficient than SDMA and NOMA.

Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission: Spectral and Energy Efficiency Analysis

“…The generalized RS requires a higher encoding and decoding complexity than MU-LP and NOMA since the multiple common streams are required to be encoded on top of the private streams. The observations in this paper confirm the superiority of RS over MU-LP, Orthogonal Multiple Access (OMA) where the unicast stream is only intended for a single user, and NOMA in NOUM transmissions, and complement our previous findings in [14], [17], [25], [35] that have shown the superiority of RS in unicast-only and multicast-only transmissions.…”

“…The key benefit of 1-layer RS in the NOUM transmission is the fact that 1layer RS does not lead to any complexity increase for the receivers compared to conventional MU-LP-assisted NOUM since one layer of SIC is required to separate multicast stream from unicast streams. This contrasts with unicast-only and muticast-only transmissions where 1-layer RS was found beneficial over MU-LP in [14], [17], [25] but at the cost of a receiver complexity increase due to the need of SIC for RS to operate. To the best of our knowledge, this is the first work that applies RS to the NOUM transmissions.…”

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

“…Linearly precoded Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers has recently appeared as a powerful non-orthogonal transmission and robust interference management strategy for multi-antenna wireless networks [1]. Though originally introduced for the two-user Single-Input Single-Output Interference Channel (IC) in [2], RS has become an underpinning communication-theoretic strategy to tackle modern interference-related problems and has recently been successfully investigated in several Multiple-Input Single-Output (MISO) Broadcast Channel (BC) settings, namely, unicast-only transmission with perfect Channel State Information at the Transmitter (CSIT) [3], [4] and imperfect CSIT [5]- [13], (multigroup) multicast-only transmission [14], as well as superimposed unicast and multicast transmission [15]. Results highlight that RS provides significant benefits in terms of spectral efficiency [3], [6], [7], [9], [13]- [15], energy efficiency [4], robustness [8], and CSI feedback overhead reduction [6], [12] over conventional strategies used in LTE-A/5G that rely on fully treating interference as noise (e.g.…”

Energy Efficiency of RSMA and NOMA in Cellular-Connected mmWave UAV Networks

“…Thus, we need to come up with an approximate algorithm to solve these optimization problems efficiently. As in [16], we adopt the SCA algorithm [17] as a good match for this problem. The algorithm proposes to approximate the non-convex objective function (or the functions in the constraints) using the first-order Taylor expansion around a given initial point.…”