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Rate-splitting multiple access (RSMA) has the potential to realize the goals of high spectral and energy efficiency, massive connectivity, ultra-reliability, and heterogeneity of quality-of-service (QoS) envisioned in 6G. By exploiting the splitting of user messages into common and private components, RSMA can softly bridge the divide between the common interference management strategies of fully decoding interference and treating interference as noise. Recent research progress has shown that RSMA generalizes and subsumes as special cases four existing MA schemes, namely, orthogonal multiple access (OMA), physical-layer multicasting, space division multiple access (SDMA) based on linear precoding (currently adopted in 5G), and non-orthogonal multiple access (NOMA) based on linearly precoded superposition coding with successive interference cancellation (SIC). It achieves the optimal spatial multiplexing gain in a number of scenarios and provides significant room for improving spectral efficiency, energy efficiency, coverage, user fairness, reliability, QoS enhancements in a wide range of network loads and user deployments, robustness against imperfect channel state information at the transmitter (CSIT), as well as feedback overhead and complexity reduction over conventional strategies used in 5G. Moreover, RSMA decreases transmit power consumption under certain QoS constraints, reduces latency and enhances robustness under user mobility, which makes it an excellent enabling and sustainable technology for 6G. It has been recognized as a promising PHY-layer transmission paradigm for non-orthogonal transmission, interference management, and multiple access in 6G. RSMA has the potential to fundamentally transform the PHY layer and lower MAC layer design of wireless communication networks, and ultimately, pave the way for the development of future green communication networks.

This Special Issue focuses on the signal processing advances for “Rate-Splitting Multiple Access” and its applications in future green communication networks. It aims at bringing together researchers from both academia and industry to share their original works on RSMA. Prospective authors are invited to submit original manuscripts on topics including, but not limited to:

• Energy-efficient transceiver design for green RSMA
• Optimization algorithms for green RSMA
• Energy-efficient signal processing for green RSMA
• Unfolding/unrolling, model-free and model-based learning techniques for green RSMA
• Coding and modulation for green RSMA
• Energy-efficient cross-layer design for green RSMA
• RSMA-based green interference management
• RSMA in green multi-user/multi-cell/cell-free multi-antenna networks such as MU-MIMO, massive MIMO, networked MIMO
• Channel estimation for RSMA-enabled green wireless networks
• Green RSMA in 6G services such as enhanced eMBB, enhanced URLLC, massive URLLC, enhanced MTC, massive MTC, human-centric services
• Green RSMA-enabled intelligent reflecting surfaces (IRS), relaying, cooperative communications, cloudenabled platforms/radio access networks (cloud/fog/caching), cognitive radio, etc 
• Green RSMA in integrated space–air–ground networks, integrated sensing and communications, wireless powered communications, vehicle-to-everything (V2X), etc
• Green RSMA-enabled millimetre wave/terahertz (THz)/visible light communications
• Green RSMA-enabled semantic communications/distributed edge learning
• Green RSMA-enabled virtual reality (VR), augmented reality (AR), extended reality (XR), and metaverse communications
• Prototyping and experimental trails of green RSMA 

Submission Guidelines

Prospective authors should follow the instructions given on the IEEE TGCN Information for Authors page and submit their manuscript with the web submission system on Manuscript Central.

Important Dates

Manuscript Submission: 15 February 2024 (Extended Deadline)
First Decision: 15 February 2024
Revised Manuscript Due: 15 March 2024
Final Decision: 15 April 2024
Final Manuscript Due: 25 April 2024
Publication Date: June 2024

Guest Editors

Yijie (Lina) Mao
ShanghaiTech University, China

Derrick Wing Kwan Ng
University of New South Wales, Australia

Wolfgang Utschick
Technical University of Munich, Germany

Bruno Clerckx
Imperial College London, UK, Silicon Austria Labs, Austria

Ying Cui
The Hong Kong University of Science and Technology (Guangzhou), China

Timothy N. Davidson
McMaster University, Canada