Rate Splitting for MIMO Wireless Networks: A Promising PHY-Layer Strategy for LTE Evolution
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Abstract (arXiv)
MIMO processing plays a central part towards the recent increase in spectral and energy efficiencies of wireless networks. MIMO has grown beyond the original point-to-point channel and nowadays refers to a diverse range of centralized and distributed deployments. The fundamental bottleneck towards enormous spectral and energy efficiency benefits in multiuser MIMO networks lies in a huge demand for accurate channel state information at the transmitter (CSIT). This has become increasingly difficult to satisfy due to the increasing number of antennas and access points in next generation wireless networks relying on dense heterogeneous networks and transmitters equipped with a large number of antennas. CSIT inaccuracy results in a multi-user interference problem that is the primary bottleneck of MIMO wireless networks. Looking backward, the problem has been to strive to apply techniques designed for perfect CSIT to scenarios with imperfect CSIT. In this paper, we depart from this conventional approach and introduce the readers to a promising strategy based on rate-splitting. Rate-splitting relies on the transmission of common and private messages and is shown to provide significant benefits in terms of spectral and energy efficiencies, reliability and CSI feedback overhead reduction over conventional strategies used in LTE-A and exclusively relying on private message transmissions. Open problems, impact on standard specifications and operational challenges are also discussed.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2.
4 papers.
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2024Modulation and Coding for NOMA and RSMAcited by 4
- 2022Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Accesscited by 3
- 2024Meta-Learning Based Optimization for Large Scale Wireless Systemscited by 1
- 2024Modulation and Coding for NOMA and RSMA30 Sep 2024
- 2024Meta-Learning Based Optimization for Large Scale Wireless Systems1 Jul 2024
- 2022Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access2 May 2022
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications24 Nov 2021