Beyond Dirty Paper Coding for Multi-Antenna Broadcast Channel with Partial CSIT: A Rate-Splitting Approach
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Abstract (arXiv)
Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of the multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of the multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate regions than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel achievable scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region achieved by DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies. The proposed DPCRS acts as a new benchmark and the best-known achievable strategy for multi-antenna BC with partial CSIT.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2019.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 2.
5 papers.
- 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
- 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
- 2024Modulation and Coding for NOMA and RSMA30 Sep 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
- 2022Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access2 May 2022