Rate Splitting for General Multicast
| Autor: | Lingzhi Zhao, Ying Cui, Sheng Yang, Shlomo Shamai Shitz, Yunbo Han, Yunfei Zhang |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Information Theory Information Theory (cs.IT) ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Computer Science::Multimedia Computer Science::Networking and Internet Architecture Data_CODINGANDINFORMATIONTHEORY Computer Science::Information Theory |
| DOI: | 10.48550/arxiv.2201.07795 |
| Popis: | Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates the optimization of general rate splitting with linear beamforming for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. Linear beamforming is adopted at the BS, and joint decoding is adopted at each user. We consider the maximization of the weighted sum rate, which is a challenging nonconvex problem. Then, we propose an iterative algorithm for the problem to obtain a KKT point using the concave-convex procedure (CCCP). The proposed optimization framework generalizes the existing ones for rate splitting for various types of services. Finally, we numerically show substantial gains of the proposed solutions over existing schemes and reveal the design insights of general rate splitting for general multicast. Comment: 6 pages, 7 figures, to appear in IEEE ICC 2022. arXiv admin note: substantial text overlap with arXiv:2201.07386 |
| Databáze: | OpenAIRE |
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