4.7 Article

An Optimization Framework for General Rate Splitting for General Multicast

期刊

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 22, 期 3, 页码 1573-1587

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2022.3205508

关键词

Unicast; Streaming media; Fading channels; Optimization; Array signal processing; Interference; Decoding; General multicast; general rate splitting; linear beamforming; joint decoding; optimization; concave-convex procedure (CCCP); stochastic successive convex approximation (SSCA)

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This paper investigates general rate splitting for general multicast in a multi-carrier single-cell wireless network. The authors propose an iterative algorithm to solve the nonconvex stochastic problem, and also propose low-complexity iterative algorithms for the fast fading scenario. Numerical analysis demonstrates the substantial gains of the proposed solutions over existing schemes and reveals insights for the design of general rate splitting.
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 general rate splitting 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. We consider linear beamforming at the BS and joint decoding at each user in the slow fading and fast fading scenarios. In the slow fading scenario, we consider the maximization of the weighted sum average rate, which is a challenging nonconvex stochastic problem with numerous variables. To reduce computational complexity, we decouple the original nonconvex stochastic problem into multiple nonconvex deterministic problems, one for each system channel state. Then, we propose an iterative algorithm for each deterministic problem to obtain a Karush-Kuhn-Tucker (KKT) point using the concave-convex procedure (CCCP). In the fast fading scenario, we consider the maximization of the weighted sum ergodic rate. This problem is more challenging than the one for the slow fading scenario, as it is not separable. First, we propose a stochastic iterative algorithm to obtain a KKT point using stochastic successive convex approximation (SSCA) and the exact penalty method. Then, we propose two low-complexity iterative algorithms to obtain feasible points with promising performance for two cases of channel distributions using approximation and 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 in both scenarios and reveal the design insights of general rate splitting for general multicast.

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