Cooperative Rate-Splitting for Downlink Multiuser MISO Systems With Partial CSIT

Multiple antenna technology and cooperative relaying are two efficient methods to improve the capacity and reliability of wireless networks. By marrying cooperative relaying with a Rate-Splitting (RS) approach, Cooperative Rate-Splitting (CRS) shows certain benefits compared to several non-cooperative and cooperative schemes in multi-antenna Broadcast Channel (BC). In this paper, we further investigate the CRS with partial Channel State Information at the Transmitter (CSIT), as partial CSIT is an inevitable issue in practical downlink Multi-user Multiple Input Single Output (MU-MISO) systems. The proposed CRS structure includes multiple users, and one user could be selected as a relay to help the other users. With the flexible design, CRS leverages the benefits of user cooperation and relay (or user) selection for cooperative network, and also retains the superiority of RS for multi-antenna BC. Considering the Weighted Average Sum Rate (WASR) performance, we aim to solve the stochastic optimization that jointly involves the precoder design, the time resource allocation, and the relay selection issue. To address the above non-convex problem, a dedicated algorithm for CRS that exploits the Weighted Minimum Mean Square Error (WMMSE) approach and the Convex-Concave Procedure (CCP) is proposed, such that the WASR is maximized by jointly updating the precoding matrix and time allocation factor according to the incoming user channel state estimate. The proposed algorithm then enables us to solve the relay selection through an exhaustive search. Since the optimal relay selection algorithm has a high computational complexity when the number of users is large, we additionally provide a suboptimal heuristic algorithm to strike a balance between complexity and performance. Numerical results demonstrate that the proposed CRS scheme is capable of achieving a larger WASR than its non-cooperative counterpart and several existing beamforming schemes in a wide range of propagation conditions, and the heuristic algorithm achieves a performance close to the optimal one but with lower complexity.