Multicast Beamforming Optimization in Cloud-Based Heterogeneous Terrestrial and Satellite Networks

Abstract

A cloud-based terrestrial-satellite network (CTSN) is conceived for supporting ubiquitous high-speed multimedia services. In the CTSN, the satellite and terrestrial base stations are connected to a cloud-computing based centralized processor (CP), where joint user scheduling and multicast beamforming are performed based on realistic imperfect channel state information (CSI). Specifically, pilot-assisted channel estimation is assumed. Then, we propose a successive convex approximation (SCA) based algorithm for generating the beamforming vectors at the CP, where specific quality of service (QoS) constraints are considered. In the proposed algorithm, the beamforming vectors are obtained by iteratively solving a convex optimization problem subject to tight convex constraints. We demonstrate that feasible solutions can be obtained by our algorithm, even for the case when the system's dimension is large. Both analytical and numerical results are provided for characterizing the performance of the CTSN. Our results qualify the tradeoff between the cooperation-aided multiantenna gain and the pilot overhead imposed by training the beamformers. Furthermore, the achievable rate of the CTSN is shown to be substantially eroded by the accuracy of CSI.