Joint Beamforming Optimization for User-Centric Multi-Satellite Systems With Backhaul Constraints

Interference cancellation based on spectrum sharing is a key solution to improve the network performance of multi-satellite systems. In this paper, we investigate a user-centric multi-satellite communication system where users are collaboratively served by multiple satellites. In this system, the integrated access and backhaul (IAB) networks, i.e., the satellite-to-user access network and the gateway-to-satellite backhaul network, are jointly considered. Our objective is to maximize the weighted sum rates (WSR) of all users by jointly optimizing the beamformers of multiple satellites and the gateway while considering the backhaul link constraints. To solve this non-convex optimization problem, a centralized algorithm is developed using the block coordinate update (BCU) method, assuming that global channel state information (CSI) is perfectly known. In addition, a low-complexity distributed algorithm based on multi-agent deep reinforcement learning (MA-DRL) is further proposed. This approach offers enhanced flexibility for implementation in multi-satellite systems and adaptability to dynamic environments. Simulation results demonstrate that the proposed distributed algorithm based on MA-DRL can achieve a performance almost equivalent to that of the centralized algorithm. It also reveals that the spatial diversity can be fully exploited through the joint beamforming of a multi-satellite system compared with a single-satellite system, while the growing number of satellites makes the backhaul constraint a more critical bottleneck for system capacity.