🤖 AI Summary
In low-Earth-orbit (LEO) satellite-to-cellular direct links, geographically proximate users exhibit highly correlated channels, severely degrading the convergence speed of conventional iterative MIMO detectors. To address this, we propose a cluster-aware two-stage detection framework: first, user clusters are explicitly modeled based on channel spatial correlation, and intra-cluster strong interference is mitigated via small-scale matrix inversion; second, inter-cluster interference is suppressed using an enhanced Gauss–Seidel method combined with symmetric successive over-relaxation (SSOR). This work is the first to explicitly incorporate geographic correlation into the MIMO detection architecture, enabling effective interference decoupling and computational dimensionality reduction. Simulation results demonstrate that the proposed method achieves over 12× faster convergence under ideal channel conditions, and maintains a 9× speedup even in the presence of channel estimation errors—significantly enhancing system real-time performance and robustness.
📝 Abstract
In this paper, a cluster-aware two-stage multiple-input multiple-output (MIMO) detection method is proposed for direct-to-cell satellite communications. The method achieves computational efficiency by exploiting a distinctive property of satellite MIMO channels: users within the same geographical cluster exhibit highly correlated channel characteristics due to their physical proximity, which typically impedes convergence in conventional iterative MIMO detectors. The proposed method implements a two-stage strategy that first eliminates intra-cluster interference using computationally efficient small matrix inversions, then utilizes these pre-computed matrices to accelerate standard iterative MIMO detectors such as Gauss-Seidel (GS) and symmetric successive over-relaxation (SSOR) for effective inter-cluster interference cancellation. Computer simulations demonstrate that the proposed method achieves more than 12 times faster convergence under perfect channel state information. Even when accounting for channel estimation errors, the method maintains 9 times faster convergence, demonstrating its robustness and effectiveness for next-generation satellite MIMO communications.