Cluster-Aware Two-Stage Method for Fast Iterative MIMO Detection in LEO Satellite Communications

TL;DR

This paper introduces a cluster-aware two-stage MIMO detection method for LEO satellite communications that significantly accelerates convergence by exploiting intra-cluster channel correlations, improving efficiency and robustness.

Contribution

It presents a novel two-stage detection approach leveraging intra-cluster correlations to enhance convergence speed in satellite MIMO systems, a significant advancement over traditional methods.

Findings

Achieves over 12 times faster convergence with perfect CSI.

Maintains 9 times faster convergence with channel estimation errors.

Demonstrates robustness and efficiency in satellite MIMO detection.

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.