🤖 AI Summary
This study addresses the performance degradation of OTFS massive MIMO beamforming caused by channel state information (CSI) uncertainty in high-mobility satellite communications. To tackle this issue, a robust optimization framework based on support vector clustering (SVC) is proposed. By integrating three-dimensional discrete dipole approximation (DDA) channel representation with data-driven SVC to construct asymmetric uncertainty sets, the chance constraints are reformulated as deterministic semidefinite programming problems. Furthermore, a GPU-accelerated alternating direction method of multipliers (ADMM) algorithm is designed for efficient computation. Simulation results demonstrate that the proposed scheme significantly reduces transmit power while enhancing system energy efficiency and robustness. The GPU acceleration achieves substantial computational speedups, thereby validating the overall effectiveness of the proposed framework.
📝 Abstract
This paper focuses on robust beamforming for orthogonal time frequency space (OTFS)-enabled massive multiple-input multiple-output (MIMO) systems under channel state information (CSI) uncertainty. In high-mobility satellite communications, uncertain CSI severely degrades beamforming accuracy and poses a major challenge to meeting users'quality of service (QoS) requirements. To address this challenge, we first formulate a chance-constrained optimization problem aiming to minimize the total transmit power while guaranteeing a predefined outage probability. Building on a 3D delay-Doppler-angle (DDA) channel representation, we propose a data-driven approach using support vector clustering (SVC) to model the uncertain CSI as an asymmetric uncertainty set. We then derive a robust counterpart that reformulates the intractable chance constraints into a deterministic semidefinite program. Finally, we design a graphics processing unit (GPU)-accelerated parallelizable alternating direction method of multipliers (ADMM) algorithm to address the computational complexity of large-scale antenna arrays. Simulation results show that the proposed SVC-based design reduces transmit power compared with conventional beamforming schemes, and that the GPU-accelerated ADMM achieves a speedup. These results confirm that the proposed framework achieves improved robustness, energy efficiency, and efficient computation in dynamic massive MIMO networks.