GPU加速的OTFS大规模MIMO在信道状态信息不确定性下的鲁棒波束成形
GPU-Accelerated Robust Beamforming for OTFS Massive MIMO with CSI Uncertainty
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中文总结 AI 辅助
本文针对OTFS大规模MIMO在CSI不确定性下的鲁棒波束成形，提出基于SVC的数据驱动方法建模不确定性，并设计GPU加速ADMM算法，实现低功耗、高鲁棒与高效计算。
中文摘要 AI 辅助
本文关注在信道状态信息（CSI）不确定性下，基于正交时频空间（OTFS）的大规模多输入多输出（MIMO）系统的鲁棒波束成形问题。在高移动性卫星通信中，不确定的CSI严重降低波束成形的准确性，并对满足用户服务质量（QoS）要求构成重大挑战。为解决这一挑战，我们首先制定一个机会约束优化问题，旨在最小化总发射功率，同时保证预定的中断概率。基于3D时延-多普勒-角度（DDA）信道表示，我们提出一种使用支持向量聚类（SVC）的数据驱动方法，将不确定CSI建模为非对称不确定性集。然后，我们推导出一个鲁棒对应形式，将难以处理的机会约束重述为确定性半定规划。最后，我们设计了一种GPU加速的可并行交替方向乘子法（ADMM）算法，以应对大规模天线阵列的计算复杂度。仿真结果表明，与传统波束成形方案相比，所提出的基于SVC的设计降低了发射功率，且GPU加速的ADMM实现了加速效果。这些结果证实，所提出的框架在动态大规模MIMO网络中实现了更高的鲁棒性、能效和高效计算。
英文摘要
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.
发表机构
- School of Physics and Information Technology, Shaanxi Normal University（陕西师范大学物理与信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。
