Summary
High-speed satellite communications often struggle because the system can't always know the exact state of the connection, which makes sending signals accurately hard. The authors provide a method that carefully plans how to send signals using a 3D model of the wireless channel while accounting for uncertainty. They use a smart approach called support vector clustering to understand this uncertainty better and turn the problem into one easier to solve. They also create a way to run the computations faster using a graphics processor, making the method practical for large antenna arrays. Their results show that this method saves energy and improves connection reliability in fast-moving environments.
What this means in practice
- •For satellite network engineers: Implement signal transmission designs that maintain connection quality despite channel uncertainties in high-speed satellite settings.
- •For wireless infrastructure designers: Use GPU-accelerated algorithms to efficiently compute robust beamforming for large antenna arrays in mobile wireless systems.
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.