🤖 AI Summary
This study addresses the challenge of guaranteeing user quality-of-service (QoS) in space-air-ground integrated networks (SAGINs), where channel state information (CSI) uncertainty complicates beamforming and resource allocation. To this end, a deep neural network (DNN)-driven robust joint optimization framework is proposed. Specifically, an opportunity-constrained model is first formulated, leveraging a DNN to learn asymmetric CSI uncertainty sets. Subsequently, efficient solutions are achieved by integrating a pre-trained parameter-based robust counterpart approximation with semidefinite relaxation and an adaptive iterative algorithm. Simulation results demonstrate that the proposed approach significantly outperforms conventional schemes in terms of both energy efficiency and robust reliability.
📝 Abstract
This paper focuses on the joint robust beamforming and resource allocation for space-air-ground integrated networks (SAGIN) under uncertain channel state information (CSI). In SAGIN, uncertain CSI undermines the precise adjustment of beamforming and resource allocation, posing a major challenge to meeting heterogeneous users'strict quality of service (QoS) requirements. To address this challenge, we first formulate a chance-constrained optimization problem to minimize the total transmit power while satisfying QoS requirements under a predefined outage probability. By leveraging semidefinite relaxation (SDR), the objective function is transformed into a linear function of the traces of the beamforming matrices. Then, we propose a deep neural network (DNN)-driven channel uncertainty learning to dynamically learn and model the uncertain CSI as an asymmetric uncertainty set. Under the constructed CSI uncertainty set, a robust counterpart method based on pre-trained network parameters is developed. It characterizes the uncertainty set as a finite union of convex sets, thereby providing a tractable approximation for the original chance constraints. Finally, we design an adaptive iterative algorithm to jointly optimize the resource allocation and beamforming vectors. Simulation results show that our proposed DNN-driven method outperforms traditional robust and non-robust methods, achieving a superior energy efficiency and robust reliability in SAGIN.