Harnessing Autoencoder-Based Power Allocation for Direct-to-Satellite IoT

Direct-to-satellite IoT (DtSIoT) enables scalable, global connectivity for diverse applications by leveraging LEO satellites and Nonorthogonal Multiple Access to support massive device access with heterogeneous QoS needs. However, efficient resource allocation remains a key challenge. To address this, we propose a Deep AutoEncoder-based Model Predictive Controller (DAE-MPC) that learns system dynamics to optimize power allocation in real-time. Simulation results show that DAE-MPC improves transmission efficiency and reduces latency, offering a robust solution for intelligent resource management in DtSIoT networks.