Harnessing Autoencoder-Based Power Allocation for Direct to Satellite IoT
Pokhrel, Shiva Raj Aslam, Sohaib Aloqaily, Moayad
Pokhrel, Shiva Raj
Aslam, Sohaib
Aloqaily, Moayad
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Direct-to-satellite IoT (DtSIoT) enables scalable, global connectivity for diverse applications by leveraging LEO satellites and Non-Orthogonal 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 networks1.
Citation
S. R. Pokhrel, S. Aslam and M. Aloqaily, "Harnessing Autoencoder-Based Power Allocation for Direct to Satellite IoT," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3578093
Source
IEEE Internet of Things Journal
Conference
Keywords
Direct-to-satellite IoT (DtSIoT), Low Earth Orbit, Non-Orthogonal Multiple Access, Quality of Service
Subjects
Source
Publisher
IEEE