Abstract
Both space and ground communications have been proven effective solutions under different perspectives in Internet of Things (IoT) networks. This paper investigates multiple-access scenarios, where plenty of IoT users are cooperatively served by a satellite in space and access points (APs) on the ground. Available users in each coherence interval are split into scheduled and unscheduled subsets to optimize limited radio resources. We compute the uplink ergodic throughput of each scheduled user under imperfect channel state information (CSI) and non-orthogonal pilot signals. As maximum-radio combining is deployed locally at the ground gateway and the APs, the uplink ergodic throughput is obtained in a closed-form expression. The analytical results explicitly unveil the effects of channel conditions and pilot contamination on each scheduled user. By maximizing the sum throughput, the system can simultaneously determine scheduled users and perform power allocation based on either a model-based approach with alternating optimization or a learning-based approach with the graph neural network. Numerical results manifest that integrated satellite-terrestrial cell-free massive multiple-input multiple-output systems can significantly improve the sum ergodic throughput over coherence intervals. The integrated systems can schedule the vast majority of users; some might be out of service due to the limited power budget.
Original language | English |
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Number of pages | 17 |
Journal | IEEE Internet of Things Journal |
Early online date | 04 Jul 2024 |
DOIs | |
Publication status | Early online date - 04 Jul 2024 |
Publications and Copyright Policy
This work is licensed under Queen’s Research Publications and Copyright Policy.Keywords
- alternating optimization
- graph neural networks
- Integrated satellite-terrestrial networks
- Internet of Things
- linear processing
- Optimization
- Resource management
- Satellite broadcasting
- Space-air-ground integrated networks
- Throughput
- throughput maximization
- Uplink
ASJC Scopus subject areas
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications