An Access Control Scheme Combining Q-Learning and Compressive Random Access for Satellite IoT

The satellite Internet of Things (IoT) covers a vast area with multiple nodes and has limited resources for random access, which leads to low throughput. In this letter, we propose a Q-learning combined with diversity slotted compressive random access control scheme (QDCC) to enhance resource utiliz...

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Bibliographic Details
Published in:IEEE communications letters 2023-11, Vol.27 (11), p.3008-3012
Main Authors: Jiang, Fang, Ma, Song, Yin, Tian-Yu, Wang, Yi, Hu, Yan-Jun
Format: Article
Language:English
Subjects:
Access control
adaptive frame length
Algorithms
Compressive sensing
Internet of Things
Learning
Low earth orbit satellites
Nodes
Q-learning
Random access
random access control
Resource management
Resource utilization
Satellites
Symbols
Throughput
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Summary:The satellite Internet of Things (IoT) covers a vast area with multiple nodes and has limited resources for random access, which leads to low throughput. In this letter, we propose a Q-learning combined with diversity slotted compressive random access control scheme (QDCC) to enhance resource utilization and throughput. Moreover, we address the issue of reduced throughput during network overload by introducing an adaptive frame length QDCC (AFL-QDCC) scheme. This scheme adjusts the frame length by utilizing the support sets estimated by the compressive sensing (CS) reconstruction algorithm. Simulation results demonstrate that the proposed QDCC scheme outperforms conventional schemes in terms of throughput. Furthermore, the AFL-QDCC scheme can maintain stable and high throughput performance even with a large number of nodes.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2023.3323387