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An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things
Changjiang Fei1, Bin Jiang1, Kun Xu1
1College of Information and Communication, National University of Defense Technology, Wuhan 430010, China.
This study introduces Load Control-based Three-Replica Contention Resolution Diversity Slotted ALOHA (LC-CRDSA3) for space-based Internet of Things (S-IoT). LC-CRDSA3 enhances throughput in dynamic S-IoT environments by intelligently controlling network load.
Area of Science:
- * Wireless communication and networking
- * Space-based Internet of Things (S-IoT) systems
- * Multiple access schemes
Background:
- * Random access schemes are crucial for S-IoT due to massive connectivity and grant-free transmission needs.
- * Existing schemes suffer from sharp throughput degradation under high or dynamic network loads, common in S-IoT.
- * Variable satellite coverage and bursty traffic exacerbate load sensitivity, reducing actual S-IoT throughput below theoretical limits.
Purpose of the Study:
- * To propose an intelligent load control mechanism for random access in S-IoT.
- * To enhance the Contention Resolution Diversity Slotted ALOHA (CRDSA) scheme for S-IoT environments.
- * To improve overall network throughput and efficiency in dynamic S-IoT networks.
Main Methods:
- * Development of Load Control-based Three-Replica Contention Resolution Diversity Slotted ALOHA (LC-CRDSA3), extending CRDSA with three replicas.
- * Implementation of a Maximum Likelihood Estimation (MLE)-based algorithm for accurate frame load estimation.
- * Integration of computational intelligence-based time series forecasting for predictive load management.
Main Results:
- * LC-CRDSA3 actively manages network load to remain near a critical value, preventing throughput collapse.
- * The MLE-based load estimation effectively utilizes time slot status for accurate load assessment.
- * Time series forecasting enables prediction of future network loads for proactive control.
- * Simulations show LC-CRDSA3 achieves throughput close to the theoretical maximum in dynamic S-IoT scenarios, outperforming CRDSA++.
Conclusions:
- * LC-CRDSA3 provides an effective solution for maintaining high throughput in S-IoT networks with dynamic load conditions.
- * Intelligent load control, enabled by MLE and time series forecasting, is key to overcoming limitations of traditional random access schemes in S-IoT.
- * The proposed scheme offers a robust and efficient multiple access solution for future S-IoT applications.
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