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Abstract
Emerging intelligent and highly interactive services result in the mass deployment of internet of things (IoT) devices. They are dominating wireless communication networks compared to human-held devices. Random access performance is one of the most critical issues in providing quick responses to various IoT services. In addition to the anchor carrier, the non-anchor carrier can be flexibly allocated to support the random access procedure in release 14 of the 3rd generation partnership project. However, arranging more non-anchor carriers for the use of random access will squeeze the data transmission bandwidth in a narrowband physical uplink shared channel. In this paper, we propose the prediction-based random access resource allocation (PRARA) scheme to properly allocated the non-anchor carrier by applying reinforcement learning. The simulation results show that the proposed PRARA can improve the random access performance and effectively use the radio resource compared to the rule-based scheme.
Original language | English |
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Pages (from-to) | 1069-1075 |
Number of pages | 7 |
Journal | Journal of Internet Technology |
Volume | 23 |
Issue number | 5 |
DOIs | |
State | Published - 2022 |
Keywords
- Anchor carrier
- Internet of Things
- LTE
- Random access
- Reinforcement learning
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Dive into the research topics of 'Effective Radio Resource Allocation for IoT Random Access by Using Reinforcement Learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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Design and Comparison of Uplink Random Access Algorithms in 4g/5g Wireless Networks for Internet of Things Services and Applicability Analysis(2/2)
Chen, Y.-W. (PI)
1/08/21 → 31/07/22
Project: Research