篇名 |
Effective Radio Resource Allocation for IoT Random Access by Using Reinforcement Learning
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並列篇名 | Effective Radio Resource Allocation for IoT Random Access by Using Reinforcement Learning |
作者 | Yen-Wen Chen、Ji-Zheng You |
英文摘要 | 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.
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起訖頁 | 1069-1075 |
關鍵詞 | Internet of Things、Random access、Anchor carrier、LTE、Reinforcement learning |
刊名 | 網際網路技術學刊 |
期數 | 202209 (23:5期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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