Effective Radio Resource Allocation for IoT Random Access by Using Reinforcement Learning

Yen-Wen Chen,
Ji-Zheng You,


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.


Internet of Things, Random access, Anchor carrier, LTE, Reinforcement learning

Citation Format:
Yen-Wen Chen, Ji-Zheng You, "Effective Radio Resource Allocation for IoT Random Access by Using Reinforcement Learning," Journal of Internet Technology, vol. 23, no. 5 , pp. 1069-1075, Sep. 2022.


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Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
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