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

Yen-Wen Chen,
Ji-Zheng You,

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.

Keywords


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.

Refbacks

  • There are currently no refbacks.





Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
JIT Editorial Office, Office of Library and Information Services, National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 974301, Taiwan, R.O.C.
Tel: +886-3-931-7314  E-mail: jit.editorial@gmail.com