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

Yen Wen Chen, Ji Zheng You

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 languageEnglish
Pages (from-to)1069-1075
Number of pages7
JournalJournal of Internet Technology
Issue number5
StatePublished - 2022


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


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