Intelligent Preamble Allocation for Coexistence of mMTC/URLLC Devices:A Hierarchical Q-Learning Based Approach  

作　　者：Jiadai Wang Chaochao Xing Jiajia Liu 

机构地区：[1]National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology,the School of Cybersecurity,Northwestern Polytechnical University,Xi’an,Shaanxi,710072,China

出　　处：《China Communications》2023年第8期44-53,共10页中国通信（英文版）

基　　金：supported by National Key R&D Program of China (2022YFB3104200);in part by National Natural Science Foundation of China (62202386);in part by Basic Research Programs of Taicang (TC2021JC31);in part by Fundamental Research Funds for the Central Universities (D5000210817);in part by Xi’an Unmanned System Security and Intelligent Communications ISTC Center;in part by Special Funds for Central Universities Construction of World-Class Universities (Disciplines) and Special Development Guidance (0639022GH0202237 and 0639022SH0201237)

摘　　要：The emergence of various commercial and industrial Internet of Things(IoT)devices has brought great convenience to people’s life and production.Both low-power,massively connected mMTC devices(MDs)and highly reliable,low-latency URLLC devices(UDs)play an important role in different application scenarios.However,when dense MDs and UDs periodically initiate random access(RA)to connect the base station and send data,due to the limited preamble resources,preamble collisions are likely to occur,resulting in device access failure and data transmission delay.At the same time,due to the highreliability demands of UDs,which require smooth access and fast data transmission,it is necessary to reduce the failure rate of their RA process.To this end,we propose an intelligent preamble allocation scheme,which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device side.In particular,considering the limited processing capacity and energy of IoT devices,we adopt the lightweight Qlearning algorithm on the device side and design simple states and actions for them.Experimental results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.

关 键 词：preamble allocation random access mMTC URLLC reinforcement learning 

分 类 号：TP391.44[自动化与计算机技术—计算机应用技术] TN915.05[自动化与计算机技术—计算机科学与技术]