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

作     者：Jiadai Wang Chaochao Xing Jiajia Liu Jiadai Wang;Chaochao Xing;Jiajia Liu

作者机构：National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technologythe School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2023年第20卷第8期

页      面：44-53页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 0810[工学-信息与通信工程] 13[艺术学] 08[工学] 081104[工学-模式识别与智能系统] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

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) 

主　　题：preamble allocation random access mMTC URLLC reinforcement learning 

摘      要：The emergence of various commercial and industrial Internet of Things(IoT)devices has brought great convenience to people’s life and *** low-power,massively connected mMTC devices(MDs)and highly reliable,low-latency URLLC devices(UDs)play an important role in different application ***,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 *** 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 *** 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 *** 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 *** 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.