Preamble slice orderly queue access scheme in cell-free dense communication systems  

作　　者：Jun Sun Mengzhu Guo Jian Liu 

机构地区：[1]School of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China [2]School of Computer and Communication Engineering,University of Science and Technology,Beijing 100083,China

出　　处：《Digital Communications and Networks》2025年第1期126-135,共10页数字通信与网络(英文版)

基　　金：supported in part by the National Natural Science Foundation of China under grants 61771255;in part by the Provincial and Ministerial Key Laboratory Open Project under grant 20190904;in part by the Key Technologies R&D Program of Jiangsu (Prospective and Key Technologies for Industry)under Grants BE2022067,BE2022067-1 and BE2022067-2。

摘　　要：High reliability applications in dense access scenarios have become one of the main goals of 6G environments.To solve the access collision of dense Machine Type Communication(MTC)devices in cell-free communication systems,an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed,that is,the Preamble Slice Orderly Queue Access(PSOQA)scheme.In this scheme,the preamble arrangement is combined with the access control.The preamble arrangement is realized by preamble slices which is from the virtual preamble pool.The access devices learn to queue orderly by deep reinforcement learning.The orderly queue weakens the random and avoids collision.A preamble slice is assigned to an orderly access queue at each access time.The orderly queue is determined by interaction information among multiple agents.With the federated reinforcement learning framework,the PSOQA scheme is implemented to guarantee the privacy and security of agents.Finally,the access performance of PSOQA is compared with other random contention schemes in different load scenarios.Simulation results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.

关 键 词：Machine type communication Random access Reinforcement learning Access success probability Federated learning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]