无蜂窝大规模MIMO中的大规模随机接入
Massive Random Access in Cell-Free Massive MIMO System
胡彦丰 1王东明 1梁楚龙 2尤肖虎1
作者信息
- 1. 东南大学,中国 南京 211102
- 2. 中兴通讯股份有限公司,中国 深圳 518057
- 折叠
摘要
研究了以用户为中心的无蜂窝大规模多输入多输出(MIMO)架构下的大规模随机接入方案.为实现可扩展架构,讨论了接入点(AP)与用户设备(UE)的关联以及AP的分簇方法.针对活跃用户检测(AUD),一种基于最大似然检测(ML)的方案被提出以获取活跃用户.通过调整阈值,可以得到不同精度的检测结果.利用AUD的检测用户集合,系统利用基于狄利克雷过程的稀疏贝叶斯学习(DP-SBL)完成信道估计(CE).该算法可以充分利用AP的空间聚散特性,提高系统准确性.基于以上工作,我们提出了联合AUD和CE算法.仿真结果验证了所提方案在性能上的优越性.
Abstract
A user-centric massive random access scheme under the context of cell-free massive multiple-input multiple-output(MIMO)ar-chitecture is investigated.To achieve a scalable architecture,the association between access points(APs)and user equipment(UE)as well as the clustering method for APs is discussed.Regarding active UE detection(AUD),a class of maximum likelihood(ML)-based schemes is proposed to obtain active UE set.By adjusting the threshold,detection results with varying accuracies can be achieved.Leveraging the de-tected user set from AUD,the system employs sparse Bayesian learning based on Dirichlet process(DP-SBL)for channel estimation(CE),effectively utilizing the spatial clustering characteristics of APs to enhance accuracy.Building on this,a joint AUD and CE algorithm is pro-posed.Simulation results validate the superiority of the proposed approach in terms of performance.
关键词
大规模随机接入/无蜂窝大规模MIMO/活跃用户检测/信道估计Key words
massive random access/cell-free massive MIMO/active UE detection/channel estimation引用本文复制引用
基金项目
国家重点研发计划(2020YFB1807200)
出版年
2024
NETL
NSTL
万方数据