摘要
无蜂窝MIMO系统中,利用大规模天线与大规模宏分集,构建以用户为中心的架构,用户可以选择多个合适的接入点来获取服务,实现均匀的用户覆盖性能,从而可以更好地满足不断增长的移动通信服务需求。研究了采用OTFS调制的无蜂窝大规模MIMO系统的上行信道估计方法。首先,根据用户的位置关系,对用户划分组群,在有限的资源下服务更多的用户;然后,设计了用户上行导频结构,研究了基于LS和LMMSE的无蜂窝大规模MIMO-OTFS上行信道估计方法。仿真结果表明,提出的上行信道估计方法在不同用户速度和不同信噪比下都能够得到稳健的信道估计结果,同时,在有多个用户的无蜂窝大规模MIMO系统中,并且每个用户在高速移动的情况下,估计的结果也能够保持较高的准确度。
In cell-free massive multiple-input multiple-output(mMIMO)systems,a user-centric architecture is constructed utilizing massive antennas and macro-diversity.This architecture allows users to select multiple suitable access points to access services,achieving uniform user coverage performance to better meet the growing demands of mobile communication services.This paper investigates the uplink channel estimation method for cell-free mMIMO systems employing orthogonal time-frequency-space(OTFS)modulation.Initially,users are grouped based on their spatial relationships to serve more users with limited resources.Subsequently,a user-specific uplink pilot structure is designed,and channel estimation methods based on least squares and linear minimum mean squared error are leveraged for non-cellular mMIMO-OTFS systems.Simulation results demonstrate that the proposed uplink channel estimation method consistently delivers robust channel estimation results under varying user velocities and signal-to-noise ratios.Moreover,in cell-free mMIMO systems with multiple users,even in high-speed mobile scenarios,the estimation results maintain high accuracy.
作者
张咪
许魁
夏晓晨
谢威
郭明喜
臧国珍
ZHANG Mi;XU Kui;XIA Xiaochen;XIE Wei;GUO Mingxi;ZANG Guozhen(The Army Engineering University of PLA,Nanjing 210007,China)
出处
《移动通信》
2023年第9期101-109,共9页
Mobile Communications
基金
国家自然科学基金“通信定位一体去蜂窝大规模MIMO智能传输方法研究”,“毫米波UAV通信系统中的智能信道获取与传输方法研究”,“复杂电磁环境下高度自由度欠定通信侦察系统盲辨识研究”(62071485,61901519,62001513)
江苏省基础研究计划“天地融合卫星移动通信组网理论与技术”(BK20192002)
江苏省自然科学基金“位置信息辅助的去蜂窝大规模MIMO系统智能传输方法研究”,“高自由度欠定无线信道统计复用系统盲辨识技术研究”(BK20201334,BK20200579)。
关键词
无蜂窝大规模多输入多输出
正交时频空调制
信道估计
最小二乘
最小均方误差
cell-free massive multiple-input multiple-output
orthogonal time-frequency-space modulation
channel estimation
least squares
minimum mean square error