电子学报 ›› 2014, Vol. 42 ›› Issue (7): 1256-1261.DOI: 10.3969/j.issn.0372-2112.2014.07.002

• 学术论文 • 上一篇    下一篇

MU-MIMO-OFDM上行频偏与信道联合跟踪

王旭, 何凡, 冯辉, 杨涛, 胡波   

  1. 复旦大学, 上海 200433
  • 收稿日期:2013-03-18 修回日期:2013-08-22 出版日期:2014-07-25
    • 作者简介:
    • 王旭 男,1986年出生,陕西富平人.2008年在复旦大学获理学学士学位,现在该校攻读博士学位.主要研究方向为OFDM系统中的同步与信道估计.E-mail:xwang.fdu@gmail.com;何凡 男,1988年出生,江苏盐城人.2012年在复旦大学获理学硕士学位.主要研究方向为OFDM系统中的同步技术.
    • 基金资助:
    • 国家自然科学基金 (No.60972024); 国家重大专项 (No.2012ZX03001007-003,No.2013ZX03003006-003)

Joint Carrier Frequency Offset and Channel Tracking for MU-MIMO-OFDM Uplink Systems

WANG Xu, HE Fan, FENG Hui, YANG Tao, HU Bo   

  1. Fudan University, Shanghai 200433, China
  • Received:2013-03-18 Revised:2013-08-22 Online:2014-07-25 Published:2014-07-25
    • Supported by:
    • National Natural Science Foundation of China (No.60972024); National Science and Technology Major Project (No.2012ZX03001007-003, No.2013ZX03003006-003)

摘要:

本文提出了一种在MU-MIMO-OFDM (Multiple-User Multiple-Input-Multiple-Output Orthogonal-Frequency-Division-Multiplexing)上行链路中,联合跟踪残留频偏(Residual Carrier Frequency Offset,RCFO)和信道的算法.本算法采用EM (Expectation Maximization)方法求解该非线性问题,并使用变分推断来近似原来复杂的隐变量的后验概率.在估计RCFO时,考虑了信道估计误差的概率分布,从而降低了信道估计误差对跟踪性能的影响.仿真中,本算法达到了较高的跟踪精度,尤其是在高信噪比时没有误差平台问题.

关键词: 频偏与信道联合跟踪, 期望最大化(EM), 变分推断

Abstract:

A joint carrier frequency offset (CFO) and channel tracking method for multiple-user multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MU-MIMO-OFDM) uplink systems is proposed in this paper.We use the expectation maximization (EM) algorithm to solve the nonlinear problem,and use variational inference to approximate the posterior distribution of the latent variable.Besides,to reduce the impact of channel estimation deviation,we consider its distribution when estimating the RCFO.Simulation results show that the proposed algorithm obtains high tracking accuracy,and there is no error floor problem in the high SNR region.

Key words: joint carrier frequency offset and channel tracking, expectation maximization, variational inference

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