MIMO系统联合参数估计  

Joint parameter estimation for MIMO systems

作　　者：董伟[1] 李建东[1] 吕卓[1] 贺鹏[1] 

机构地区：[1]西安电子科技大学综合业务网理论及关键技术国家重点实验室信息科技研究所宽带无线通信实验室,陕西西安710071

出　　处：《西安电子科技大学学报》2008年第2期189-195,共7页Journal of Xidian University

基　　金：国家自然科学基金重大项目资助(60496316);国家自然科学基金项目资助(60572146);高等学校博士学科点专项科研基金资助(20050701007);高等学校优秀青年教师教学科研奖励计划资助;教育部科学技术研究重点项目资助(107103)

摘　　要：针对多输入多输出系统的联合频偏信道估计问题,考虑了更一般的模型(每一根发送天线到每一根接收天线之间的频偏是不同的),研究了频偏和信道的最大似然估计,分析表明该估计问题包含一个多维搜索过程.为了解决上述复杂的估计问题,提出了一种联合频偏信道估计新方法,首先根据粒子群优化理论估计出多个发射天线到某一接收天线的频偏,然后再利用最大似然估计器对信道增益进行估计.仿真结果表明,与基于相关的估计算法相比,所提出的算法有更大的频偏估计范围,且估计值的均方误差渐近达到Cramer-Rao下界.This paper addresses the problem of frequency offsets and channel gains estimation for a multi-input multi-output （MIMO） system in flat-fading channels. The general case where frequency offsets are possibly different for each transmit antenna is considered. The maximum-likelihood（ML） estimation of the joint frequency offsets and channel gains is investigated, assuming that a training sequence is available. The exact solution to this estimation problem turns out to be too complex as it involves a search over a multi-dimensional domain. To solve this complex estimation problem, a novel joint estimation algorithm for frequency offsets and channel gains is proposed. The new algorithm involves two steps. Frequency offsets are first estimated by the particle swarm optimization （PSO） theory. Then channel gains are estimated by the ML estimator. Simulation results show that the proposed algorithm has a larger frequency offset estimation range than the correlation-based estimation algorithm and asymptotically achieves the Cramer-Rao lower bound （CRLB）.

关 键 词：粒子群优化 MIMO 频偏估计 信道估计 最大似然估计 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]