中文摘要：

提出一种基于叠加训练序列的MIMO信道估计模型,该模型信道估计方法不占用额外的信号带宽并具有较高的估计精度。通过最小二乘（LS）方法,推导出模型信道估计误差的均方差、信道容量的下限以及更加紧凑的Cramer-Rao界。数值模拟结果表明,相同条件下,采用叠加的训练序列要比直接采用训练序列对系统容量的改善5 dB左右。此外,模型所采用的算法结构简单,计算量小,有很大的实际应用前景。

英文摘要：

An MIMO channel estimation model using superimposed training sequences is proposed, which doesn＇t take any extra signal spectral and has high accuracy. Through exploiting the least square method, a closed-form solution for the estimation of variance and the lower bound of channel capacity, as well as a rather compact Cramer-Rao bound are derived. Simulation results show that the system has higher performance by using superimposed training sequences than that using training sequences, which is improved about 5 dB. Moreover, the algorithm has a simple structure and low computation complexity, which is very promising in the reality wireless communication.