叠加训练序列OFDM系统的迭代最大似然信道估计

叠加训练序列正交频分复用系统具有频谱效率高的优点.传统的基于叠加训练序列的信道估计方法仅使用一阶统计量,受功率分配和噪声影响较大.文中利用训练序列与数据信号相加后发送并经历相同信道的特性,提出一种最大似然方法进行系统的信道估计.该方法将发送的数据信号视为高斯随机变量,由此建立关于信道参数的似然表达式,采用循环最小化技术对参数进行迭代求解并获得方差下界,最后对算法的收敛性进行了分析.仿真实验表明,文中方法同时利用了接收信号的一阶与二阶统计量,从而获得了更优的均方误差和误符号率性能.

Iterative Maximum Likelihood Channel Estimation of OFDM Systems via Superimposed Training Sequence

Although Orthogonal Frequency Division Multiplexing (OFDM) system with superimposed training sequence is bandwidth-efficient,the effectiveness of the conventional channel estimation method by utilizing the first-order statistic is restricted by the power allocation ratio and noise.In order to solve this problem,by considering the property that the training sequence and data signal are arithmetically added and experience the same channel,a maximum likelihood channel estimation scheme is proposed for OFDM systems using the superimposed training sequence.In the proposed scheme,the transmitted data signals are considered as Gaussian variables to establish the likelihood function related to the channel parameter,and an iterative maximum likelihood estimation(MLE) algorithm is derived by means of the cyclic minimizing technology.Afterwards,the lower bound of the variance is obtained and the convergence of the algorithm is analyzed.Simulated results show that the proposed scheme is of better performance in the mean square error and the symbol error rate due to the simultaneous use of the first-order and the second-order statistics of the received signals.