基于平行因子分析的SIMO-OFDM系统盲信道与符号联合估计算法

针对SIMO-OFDM系统下的信道估计和符号检测问题，该文建立了接收数据矩阵的平行因子分析(PARAFAC)模型，利用PARAFAC模型中离散傅里叶变换矩阵的行满秩特性，结合数据矩阵的奇异值分解，提出了一种信道与符号联合盲估计的闭式求解方法。由于提出的求解方法无须进行迭代便可以完成信道估计和符号检测，因此其计算复杂度低，此外，利用PARAFAC模型实现信道和符号的同时计算，避免了因信道估计误差导致的符号误码率性能下降问题。仿真结果表明，与传统方法相比提出的方法计算复杂度更低，估计性能更好。

Joint Blind Channel Estimation and Symbols Detection for SIMO-OFDM Systems Based on PARAFAC

To solve the problem of the joint blind channel estimation and symbol detection for SIMO-OFDM systems, a PARAllel FACtor (PARAFAC) analysis model of the receive data matrix is established. Then, with the full row rank characteristic of the discrete Fourier transform matrix and the singular value decomposition of the receiving data matrix, a closed method is proposed for joint blind channel estimation and symbol detection. The proposed method has low computational complexity because it has no iteration. Furthermore, by the simultaneously calculated of channel and signals, the proposed method can avoid the performance reduction of signal estimation caused by channel estimation error. Simulation results show that the proposed method has lower computational complexity and better estimation performance compared with traditional methods.