基于张量分解的MIMO多中继系统半盲信道估计方法

Tensor-Based Semi-Blind Channel Estimation Method for Three-Hop MIMO Relay Systems

针对多输入多输出三跳中继系统,提出了一种基于张量分解的半盲信道估计方法.该方法通过对接收信号构造基于张量分解的PARAFAC和PARATUCK2模型,通过两阶段的迭代算法拟合张量模型.两阶段迭代算法利用ALS拟合PARAFAC模型估计复合信道和发送信号,并利用TALS拟合PARATUCK2模型并行估计三跳信道矩阵.与已有的信道估计方法相比,该方法只需少量的导频序列便能并行估计三跳信道矩阵,不仅可以避免误差叠加,而且提高了系统的频谱利用率,仿真结果验证了其有效性.

A novel semi-blind channel estimation was devised to jointly estimate the channel matrices of all links in a three-hop multiple-input multiple-output relay system. A PARAFAC and a PARATUCK2 tensor model of the received signal were constructed, and the proposed algorithm used a two-stage iterative fitting al- gorithm for tensor model. The ALS algorithm was used to fit the PARAFAC tensor model in the process of esti- mating the compound channel matrix. Then the TALS algorithm was used to fit the PARATUCK2 tensor model in the process of extracting all the sub-channel matrices. The proposed algorithm could loose the limitation on the number of antennas at the destination node. Moreover, compared with existing methods, the proposed al- gorithm could avoid error propagation as well as improve the spectral efficiency with few pilots. Numerical ex- amples demonstrated the effectiveness of the proposed algorithm.