Low-complexity tensor-based blind receivers for MIMO systems

In the tensor-based MIMO receivers, the multidimensional MIMO signals first are expressed as a third-order tensor model, wherein the factor matrices of tensor model are corresponding time/frequency, symbols, code/diversity of signals. A algorithm then is used for fitting this tensor mode, in which the symbols are estimated as a independent factor matrix. Although the performance of tensor-based receivers strongly depends on the initializations of the factor matrices. However, due to the absence of a priori on channels, these initializations are done randomly in alternating least squares (ALS), a basic algorithm for fitting the tensor models. In order to avoid these random initializations, this paper proposes two algorithms for fitting the tensor models. The first one, called delta bilinear ALS (DBALS) algorithm, where we exploit the increment values between two iterations of the factor matrices, refine these predictions by using the enhanced line search and use these refined values to initialize for two factor matrices. The second one, called orthogonal DBALS algorithm that takes into account the potential orthogonal in factor matrix for the DBALS algorithm, to provide the initialization for this factor matrix. By this way, we avoid random initializations for three factor matrices of tensor model. The performance of proposed receivers is illustrated by means of simulation results and a comparison is made with traditional ALS algorithm and other receivers. Beside a performance improving, our receivers give a lower complexity due to avoid random initializations.