Channel iterative optimization algorithm based on algebraic solution in MIMO relay system

At present, in the one-way two-hop multiple-input and multiple-output relay system, channel estimation algorithms based on tensor model were mainly used by alternating least squares (ALS) and khatri-rao Factorization (KRF) algorithm. ALS algorithm has high computational complexity and slow convergence. KRF algorithm is an algebraic solution based on no-iterative form and has low computational complexity, but its estimation performance is not optimal in the least squares sense. In this paper, a compromise channel iterative optimization algorithm (IOA) is proposed. In the algorithm, cost function is constructed and a regularization method is used to derive an update that minimizes the residual tensor. Initial value of the algorithm uses the estimation result of KRF algorithm, so only a small number of iterations is needed to be convergence. Therefore, compared with ALS algorithm, the proposed algorithm has lower complexity, and the simulation results show that the algorithm has almost the same performance as the ALS algorithm.