An iterative detector based on sparse bayesian error recovery for uplink large-scale MIMO systems

In uplink large-scale multiple-input and multiple-output (MIMO) systems, the data detection at the receiver is the major challenge due to the considerable increase in dimensions of MIMO systems. In this paper, a Bayesian strategy is investigated for MIMO systems. The main idea of this algorithm is to improve the performance of a detector by finding the incorrectly detected symbols relying on the sparse property of the estimation error. In the proposed method, the conventional massive MIMO model is converted into a sparse model by inducing a sparse constrain on the symbol error vector obtained from a linear detector (minimum mean square error or zero forcing detectors). Then, by recovering the non-zero entries of error (i.e. the incorrectly detected symbols) via the sparse Bayesian approach, the primary estimate of the information vector is corrected. Numerical results reveal that exploiting the sparse characteristic of the estimation error leads to improvement in detection performance and the proposed Bayesian approach achieves better results than conventional and previous state of the art error recovery methods. Meanwhile, computational complexity of the sparse Bayesian error recovery (SBER) algorithm is the same as that of the linear detectors.