Dynamically Adjusted Order Successive Interference Cancellation Algorithm for Massive Multiple Input Multiple Output

Aiming at the low signal detection performance of the order successive interference cancellation (OSIC) algorithm in the nonlinear detection of multiple input multiple output (MIMO) systems, the paper proposes a dynamic adjusted OSIC (D-OSIC) detection algorithm. In order to solve the problem of early error propagation, the maximum likelihood (ML) is used to select the best symbol which could improve the detection performance of the ML-D-OSIC algorithm. The number of elimination layers is dynamically adjusted due to the traversal capacity. Compared with the hybrid iterative algorithm, the complexity of the algorithm is reduced. The simulation results show that ML-D-OSIC algorithm is superior to the OSIC algorithm in signal detection performance. The detection performance improves with the difference of predefined threshold, offset and weight. The complexity is much lower than that of ML algorithm.