Iterative MIMO Detection Using Stochastic Sampling

A novel iterative MIMO detection method is presented. To generate an effective list for a maximum-a-posteriori (MAP) iterative detector, a stochastic sampling method is employed. The effectiveness of the generated list is benchmarked against the celebrated method of K-best. Extensive computer simulations reveal that the proposed method outperforms the K-best method, when the system parameters are set for the same list size. It is also noted that while the list generation method in K-best follows a sequential approach, the stochastic sampling method proposed in this paper is tailored towards a parallel implementation, which, helps in reducing the detector latency significantly.