Complexity reduced SBI estimation in iterative MIMO systems

Abstract

In this paper, we demonstrate the performance of low complexity soft interference cancellation minimum mean-squared error (SIC-MMSE) detection method for a turbo coded multiple-input multiple-output (MIMO) system with joint iterative detection and decoding (JIDD) principle. The main computational burden of SIC-MMSE detector lies in the multiple inverse operation of the filtering process and maximum a posteriori (MAP) based soft bit information (SBI) estimation. In order to reduce the complexity we apply hybrid approach for SBI estimation. Based on the reliability of soft information provided by channel decoder, the SBI estimation process is switched between the hard decision threshold based (HDT) method with a single distance calculation and a MAP based estimation. Furthermore, we employ a scaling method for the HDT method which can reduce the over estimated SBI values, resulting in performance improvement. Simulation results show that the hybrid approach for SIC-MMSE method highly reduces computational complexity, without appreciable bit error rate performance degradation.