Multiple feedback successive interference cancellation with shadow area constraints for MIMO systems

In this paper, we propose a novel low-complexity, multiple feedback successive interference cancellation (SIC) strategy for uncoded multiple-input multiple output (MIMO) spatial multiplexing systems. Since the complexity of the ML and the existing near ML algorithms such as the sphere decoder (SD) is still high in systems with bad channel conditions and/or low signal-noise ratio (SNR), there is a need for flexible and cost effective MIMO detectors. In the proposed multiple feedback successive interference cancellation with shadow area constraints (MF-SIC-SAC) algorithm, feedback diversity (FD) is introduced to combat the error propagation effect in decision feedback systems and achieve a close to ML performance. For our scheme, the computational complexity is as low as the SIC algorithm with very low additional complexity added. Simulation results show that the proposed detection significantly outperforms the conventional SIC scheme while maintaining a low detection complexity. The SNR has about 0.5dB degradation at the target bit error rate BER = 0.001 compared with the MLD with QPSK modulation.