Low Complexity Soft Interference Cancellation for MIMO-Systems

Iterative equalization has emerged as an efficient means of achieving near-capacity detection performance in multiple-antenna (MIMO) systems. However, many proposed detection strategies still exhibit a very high complexity which may render them unsuited for practical implementation. In this paper, we show that the appropriate use of a-priori knowledge during iterative equalization based on soft interference cancellation enables to drastically reduce detection complexity. More specifically, we propose to take into account only a subset of constellation points in the calculation of detector soft output, by considering the vicinities of the interference reduced received signal and the constellation points supported by the a-priori knowledge. Additionally, a threshold rule on symbol probabilities is used to reduce complexity in the calculation of soft symbols and residual noise during soft interference cancelling. Our results show that the computational effort required for detection can be lowered by as much as 50% for 16-QAM and 96% for higher order constellations (64-256-QAM), without any significant loss in performance