摘要
In this article, two methods adopting simplified minimum mean square error （MMSE） filter with soft parallel interference cancellation （SPIC） are discussed for turbo receivers in bit interleaved coded modulation （BICM） multiple-input multiple-output （MIMO） systems. The proposed methods are utilized in the non-first iterative process of turbo receiver to suppress residual interference and noise. By modeling the components of residual interference after SPIC plus the noise as uncorrelated Gaussian random variables, the matrix inverse for weighting vector of conventional MMSE becomes unnecessary. Thus the complexity can be greatly reduced with only slight performance deterioration. By introducing optimal ordering to SPIC, performance gap between simplified MMSE and conventional MMSE further narrows. Monte Carlo simulation results confirm that the proposed algorithms can achieve almost the same performance as the conventional MMSE SPIC in various MIMO configurations, but with much lower computational complexity.
In this article, two methods adopting simplified minimum mean square error （MMSE） filter with soft parallel interference cancellation （SPIC） are discussed for turbo receivers in bit interleaved coded modulation （BICM） multiple-input multiple-output （MIMO） systems. The proposed methods are utilized in the non-first iterative process of turbo receiver to suppress residual interference and noise. By modeling the components of residual interference after SPIC plus the noise as uncorrelated Gaussian random variables, the matrix inverse for weighting vector of conventional MMSE becomes unnecessary. Thus the complexity can be greatly reduced with only slight performance deterioration. By introducing optimal ordering to SPIC, performance gap between simplified MMSE and conventional MMSE further narrows. Monte Carlo simulation results confirm that the proposed algorithms can achieve almost the same performance as the conventional MMSE SPIC in various MIMO configurations, but with much lower computational complexity.
基金
supported by the Major Project of the Beijing Natural Science Foundation under Grant No. 4110001
the National Science and Technology Major Project of China under Grant No. 2012ZX03001021-003
