一种分数低阶统计量广义恒模盲多用户检测算法
A Fractional Lower Order Statistics based Generalized Constant Modulus Algorithm for Blind Multi-user Detection
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摘要: 针对实际盲多用户检测系统中存在的大量噪声呈现非高斯性,而这种非高斯性使基于高斯噪声假定下的恒模盲多用户检测算法的性能显著退化甚至不能正常工作,本文提出了一种分数低阶统计量的广义恒模盲多用户检测算法。该算法是分数低阶统计量恒模算法的推广, 能有效地应对非高斯噪声的影响,具有广泛的适用性。通过以DS-CMDA系统为例,将分数低阶统计量广义恒模盲多用户检测算法与传统恒模盲多用户检测算法(CMA)、分数低阶统计量恒模盲多用户检测算法(FLOS-CMA)进行了对比,实验仿真结果表明:无论在高斯白噪声下还是在α稳定分布噪声下,分数低阶统计量广义恒模盲多用户检测算法均具有良好的抗多址干扰和抑制噪声的性能,并且该算法具有更快的收敛速度。Abstract: A lot of ambient noises are non-Gaussian in actual blind multi-user detection systems, and non-Gaussianity often results in significant performance degradation or even invalidation to constant modulus blind multi-user detection algorithm with the channel model premised upon the Gaussian noise assumption. Alpha-stable distributions are one of the most important non-Gaussian models and can provide useful model for many phenomena observed in different fields. Facing with this problem, we present a fractional lower order statistics based generalized constant modulus algorithm (FLOS-GCMA). This FLOS-GCMA, which is a generalization of FLOS-CMA, can work efficiently under non-Gaussian noise condition and can be used in many areas. With DS-CDMA system as an example, we compare our algorithm with the constant modulus algorithm (CMA) and fractional lower order statistics based constant modulus algorithm (FLOS-CMA) analytically and numerically. The simulation results show that FLOS-GCMA can suppress multiple access interference (MAI) and non-Gaussian noise effectively, and converges fast either under Gaussian noise or alpha-stable distributed noise environment.