基于子空间分解的线性多用户检测算法及其泛散度

Multiuser detection based on subspace fitting and its norm divergence

讨论了基于子空间分解线性多用户检测算法的原理,重点对解相关检测算法、最小均方误差检测算法、最小输出能量检测算法的基于子空间分解的检测算子的形式、性能进行了讨论。定义了一个称谓"泛散度"的量,来量度基于子空间分解的检测算子与传统检测算子的拟合距离,这个泛散度不但与数据长度有关,而且特别与在线用户的功率强度及分散度有关。笔者还对理论分析的结果进行了计算机模拟,实验结果与理论分析结果吻合。

The paper discusses the principle of the detectors based, and especially discusses the form and performance of decorrelator, MMSE, and MOE based on the subspace fitting. By defining the 'norm divergence' the paper quantifies the difference of the conventional and the subspace fitting detectors, which not only rely on the length of the data, but also depend on the dispersion and intensity of the energy of the online users. Finally, results of experiments confirm the theory analysis via simulation.