Performance analysis of RLS linearly constrained constant modulus algorithm for multiuser detection
Performance analysis of RLS linearly constrained constant modulus algorithm for multiuser detection
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DOI：
10.1016/j.sigpro.2008.08.007
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发表时间：
2009-02
期刊：
影响因子：
--
通讯作者：
Xin Wang;G. Feng
中科院分区：
文献类型：
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作者：
Xin Wang;G. Feng
线性约束恒模算法 (LCCMA) 是一种用于抑制多址干扰 (MAI) 的盲多用户检测器 (MUD) 解决方案，在直接序列码分 (DS-CDMA) 系统中得到了广泛研究。然而，基于随机梯度下降（SGD）的传统CMA收敛速度慢。我们的研究将递归最小二乘（RLS）近似引入到 LCCMA 中，以提高 DS-CDMA 系统的收敛速度，并量化基于 RLS-LCCMA 的盲自适应滤波器在静态和时变信道中的性能。在本研究中，我们使用反馈方法的框架推导了 MUD 的超额均方误差 (EMSE) 的表达式，并进一步获得了 SGD-LCCMA 的步长与 RLS-LCCMA 的遗忘因子之间的关系。最终仿真结果展示了RLS-LCCMA的优势并验证了算法的性能分析。
The linearly constrained constant modulus algorithm (LCCMA) is a blind multiuser detector (MUD) solution to multiple access interference (MAI) suppression that is widely investigated in direct-sequence code division (DS-CDMA) systems. However, the conventional CMA based on the stochastic gradient descent (SGD) has slow convergence speed. Our research introduces an approximation of recursive least square (RLS) into LCCMA for better convergence speed in DS-CDMA system and quantifies the performance of blind adaptive filter based on RLS-LCCMA in both a static and a time-varying channel. In this investigation, we derive the expressions for the excess mean-square error (EMSE) of the MUD with a framework called feedback approach, and further obtain a relationship between the step size of SGD-LCCMA and the forgetting factor of RLS-LCCMA. Eventually, simulation results show the advantage of RLS-LCCMA and verify the performance analysis of the algorithm.