Performance analysis of RLS linearly constrained constant modulus algorithm for multiuser detection

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.