Robust Linearly Constrained LSCM for Adaptive Interference Cancellation

This paper presents a robust least square constant modulus algorithm (LSCMA) for interference cancellation based on subspace decomposition in CDMA system. The conventional least square constant modulus algorithm (LSCMA) is easy to capture the interference signals in the system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA algorithm is proposed by applying the linear constrained condition to the LSCMA, thus makes the algorithm converge to the desired user. To further enhance the performance, we apply the subspace decomposition to autocorrelation matrix of the received signal, and combine the Lagrange method and the partial Taylor-series expansion to update the weight vector. To reduce the computational complexity, an orthogonal projection approximation subspace-tracking (OPAST) algorithm is employed for adaptive signal subspace estimation. The simulation results demonstrate that the proposed algorithm has lower complexity, superior performance and is more robust in the case of random initial value.