Blind multiuser detection using the subspace-based linearly constrained LSCMA

Abstract

The least squares constant modulus algorithm (LSCMA) is a popular constant modulus algorithm (CMA) because of its global convergence and stability. But the performance will degrade when it is affected by the problem of interference capture in the MC-CDMA system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA multiuser detection algorithm is proposed by using the spreading code of the desired user to impose linear constraint on the LSCMA. To further enhance the performance, we project the weight vector obtained by the proposed linearly constrained LSCMA algorithm onto the signal subspace and propose a subspace-based linearly constrained LSCMA multiuser detection algorithm. The proposed algorithm ensures the algorithm convergence to the desired user and suppresses the noise subspace in the weight vector. Thus the performance of the system is improved. Moreover, to reduce the computational complexity, an improved projection approximation subspace tracking with deflation (PASTd) algorithm is proposed for adaptive signal subspace estimation. The simulation results demonstrate that the proposed algorithm achieves better output signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) performance than the traditional LSCMA algorithm, linearly constrained LSCMA algorithm and subspace-based MMSE algorithm.