A Linearly Constrained LSCM Algorithm Based on Subspace
基于子空间的线性约束最小二乘恒模算法

Linearly constrained Least Square Constant Modulus Algorithm (LSCMA) is an effective solution to the problem of interference capture in Constant Modulus Algorithm (CMA). But the performance will degrade when it is affected by the noise subspace in practical situations. In order to overcome this shortage, a subspace-based linearly constrained LSCMA multiuser detection algorithm is proposed. The proposed algorithm offers fast convergence rate, has good channel tracking ability and provides excellent output Signal-to-Interference-plus- Noise-Ratio (SINR) and Bit Error Rate (BER) performance. Theory analysis and simulation results demonstrate the effectiveness and superiority of the proposed algorithm.