A subspace constrained LSCM algorithm
Lijuan Zhao, Feng Guang-zeng · 2008
In this paper, a subspace constrained least-squares constant modulus algorithm (LSCMA) is proposed for blind adaptive multiuser detection. The new algorithm has been derived by integrating projection approximate subspace tracking with deflation (PASTd) algorithm, singular value decomposition (SVD) technique and LSCMA. This proposed algorithm reduces computational complexity remarkably compared with the traditional eigenvalue decomposition (ED) subspace algorithm. Simulation results show that the proposed algorithm outperforms LSCMA in terms of bit error rate (BER) and convergence rate, especially when the signal-to-noise ratio (SNR) is low. Simulation results also show that the proposed algorithm achieves a comparable performance as the traditional ED subspace algorithm based LSCMA in terms of convergence rate, tracking ability and BER performance, but with a much lower complexity.