A fast convergence VSS-SDSELMS Adaptive Channel Estimation algorithm for MIMO-OFDM system

MIMO-OFDM Systems are widely used nowadays because of their improved performance in terms of link reliability, high data rates and capacity. Channel Estimation is one of the major challenges faced by a MIMO-OFDM system. For a rapidly time varying wireless channel, Adaptive Channel Estimation (ACE) algorithms are widely used for the purpose of channel estimation. Least Mean Square (LMS) algorithm is the commonly used ACE algorithm, as it has low complexity and numerical robustness. Its main disadvantage is high Mean Square Error (MSE). This disadvantage is overcome in Normalized LMS algorithm with low MSE but its complexity is high and convergence performance is poor. Inorder to further reduce the complexity of LMS algorithm, several simplified LMS algorithms can be used. Simplified algorithms includes Sign Data LMS (SDLMS) algorithm, Sign Error LMS (SDLMS) algorithm, Sign Data Normalized LMS (SDNLMS) algorithm, Sign Data Sign Error Least Mean Square (SDSELMS) algorithm etc. In all these algorithms there occurs a trade-off between convergence rate and MSE. In order to overcome this trade-off, a Variable Step Size (VSS) algorithm can be used. By combining VSS algorithm with such simplified algorithms, we can realize CE algorithms that can reduce the complexity as well as the trade-off between convergence rate and MSE performance. In this paper, a new fast convergence, low complex Variable Step Size-Sign Data Sign Error Least Mean Square (VSS-SDSELMS) algorithm is proposed which further improves the convergence performance along with low computational complexity and comparable MSE performance.