Soft Decision Adjusted Modulus Algorithm for Blind Equalization
Liang Wang, Hewen Wen · 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · 2022
Enhanced decision-adjusted modulus algorithm (EDAMA) is excellent to reduce the residual errors of constant modulus algorithm (CMA) for blind equalization problems. However, EDAMA is unsuitable when processing time-varying channel and high order quadrature amplitude modulation (QAM) signals even for low ISI levels due to slow convergence rate and high misestimation. To improve convergence rate and reduce misestimation while maintaining low residual error, a new blind equalization algorithm is proposed by introducing soft decision algorithm into EDAMA. The simulation results show that the new algorithm has a better convergence rate than EDAMA.