TY - JOUR
T1 - NLMS Is More Robust to Input-Correlation Than LMS: A Proof
AU - Ali, Anum
AU - Moinuddin, Muhammad
AU - Al-Naffouri, Tareq Y.
N1 - KAUST Repository Item: Exported on 2021-12-13
PY - 2021
Y1 - 2021
N2 - In this work, we comparatively analyze the least mean squares (LMS) algorithm and the normalized least mean squares (NLMS) algorithm. We use the input moment matrices for comparison as the mean-square behavior of both algorithms is determined by the input moment matrices. First, we derive the closed-form expressions of the input moment matrices of the NLMS. Second, we do a numerical and theoretical comparison of the input moment matrices of the LMS and the NLMS. The analysis shows why the performance of the NLMS is less sensitive to the changes in eigenvalue-spread (of the input-correlation matrix) than the LMS.
AB - In this work, we comparatively analyze the least mean squares (LMS) algorithm and the normalized least mean squares (NLMS) algorithm. We use the input moment matrices for comparison as the mean-square behavior of both algorithms is determined by the input moment matrices. First, we derive the closed-form expressions of the input moment matrices of the NLMS. Second, we do a numerical and theoretical comparison of the input moment matrices of the LMS and the NLMS. The analysis shows why the performance of the NLMS is less sensitive to the changes in eigenvalue-spread (of the input-correlation matrix) than the LMS.
UR - http://hdl.handle.net/10754/673978
UR - https://ieeexplore.ieee.org/document/9645218/
U2 - 10.1109/LSP.2021.3134141
DO - 10.1109/LSP.2021.3134141
M3 - Article
SN - 1558-2361
SP - 1
EP - 1
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
ER -