KAUST DepartmentElectrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Physical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/667111
MetadataShow full item record
AbstractIn this work, we study the impact of input-spread on the steady-state excess mean squared error (EMSE) of the normalized least mean squares (NLMS) algorithm. First, we use the concept of majorization to order the input-regressors according to their spread. Second, we use Schur-convexity to show that the majorization order of the input-regressors is preserved in the EMSE. Effectively, we provide an analytical justification of the increase in steady-state EMSE as the input-spread increases.
CitationAli, A., Moinuddin, M., & Alnaffouri, T. (2021). The NLMS is Steady-State Schur-Convex. IEEE Signal Processing Letters, 1–1. doi:10.1109/lsp.2021.3055460
SponsorsThis work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant no. (DF-211-135-1441). The authors, therefore, acknowledge with thanks DSR technical and financial supports.
JournalIEEE Signal Processing Letters
RelationsIs Supplemented By: