  Most studies of adaptive algorithm behavior consider performance measures
based on mean values such as the mean-square error. The derived models are
useful for understanding the algorithm behavior under different environments
and can be used for design. Nevertheless, from a practical point of view, the
adaptive filter user has only one realization of the algorithm to obtain the
desired result. This letter derives a model for the variance of the
squared-error sample curve of the least-mean-square (LMS) adaptive algorithm,
so that the achievable cancellation level can be predicted based on the
properties of the steady-state squared error. The derived results provide the
user with useful design guidelines.
