# Fair performance comparison betweem LMS & NLMS

How can I choose the step size $\mu$, when I'm comparing different algorithms such as LMS, NLMS and transform domain adaptive filters, regarding their convergence speed, to get a fair comparison between them for mean-squared-error learning curves?

Is it necessary for a fair comparison to use the same step size for all algorithms since in most of the literature $\mu$ is not specified for the individual algorithms?