  Recently, we have proposed a maximum likelihood iterative algorithm for
estimation of the parameters of the Nakagami-m distribution. This technique
performs better than state of art estimation techniques for this distribution.
This could be of particular use in low data or block based estimation problems.
In these scenarios, the estimator should be able to give accurate estimates in
the mean square sense with less amounts of data. Also, the estimates should
improve with the increase in number of blocks received. In this paper, we see
through our simulations, that our proposal is well designed for such
requirements. Further, it is well known in the literature that an efficient
estimator does not exist for Nakagami-m distribution. In this paper, we derive
a theoretical expression for the variance of our proposed estimator. We find
that this expression clearly fits the experimental curve for the variance of
the proposed estimator. This expression is pretty close to the cramer-rao lower
bound(CRLB).
