A Lower Bound for the Variance of Estimators for Nakagami m Distribution

Recently we have proposed a maximum-likelihood iterative algorithm for estimation of parameters of the Nakagamim distribution. This technique performs better than state of art estimation techniques for this distribution. This could be of particular use in low-data/block based estimation problems. In these scenarios, the estimator should be able to give accurate estimates (in the mean square sense) with less amount of data. Also, the estimates should improve with increase in number of blocks received. In this paper, we see through our simulations, that our proposal is well designed for meeting such requirements. Further, it is well known in the literature that an efficient estimator does not exist for the Nakagami-m distribution. In this paper, we also 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).