This paper gives an example where iterate averaging can result in a tracking stochastic approximation (i.e. constant step size) algorithm with optimal convergence rate. Previously iterate averaging has been shown to result in optimal asymptotic convergence rate for decreasing step size algorithms. To our knowledge, this is the first time an example has been given of a case where iterate averaging can lead to a tracking algorithm with asymptotically optimal convergence rate! We also show how to utilize these result to analyse the effect of Markovian type admission and access policies on an adaptive multiuser deteciton algorithm.