  In this paper, a hard thresholding wavelet estimator is constructed for a
deconvolution model in a periodic setting that has long-range dependent noise.
The estimation paradigm is based on a maxiset method that attains a near
optimal rate of convergence for a variety of L_p loss functions and a wide
variety of Besov spaces in the presence of strong dependence. The effect of
long-range dependence is detrimental to the rate of convergence. The method is
implemented using a modification of the WaveD-package in R and an extensive
numerical study is conducted. The numerical study supplements the theoretical
results and compares the LRD estimator with na\"ively using the standard WaveD
approach.
