The desire to recover the unknown density when data are contaminated with errors leads to nonparametric deconvolution problems. The difficulty of deconvolution depends on both the smoothness of error distribution and the smoothness of the priori. Under a general class of smoothness constraints, we show that deconvolution kernel density k-l estimates achieve the best attainable global rates of c...