We study the problem of nonparametric distribution function deconvolution in the 1-Wasserstein metric, when the error distribution is unknown and ordinary smooth. We propose an estimator based on the integral of the classical deconvolution kernel density estimator, where the characteristic function of the error distribution is estimated by its empirical counterpart, using a sample of independent and identically distributed observations. Under a regularity assumption for the mixing density, the estimator achieves a convergence rate with the same structure as in the known-error distribution case, though governed by the minimum of the error sample size and the recorded signal sample size.