Performance prediction of a coded digital communication system using cross-validation

A semi-analytical performance prediction of a digital communication system using Fourier transform inversion

“…Consequently, the probability density function can be estimated according to (7) by using the approximation of ϕ X (t) given in (9). However, the Fourier integral in (7) can exhibit divergence for large values of the time variable t. To solve this limitation, the characteristic function estimator ϕ X (t) is multiplied by a damping function ψ h (t) = ψ(ht) to control the smoothness of the estimated probability density function.…”

“…Using the definition of Fourier transform inversion (7), the probability density function is done as:…”

“…The method we have proposed in [5] considers the estimation of the pdf using kernel estimator [6] which uses an efficient technique for selecting the smoothing parameter. In [7], we have compared some methods to make up for the optimum smoothing parameter choice. The first is the minimum integrated squared error (MISE) [8], which exhibited a significant squared error between the true pdf and the estimated one.…”

Fast performance prediction method for QPSK modulation

“…In our previously work [4], we have proved the optimal smoothing parameter can be calculated using Crossvalidation criterion which minimizes the integrated squared error (ISE) [11]. In the fact, to measure the closeness of f and f for a given sample, we compute the ISE:…”

“…The appraoch we have proposed in [4] considers the estimation of the pdf using the Gaussian Kernel [5]. Its accuracy depends on choosing of the smoothing parameter.…”