Browsing by Subject "Input distributions"

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Open Access
On the capacity of fading channels with amplitude-limited inputs
(IEEE, 2016) Elmoslimany, A.; Duman, Tolga M.
We address the problem of finding the capacity of fading channels under the assumption of amplitude-limited inputs. Specifically, we show that if the fading coefficients have a finite support and the channel state information is only available at the receiver side, there is a unique input distribution that achieves the channel capacity and this input distribution is discrete with a finite number of mass points.
Open Access
Quantifying input uncertainty in an assemble-to-order system simulation with correlated input variables of mixed types
(IEEE, 2014) Akçay, Alp; Biller, B.
We consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates random input vectors with correlated discrete and continuous components. In this paper, we capture the dependence between input variables in an undirected graphical model and decouple the statistical estimation of the univariate input distributions and the underlying dependence measure into separate problems. The estimation errors due to finiteness of the real-world data introduce the so-called input uncertainty in the simulation output. We propose a method that accounts for input uncertainty by sampling the univariate empirical distribution functions via bootstrapping and by maintaining a posterior distribution of the precision matrix that corresponds to the dependence structure of the graphical model. The method improves the coverages of the confidence intervals for the expected profit the per period.