Sharif Digital Repository

Deterministic approach of [1] models the interaction between the bits that are received at the same signal level by the modulo 2 sum of the bits where the carry-overs that would happen with real addition are ignored. By this model in a multi-user setting, the receiver can distinguish most significant bits (MSBs) of the stronger user without any noise. A faithful implementation of the deterministic model requires one to 'neutralize interference' from previous carry over digits. This paper proposes a new implementation of 'interference neutralization' [2] using structured lattice codes. We first present our implementation strategy and then, as an application, apply this strategy to a symmetric... 

In this paper, the problem of statistical reconstruction and prediction of chaotic systems with unknown governing equations using stochastic Markov models is investigated. Using the time series of only one measurable state, an algorithm is proposed to design any orders of Markov models and the approach is state transition matrix extraction. Using this modeling, two goals are followed: first, using the time series, statistical reconstruction is performed through which the probability density and conditional probability density functions are reconstructed; and second, prediction is performed. For this problem, some estimators are required and here the maximum likelihood and the conditional... 

In this paper, capacity inner and outer bounds are established for multiuser channels with Channel State Information (CSI) known non-causally at the transmitters: The Multiple Access Channel (MAC), the Broadcast Channel (BC) with common information, and the Relay Channel (RC). For each channel, the actual capacity region is also derived in some special cases. Specifically, it is shown that for some deterministic models with non-causal CSI at the transmitters, similar to Costa's Gaussian channel, the availability of CSI at the deterministic receivers does not affect the capacity region  

This paper studies a simultaneous weekly assignment and scheduling decisionmaking problem in operating theaters with elective patients. Because of limited recourses in hospitals, considering assignment and scheduling decisions simultaneously can help mangers exploit the available resources more efficiently and make the work-load uniformly distributed during the planning horizon. This procedure can significantly reduce hospital costs and increase satisfaction of patients and personnel. This paper formulates the mentioned problem as a Mixed Integer Linear Program (MILP) considering applicable assumptions like finite recovery beds and limitation of equipment. Since the problem is NP-hard, in... 

We propose a robust deviation framework to deal with uncertain component reliabilities in the constrained redundancy optimization problem (CROP) in series-parallel reliability systems. The proposed model is based on a linearized binary version of standard nonlinear integer programming formulations of this problem. We extend the linearized model to address uncertainty by assuming that the component reliabilities belong to an interval uncertainty set, where only upper and lower bounds are known for each component reliability, and develop a Min-Max regret model to handle data uncertainty. A key challenge is that, because the deterministic model involves nonlinear functions of the uncertain... 

The railroad blocking problem emerges as an important issue at the tactical level of planning in freight rail transportation. This problem consists of determining the optimal paths for freight cars in a rail network. Often, demand and supply resource indicators are assumed certain; hence, the solution obtained from a certain model might not be optimal or even feasible in practice due to the stochastic nature of these parameters. To address this issue, this paper develops a robust model for this problem with uncertain demand and travel time as supply resource indicators. Since the model combines integer variables and nonlinear functions, a branch-And-cut algorithm is used to solve the... 

A deterministic energy supply model with bottom-up structure has limited capability in handling the uncertainties. To enhance the applicability of such a model in an uncertain environment two main issues have been investigated in the present paper. First, a binomial lattice is generated based on the stochastic nature of the source of uncertainty. Second, an energy system model (ESM) has been reformulated as a multistage stochastic problem. The result of the application of the modified energy model encompasses all uncertain outcomes together and enables optimal timing of capacity expansion. The performance of the model has been demonstrated with the help of a case study. The case study has...