Sungsoo Park

We consider a cognitive radio network with an energy-harvesting secondary transmitter to improve both energy efficiency and spectral efficiency. The goal of this paper is to determine an optimal spectrum sensing policy that maximizes the expected total throughput subject to an energy causality constraint and a collision constraint. The energy causality constraint comes from the fact that the total consumed energy should be equal to or less than the total harvested energy, while the collision con

We consider energy harvesting cognitive radio networks in which a secondary transmitter harvests energy from ambient sources or wireless power transfer systems while opportunistically accessing the spectrum licensed to the primary network. The primary traffic is modeled as a time-homogeneous discrete Markov process, and the secondary transmitter may not be able to operate continuously due to sporadic and unstable energy sources. At the beginning of each time slot, the secondary transmitter thus

This paper investigates an optimal mode selection policy for cognitive radio sensor networks powered by RF energy harvesting. The RF energy harvesting enables the sensor node to operate with a potentially perpetual lifetime. We assume that the sensor node harvests RF energy received from the primary network and it cannot carry out RF energy harvesting and opportunistic spectrum access at the same time. Therefore, the sensor node should decide whether to access the spectrum or to harvest RF energ

We consider the single allocation problem in the interacting three-hub network with fixed hub locations. In the single allocation hub network, the hubs are fully interconnected and each nonhub node has to be connected to exactly one of the hubs. The flows between each pair of nodes are sent using the hubs as intermediate switching points. The problem is to find an optimal allocation of nonhub nodes to the hubs which minimizes the total flow cost. We show that the single allocation problem is NP-

Abstract In this paper, we consider a new weapon‐target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model, but it can be transformed into a linear integer programming model. We present a branch‐and‐price algorithm for the problem employing the disaggregated formulation, which has exponentially many columns denoting the feasible allocations of weapon systems to each target. A greedy‐style heuristic is us

Abstract We consider a network design problem in which flow bifurcations are allowed. The demand data are assumed to be uncertain, and the uncertainties of demands are expressed by an uncertainty set. The goal is to install facilities on the edges at minimum cost. The solution should be able to deliver any of the demand requirements defined in the uncertainty set. We propose an exact solution algorithm based on a decomposition approach in which the problem is decomposed into two distinct problem

In this paper, we consider a new weapon–target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model. We applied Lagrangian relaxation and a branch-and-bound method to the problem after transforming the nonlinear constraints into linear ones. An efficient primal heuristic is developed to find a feasible solution to the problem to facilitate the procedure. In the branch-and-bound method, three different bran

In cognitive radio networks, secondary user performs spectrum sensing to detect primary user activities and to access the channel opportunistically. We investigate the problem of developing energy efficient opportunistic spectrum access strategy for a secondary user with energy harvesting capability. We formulate the problem to determine the optimal sensing and access policy as a partially observable Markov decision process (POMDP). Since obtaining the optimal policy is computationally prohibiti