The optimal geometric configuration of the cells of a wireless network using multiple antennas for each base station and a macrodiversity technique is investigated. The simplest model of a cell of an existing wireless...
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The optimal geometric configuration of the cells of a wireless network using multiple antennas for each base station and a macrodiversity technique is investigated. The simplest model of a cell of an existing wireless network is disk shaped, and the cell deployment is arranged in a honeycomb tiling pattern. This model has been used as the first-order approximation for designing and evaluating wireless networks. However, the cells of a network using multiple antennas and macrodiversity are no longer disk shaped. This study investigated a network with a cell-and-antenna deployment pattern that covers a given service area using the minimum number of cells. The objective of this paper is to offer a first-order approximation model for a cell-and-antenna deployment pattern of such a network. For this objective, first, by imposing practical conditions, cell-and-antenna deployment patterns are classified. Then, the asymptotic minimum coverage problem is formulated as an optimization problem with a constraint for a set of deployment patterns. To easily obtain the first-order approximation model, a simplified formulation and model are proposed. Numerical examples show that the proposed deployment pattern covers the service area with nearly half the cells required by the existing heuristic pattern.
Recent accelerated and traditional approval of anti-amyloid therapies by the U.S. Food and Drug Administration for the treatment of patients with early Alzheimer's disease has stimulated heated debate on whether o...
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In this work, we study an extension of the k-center facility location problem, where centers are required to service a minimum of clients. This problem is motivated by requirements to balance the workload of centers w...
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In this work, we study an extension of the k-center facility location problem, where centers are required to service a minimum of clients. This problem is motivated by requirements to balance the workload of centers while allowing each center to cater to a spread of clients. We study three variants of this problem, all of which are shown to be N P-hard. In-approximation hardness and approximation algorithms with factors equal or close to the best lower bounds are provided. Generalizations, including vertex costs and vertex weights, are also studied. (c) 2004 Elsevier B.V. All rights reserved.
The objective is to locate undesirable facilities on a network so as to minimize the total demand covered subject to the condition that no two facilities are allowed to be closer than a pre-specified distance. We prov...
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The objective is to locate undesirable facilities on a network so as to minimize the total demand covered subject to the condition that no two facilities are allowed to be closer than a pre-specified distance. We prove that there exists a dominating location set and that it is a challenging problem to determine the consistency of the distance constraints. We compare several different mathematical formulations to solve the problem. Heuristics with computational experiments are provided. (C) 2006 Elsevier Ltd. All rights reserved.
Several methods have been proposed to construct confidence intervals for the binomial parameter. Some recent papers introduced the 'mean coverage' criterion to evaluate the performance of confidence intervals ...
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Several methods have been proposed to construct confidence intervals for the binomial parameter. Some recent papers introduced the 'mean coverage' criterion to evaluate the performance of confidence intervals and suggested that exact methods, because of their conservatism, are less useful than asymptotic ones. In these studies, however, exact intervals were always represented by the Clopper-Pearson interval (C-P). Now we focus on Sterne's interval, which is also exact and known to be better than the C-P in the two-sided case. Introducing a computer intensive level-adjustment procedure which allows constructing intervals that are exact in terms of mean coverage, we demonstrate that Sterne's interval performs better than the best asymptotic intervals, even in the mean coverage context. Level adjustment improves the C-P as well, which, with an appropriate level adjustment, becomes equivalent to the mid-P interval. Finally we show that the asymptotic behaviour of the mid-P method is far poorer than is generally expected. Copyright (C) 2003 John Wiley Sons, Ltd.
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