• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.626-648
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• 2011

Energy consumption of wireless access networks is in permanent increase, which necessitates development of more energy-efficient network management approaches. Such management schemes must result with adaptation of network energy consumption in accordance with daily variations in user activity. In this paper, we consider possible energy savings of wireless local area networks (WLANs) through development of a few integer linear programming (ILP) models. Effectiveness of ILP models providing energy-efficient management of network resources have been tested on several WLAN instances of different sizes. To cope with the problem of high computational time characteristic for some ILP models, we further develop several heuristic algorithms that are based on greedy methods and local search. Although heuristics obtains somewhat higher results of energy consumption in comparison with the ones of corresponding ILP models, heuristic algorithms ensures minimization of network energy consumption in an amount of time that is acceptable for practical implementations. This confirms that network management algorithms will play a significant role in practical realization of future energy-efficient network management systems.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.145-155
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• 2004

The design of a communication network has long been a challenging optimization problem. Since the optimal design of a network topology is a well known as a NP-complete problem, many researches have been conducted to obtain near optimal solutions in polynomial time instead of exact optimal solutions. All of these researches suggested diverse heuristic algorithms that can be applied to network design problems. Among these algorithms, a simulated annealing algorithm has been proved to guarantee a good solution for many NP-complete problems. in applying the simulated annealing algorithms to network design problems, generating mechanisms for initial solutions and candidate solutions play an important role in terms of goodness of a solution and efficiency. This study aims at analyzing these mechanisms through experiments, and then suggesting reliable mechanisms.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.115-134
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• 2020

Several studies on machine learning (ML) based self-organizing networks (SONs) have been conducted, specifically for LTE, since studies to apply ML to optimize mobile communication systems started with 2G. However, they are still in the infancy stage. Owing to the complicated KPIs and stringent user requirements of 5G, it is necessary to design the 5G SON engine with intelligence to enable users to seamlessly and unlimitedly achieve connectivity regardless of the state of the mobile communication network. Therefore, in this study, we analyze and summarize the current state of machine learning studies applied to SONs as solutions to the complicated optimization problems that are caused by the unpredictable context of mobile communication scenarios.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.438-445
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• 2007

In this paper, we consider the problem of optimum selection of quality-of-service(QoS) classes in interworking between the networks in a next-generation-network(NGN) environment. After introducing the delay-cost and loss-cost characteristics, we discuss the time-invariant(TI) and time-variant(TV) scenarios. For the TI case, we show that under nearly lossless transmission condition, each network can make its own optimization regardless of other networks. For the TV case, we present sufficient conditions under which the optimum QoS class of each network can be considered fixed with respect to time without considerable degradation in the optimization target. Therefore, under the conditions presented in this paper, the QoS of a flow in each network can be determined solely by considering the characteristics of that network and this QoS class can be held fixed during the flow period.

• ETRI Journal
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• v.42 no.6
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• pp.846-858
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• 2020

The non-orthogonal multiple access (NOMA) technique offers throughput improvement to meet the demands of the future generation of wireless communication networks. The objective of this work is to further improve the throughput by including an underlay cognitive radio network with an existing multi-carrier NOMA network, using cooperative communication. The throughput is maximized by optimal resource allocation, namely, power allocation, subcarrier assignment, relay selection, user pairing, and subcarrier pairing. Optimal power allocation to the primary and secondary users is accomplished in a way that target rate constraints of the primary users are not affected. The throughput maximization is a combinatorial optimization problem, and the computational complexity increases as the number of users and/or subcarriers in the network increases. To this end, to reduce the computational complexity, a dynamic network resource allocation algorithm is proposed for combinatorial optimization. The simulation results show that the proposed network improves the throughput.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1334-1337
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• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

• Korean Management Science Review
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• v.25 no.2
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• pp.73-80
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• 2008

The location area planning is to assign cells to the location areas of a wireless communication network in an optimum manner. The two important cost components are cost of location update and cost of paging that are of conflicting in nature; i.e., minimizing the registration cost might increase the search cost. Hence, it is important to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking cost is a minimum. The complete mobile network is divided into location areas. Each location area consists of a group of cells. In fact this is shown to be an NP-complete problem in an earlier study. In this paper, we use an ant colony optimization method to obtain the best/optimal group of cells for a given a network.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1636-1648
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• 2006

In this paper, we propose a new optimization approach to the rate control problem in a wired-cum-wireless network. A primal-dual interior-point(PDIP) algorithm is used to find the solution of the rate optimization problem. We show a comparison between the dual-based(DB) algorithm and PDIP algorithm for solving the rate control problem in the wired-cum-wireless network. The PDIP algorithm performs much better than the DB algorithm. The PDIP can be considered as an attractive method to solve the rate control problem in network. We also present a numerical example and simulation to illustrate our conclusions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1753-1762
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• 2000

An inverse method is presented to obtain material's flow properties by using small punch test. This procedure employs, as the objective function of inverse analysis, the balance of measured load-di splacement response and calculated one during deformation. In order to guarantee convergence to global minimum, simulated annealing method was adopted to optimize the current objective function. In addition, artificial neural network was used to predict the load-displacement response under given material parameters which is the most time consuming and limits applications of global optimization methods to these kinds of problems. By implementing the simulated annealing for optimization along with calculating load-displacement curve by neural network, material parameters were identified irrespective of initial values within very short time for simulated test data. We also tested the present method for error-containing experimental data and showed that the flow properties of material were well predicted.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.22-27
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• 2020

Content centric networking (CCN) is regarded as promising internet architecture because it can provide network efficiency in terms of bandwidth consumption by separating contents from a specific network location and decrease network congestion events. However, the application of a CCN does not widely consider the side effects of mobile devices, particularly mobile content sources. For content source mobility, a full routing update is required. Therefore, in this study, a route optimization scheme is proposed for mobile content sources in a CCN environment to provide low communication overhead, short download time, and low resource consumption. The proposed scheme establishes a direct path between content requesters and a mobile content source for the exchange of interest and data packets using interest-piggybacked data packets. Based on the inherent CCN naming characteristics, the content source does not know the name prefix of the content consumer, and thus the proposed optimized CCN scheme utilizes the content router in the home domain of the content source.