• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.1979-1995
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• 2015

Network convergence is regarded as the development tendency of the future wireless networks, for which self-organization paradigms provide a promising solution to alleviate the upgrading capital expenditures (CAPEX) and operating expenditures (OPEX). Self-optimization, as a critical functionality of self-organization, employs a decentralized paradigm to dynamically adapt the varying environmental circumstances while without relying on centralized control or human intervention. In this paper, we present comprehensive surveys of heterogeneous networks (HetNets) and investigate the enhanced self-optimization models. Self-optimization approaches such as dynamic mobile access network selection, spectrum resource allocation and power control for HetNets, etc., are surveyed and compared, with possible methodologies to achieve self-optimization summarized. We hope this survey paper can provide the insight and the roadmap for future research efforts in the self-optimization of convergence networks.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1025-1030
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• 2019

This paper describes SON(: Self Organization Network) technology in LTE networks. The SON is a unique feature of LTE compared to previous cellular systems such as UMTS and GSM, and it is a tool that effectively derives the best performance in the time-varying wireless radio environment. Also, the SON has the ability for the operator to automate the setting of the network, allowing for centralized planning and reducing the need for manual work. The SON is largely divided into three categories: Self-Configuration, Self-Optimization, and Self-Healing. Each large categories has a detailed description of technology, and the technologies in each categories are gathered to complete the technology called the SON. In this paper, we focus on MRO which is one of the Self-Optimization technique in each of the three categories.

• 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.

• Computers and Concrete
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• v.22 no.4
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.574-581
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• 2002

Traditional queueing analyses are very useful for designing a network's capacity and predicting their performances, however most of the predicted results from the queueing analyses are quite different from the realistic measured performance. And recent empirical studies on LAN, WAN, and VBR traffic characteristic have indicated that the models used in the traditional Poisson assumption cannot properly predict the real traffic properties due to underestimation of the long-range dependence of network traffics and self-similar properties. In this paper, It is also shown that the self-similar traffic reflects real Ethernet traffic characteristics by comparing Pareto-like ON/OFF source model which is exactly self-similar model to the traditional Poisson model. It is also performed optimization design and performance analysis of ATM network using Internet traffic characteristics.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.893-900
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• 2019

This paper deals with the analysis of SON (Self Organization Network) technology in LTE networks. SON is a unique LTE feature compared to previous cellular systems UMTS and GSM, and is a cost-effective tool for achieving the best performance in a changing environment. In addition, SON has the function of automating the settings of the network, enabling centralized planning and reducing the need for manual tasks. SON is largely divided into three categories: Self-Configuration, Self-Optimization, and Self-Healing. Each large category has a detailed description, and all the technologies in each category come together to complete the technology called SON. In this paper, we analyzed intensively about ANR among the techniques of Self-Configuration in each of the three categories.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1357-1366
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• 2022

It is important to quickly and accurately build a disaster network or tactical mobile communication network adapting to the field. In configuring the traditional wireless communication systems, the parameters of the base station are set through cell planning. However, for cell planning, information on the environment must be established in advance. If parameters which are not appropriate for the field are used, because they are not reflected in cell planning, additional optimization must be carried out to solve problems and improve performance after network construction. In this paper, we present a rapid mobile communication network construction and optimization method using artificial intelligence and SON technologies in mobile communication base stations. After automatically setting the base station parameters using the CNN model that classifies the terrain with path loss prediction through the DNN model from the location of the base station and the measurement information, the path loss model enables continuous overage/capacity optimization.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.3063-3072
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• 1993

The layout is an important and difficult problem in industrial applications like sheet metal manufacturing, garment making, circuit layout, plant layout, and land development. The module layout problem is known to be non-deterministic polynomial time complete(NP-complete). To efficiently find an optimal layout from a large number of candidate layout configuration a heuristic algorithm could be used. In recent years, a number of researchers have investigated the combinatorial optimization problems by using neural network principles such as traveling salesman problem, placement and routing in circuit design. This paper describes the application of Self-organizing Feature Maps(SOM) of the Kohonen network and Simulated Annealing Algorithm(SAA) to the layout problem of the two-dimensional rectangular shapes.