• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.956-964
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• 2012

An opportunistic interference alignment based on dynamic cell selection is proposed. Since the proposed method can switch the desired signal space and the interference space of received signals, an additional selective diversity gain increases. The performance analysis using probabilistic models provides a mathematical expression for the sum-rate capacity. Simulation examples show that the proposed method achieves the higher sum-rate capacity than that of the conventional opportunistic interference alignment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.1976-1997
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• 2016

This paper investigates the problem of co-tier interference mitigation for dynamic small- cell networks, in which the load of each small-cell varies with the number of active associated small-cell users (SUs). Due to the fact that most small-cell base stations (SBSs) are deployed in an ad-hoc manner, the problem of reducing co-tier interference caused by dynamic loads in a distributed fashion is quite challenging. First, we propose a new distributed channel allocation method for small-cells with dynamic loads and define a dynamic interference graph. Based on this approach, we formulate the problem as a dynamic interference graph game and prove that the game is a potential game and has at least one pure strategy Nash equilibrium (NE) point. Moreover, we show that the best pure strategy NE point minimizes the expectation of the aggregate dynamic co-tier interference in the small-cell network. A distributed dynamic learning algorithm is then designed to achieve NE of the game, in which each SBS is unaware of the probability distributions of its own and other SBSs' dynamic loads. Simulation results show that the proposed approach can mitigate dynamic co-tier interference effectively and significantly outperform random channel selection.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.5
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• pp.92-98
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• 2007

We propose the schemes for the dynamic channel allocation (DCA) in multi-cell OFDMA systems to avoid co-channel interference (CCI) without the additional complexity. The allocatable subcarriers areas, which is designed to avoid CCI among cells, are determined for each cell. Each cell allocates the subcarriers within the allocatable subcarriers area of the cell independently. We consider the trade off between the reduced frequency selection diversity and the amount of CCI on a subcarrier by the determination of allocatable subcarriers area. Hence, the equal allocation bound scheme for the high selectivity channel and the flexible allocation bound scheme for the low selectivity channel are proposed. Through the numerical results, it is confirmed that the proposed schemes have better performance in the aspects of the number of overlapping allocated subcarriers, the capacity and the outage probability compared to the case which does not determined the allocatable subcarriers area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1517-1524
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• 2022

In this paper, we consider a heterogeneous network (HetNet) consisting of one macro base station and multiple small base stations, and assume the coordinated multi-point transmission between the base stations. In addition, we assume that the channel between the base station and the user consists of path loss and Rayleigh fading. Under these assumptions, we present the energy efficiency (EE) achievable by the user for a given base station and we formulate an optimization problem of dynamic cell selection and transmit power allocation to maximize the total EE of the HetNet. In this paper, we propose an unsupervised deep learning method to solve the optimization problem. The proposed deep learning-based scheme can provide high EE while having low complexity compared to the conventional iterative convergence methods. Through the simulation, we show that the proposed dynamic cell selection scheme provides higher EE performance than the maximum signal-to-interference-plus-noise ratio scheme and the Lagrangian dual decomposition scheme, and the proposed transmit power allocation scheme provides the similar performance to the trust region interior point method which can achieve the maximum EE.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.127-130
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• 1998

In this paper, we propose a method of cooperative control(T-cell modeling) and selection of group behavior strategy(B-cell modeling) based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-call respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based of clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.567-579
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• 2016

Several energy saving techniques in cellular wireless networks such as active base station (BS) selection, transmit power budget adaptation and user association have been studied independently or only part of these aspects have been considered together in literature. In this paper, we jointly tackle these three problems and propose an integrated framework, called dynamic cell reconfiguration (DCR). It manages three techniques operating on different time scales for ultimate energy conservation while guaranteeing the quality of service (QoS) level of users. Extensive simulations under various configurations, including the real dataset of BS topology and utilization, demonstrate that the proposed DCR can achieve the performance close to an optimal exhaustive search. Compared to the conventional static scheme where all BSs are always turned on with their maximum transmit powers, DCR can significantly reduce energy consumption, e.g., more than 30% and 50% savings in uniform and non-uniform traffic distribution, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.591-597
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• 2001

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control school is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.634-642
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• 2008

Radio wave is the valuable resources in $21^{st}$ century. It will be widely used in various applications such as DMB, USN, telematics, and home network as well as mobile and wireless communications. Cognitive Radio technology is devised to maximize the utilization of radio resources by sensing near-by spectrum and dynamic and allocating free resources dynamically and adaptively. Wireless links for the secondary user need to be frequently switched to idle frequencies during the transmission of multimedia data in the cognitive radio based system. This may cause delay and information loss, and QoS degradations occur inevitably. The efficient frequency allocation scheme is necessary to support the seamless multimedia service to the secondary user while maintaining QoS of the primary user. This paper suggests a frequency selection scheme which considers other parameters such as cell load, data rate, and available bandwidth than just received signal strength during the frequency selection process. The performance of our proposed scheme is analyzed by simulation.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.