• 한국통신학회논문지
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• 제37B권10호
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• pp.956-964
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• 2012

본 논문에서는 동적 셀 선택 기반의 기회적 간섭 정렬 기법을 제안한다. 제안된 방법은 각 사용자의 수신 신호 공간과 간섭 공간을 선택함으로써 기존의 기회적 간섭 정렬 기법을 통해 얻을 수 있는 다중 사용자 다이버시티 이득에 추가적인 선택적 다이버시티 이득을 얻을 수 있다. 제안된 방법의 합용량 성능 검증을 위해 확률 모델을 사용하여 전체 시스템의 합용량 성능을 수학적으로 유도하였다. 전산모의실험을 통해 제안된 방법이 기존의 방법에 비해 성능이 향상됨을 검증하였고 분석한 성능과 실험 결과의 일치함을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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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.

• 제어로봇시스템학회논문지
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• 제6권12호
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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.

• 대한전자공학회논문지TC
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• 제44권5호
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• pp.92-98
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• 2007

다중 셀 OFDMA 환경에서 OFDMA 무선망 제어 부를 사용 할 필요 없이 각 셀에서 독립적으로 자원 할당을 하기 위한 기법을 제안하였다. 셀 간 동일 채널 간섭을 최소화 하도록 설계 된 할당 가능한 부반송파 영역을 각 셀에 정해주고, 이 영역 안에서 동적 자원 할당을 하도록 하였다. 할당 가능 영역의 제한으로 발생하는 frequency selection diversity의 감소와 CCI가 집중되는 현상 사이의 Trade off 관계를 이용하기 위하여, frequency selectivity가 높은 채널을 위한 균등한 할당 영역 경계 (EB) 방식과 selectivity가 낮은 채널 환경을 위한 유연 적 할당 영역 경계 (FB) 방식을 제안하였다. 이러한 할당 영역 경계 방식들을 사용하여, Capacity와 Outage 측면에서 경계 방식을 사용하지 않고 동적 자원 할당을 한 경우에 비하여 우수한 성능을 얻을 수 있었다.

• 한국정보통신학회논문지
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• 제26권10호
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• pp.1517-1524
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• 2022

본 논문에서는 하나의 매크로 기지국과 다수의 소형 기지국들로 구성된 이기종 네트워크를 고려하고, 그 기지국들간 협력적 다중 포인트 전송을 가정한다. 또한, 기지국과 단말간 채널은 경로 손실과 레일레이 페이딩으로 구성된다고 가정한다. 이러한 가정에서 주어진 기지국에 대해 단말이 달성할 수 있는 에너지 효율을 제시하고, 이기종 네트워크의 총 에너지 효율을 최대화하기 위한 동적 셀 선택과 송신 전력 할당의 최적화 문제를 공식화한다. 본 논문에서는 최적화 문제를 해결하기 위하여 비지도 딥러닝 기법을 제안한다. 제안된 딥러닝 기법은 기존의 반복적 수렴 방식의 기법들에 비해서 낮은 복잡도를 갖는 동시에 높은 에너지 효율을 제공하는 것이 가능하다. 시뮬레이션을 통해서 제안된 동적 셀 선택 기법이 최대 신호 대 간섭 및 잡음비 기법과 Lagrangian dual decomposition 기법 보다 높은 에너지 효율 성능을 제공함을 보여주고, 제안된 송신 전력 할당 기법은 최대 에너지 효율을 달성할 수 있는 trust region interior point 기법과 유사한 성능을 제공함을 보여준다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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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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• 제18권4호
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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.

• 한국지능시스템학회논문지
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• 제11권7호
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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.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2008년도 추계 공동 국제학술대회
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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.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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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.