• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1336-1356
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• 2017

The spectrum scarcity crisis has resulted in a shortage of resources for many emerging wireless services, and research on dynamic spectrum management has been used to solve this problem. Game theory can allocate resources to users in an economic way through market competition. In this paper, we propose a bidding game-based spectrum allocation mechanism in cognitive radio network. In our framework, primary networks provide heterogeneous wireless service and different numbers of channels, while secondary users have diverse bandwidth demands for transmission. Considering the features of traffic and QoS demands, we design a weighted interference graph-based grouping algorithm to divide users into several groups and construct the non-interference user-set in the first step. In the second step, we propose the dynamic bidding game-based spectrum allocation strategy; we analyze both buyer's and seller's revenue and determine the best allocation strategy. We also prove that our mechanism can achieve balanced pricing schema in competition. Theoretical and simulation results show that our strategy provides a feasible solution to improve spectrum utilization, can maximize overall utility and guarantee users' individual rationality.

• 한국통신학회논문지
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• 제32권4A호
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• pp.373-378
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• 2007

본 논문에서는 CR(Cognitive Radio)의 가장 적합한 인공지능 기술로 주목받고 있는 게임이론을 전력 제어 방식에 적용해 OFDM 시스템 기반의 사용효율과 효용에 대한 성능 평가 결과를 제시하였다. 사용자와 네트워크 동시 최적화를 위한 효용함수식을 정의했으며 모의실험을 통해 FOM(Figure of Merit)과 형평성(Fairness)에서 기존의 전력제어 방식보다 월등한 성능을 입증하였다. 또한 게임이론을 이용한 전력 제어 방식은 통신 환경을 인지하고 연산하여 적합한 최적의 서비스를 제공하는 CR의 여러 분야에 확장 적용 가능성을 제시하였다.

• 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.

• Journal of Communications and Networks
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• 제18권3호
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• pp.411-419
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• 2016

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

• 한국게임학회 논문지
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• 제21권2호
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• pp.43-54
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• 2021

본 연구의 목적은 포스트모더니즘의 사상적 토대 안에서 포스트모던 디자인의 종류인 패러디, 모조, 차용, 패스티쉬를 게임 캐릭터 디자인의 사용에 접목된 예를 고찰하는 연구로 진행되었다. 포스트모더니즘의 사상 속에서 세상의 디자인들은 대중문화와 고급문화가 뒤섞이고, 우연성이 강조되며, 무작위적인 콜라주의 형태를 보인다. 게임 캐릭터 역시 이와 같은 요소들을 가지고 있다. 게임 캐릭터 디자인의 분야 역시 포스트모더니즘적 사상이 투영되어 있을 것이라 판단하였고, 이를 먼저 고찰한 후 한·중·미·일의 국가에서 제작된 영향력 있는 게임을 선정하여, 등장하는 캐릭터를 포스트모던 디자인의 분류에 맞게 분류하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1426-1447
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• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• 한국빅데이터학회지
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• 제4권2호
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• pp.73-83
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• 2019

4차 산업혁명의 기술 등장 이후 대규모 데이터 시대에서 새로운 가치 창출을 위한 데이터 정보 분석은 다양한 분야에서 시도되고 있다. 대용량 데이터를 빠르게 처리하는데 있어서 분산 데이터 처리는 이미 필수적이다. 하지만 아직 국방 분야에서 운용하고 있는 시뮬레이션들은 쌓여 있는 비정형 데이터를 활용할 수 있는 시스템이 미비하다. 이에 본 연구에서는 훈련간 발생하는 문제에 대응하기 위한 지휘결심에 가시화된 데이터를 제공하기 위해서 대대급 규모의 시뮬레이션 모델에 적용 가능한 분산 처리 플랫폼을 제안한다. 전략게임 데이터 50만개를 분석하는 과정으로, 데이터가 가지고 있는 여러 요인들 중 승리요인에 영향을 미치는 요소들을 분석할 수 있게 구현하였다. 결과적으로 상위 10%에 있는 팀들의 데이터를 분석하는 과정에서의 분산처리 사용한 결과를 측정 및 비교 하였다.

• International Journal of Contents
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• 제7권4호
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• pp.6-9
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• 2011

Currently, the argument that humanity will eventually reside in some sort of a simulated environment with unlimited resources is actively being discussed in the realm of science and engineering. This paper addresses the issue from the perspective of computer engineering, more specifically in terms of the future gaming environment which is very likely to be brought to us in the form of virtual reality probably in the not-so-distant future. In so doing, Bostrom's simulation argument[1]and Kurzweil's singularity[5] are reviewed, and how our future adobe indistinguishable from our 'real' reality that may be attained by explosive technological advancement relates to the future gaming environment. The problem of human consciousness which is inevitably intertwined with the issue of living in a virtual reality environment is also addressed.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.683-691
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• 2002

In a game called red and black, you can stake any amount s in your possession. Suppose your goal is 1 and your current fortune is ｆ, with 0 < f < 1. You win back your stake and as much more with probability p and lose your stake with probability, q = 1- p. Ahn(2000) considered optimum strategy for this game with the value of p less than $\frac{1}{2}$ where the house has the advantage over the player. The optimum strategy at any f when p < $\frac{1}{2}$ is to play boldly, which is to bet as much as you can. In this paper we perform the simulation study to show that the Bold strategy is optimum.

• 한국정보통신학회논문지
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• 제10권10호
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• pp.1752-1758
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• 2006

자동차 경주 게임은 실시간에 시뮬레이션 될 수 있는 사실적인 물리 모델을 요구한다. 자동차 경주 게임에서는 움직임의 사소한 오류도 쉽게 눈에 띄며, 다른 게임과 같이 상호작용적으로 동작해야 한다. 타이어와 지면의 정확한 물리를 모델링하여 이를 실시간 환경에 구현하는 것은 어려운 일이다. 이 논문에서는 효율적이며 효과적인 '가상 벽(imaginary wall)' 모델을 제안한다. 이 기법은 사용된 물리 모델의 단순성 때문에 쉽게 구현이 가능하며, 시뮬레이션의 결과가 자동차 경주 게임에 사용하기에 충분한 사실성을 가진다.