• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4A
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• pp.373-378
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• 2007

In this paper, the Game Theory based power control for OFDM system is studied, which has attained intensive interest as a core artificial intelligent technology for Cognitive Radio and its efficiency is evaluated using performance metrics such as system throughput and fairness. Utility Function for joint user centric and network centric power control is defined and simulation results show that game theory based power control is far better than closed loop power control. The contribution of this paper is to formalize the game theory based power control toward the Cognitive Radio that recognizes and adapts to the radio communication environments.

• 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 Communications and Networks
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• v.18 no.3
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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.

• Journal of Korea Game Society
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• v.21 no.2
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• pp.43-54
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• 2021

The goal of this research is contemplation on whether postmodern design concepts such as parody, simulation, appropriation, pastiche apply to the character design in games, within bounds of postmodernism. In this philosophy, designs display a mixture of advanced culture and mass culture, emphasize philosophical contingency, and appear to be a random collage of ideas. it was perceived that the visual art in game character design is also a projection of the postmodernist philosophy. Upon analyzing this perception, character designs categorized based on the applicability of postmodern designs

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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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.

• The Journal of Bigdata
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• v.4 no.2
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• pp.73-83
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• 2019

Since the emergence of the fourth industrial revolution, data analysis is being conducted in various fields. Distributed data processing has already become essential for the fast processing of large amounts of data. However, in the defense sector, simulation used cannot fully utilize the unstructured data which are prevailing at real environments. In this study, we propose a distributed data processing platform that can be applied to battalion level simulation models to provide visualized data for command decisions during training. 500,000 data points of strategic game were analyzed. Considering the winning factors in the data, variance processing was conducted to analyze the data for the top 10% teams. With the increase in the number of nodes, the model becomes scalable.

• International Journal of Contents
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• v.7 no.4
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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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• v.9 no.3
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1752-1758
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• 2006

Racing game requires plausible physics model that can be simulated in realtime. Minor artifacts in racing games are easily noticed, and any kinds of games should work interactively. It is difficult to model the accurate tire-ground physics and to integrate the model into realtime environments. In this paper, an efficient and effective 'imaginary wall' model was proposed. The method can be easily implemented because of the simplicity of the physical model used, and the result of the simulation is realistic enough for the racing games.