• 대한수학회지
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• 제40권5호
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• pp.883-899
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• 2003

In this paper, we will introduce the general concepts of generalized multiobjective game, generalized weight Nash equilibria and generalized Pareto equilibria. Next using the fixed point theorems due to Idzik [5] and Kim-Tan [6] , we shall prove the existence theorems of generalized weight Nash equilibria under general hypotheses. And as applications of generalized weight Nash equilibria, we shall prove the existence of generalized Pareto equilibria in non-compact generalized multiobjective game.

• ETRI Journal
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• 제43권1호
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• pp.17-30
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• 2021

At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high-speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C-means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C-means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies-generalized Nash game and cooperative and noncooperative game theory.