临近空间平台自组织网络优化部署的博弈算法

doi:10.3969/j.issn.1001-2400.2013.05.030

Abstract

Abstract:

Aiming at the self-organized networking problem on near space (NS) communication platforms, a distributed optimization method for the deployment of the network on NS platforms is proposed based on the game theoretical learning algorithm. First, the self-organized network deployment on NS platforms is modeled as a potential game, and the optimizing objective is the network’s coverage area and the quality of service. Then the potential game can be solved by the Restricted Spatial Adaptive Play (RSAP) algorithm, which leads the game to a guaranteed Nash equilibrium with convergence in probability. The Nash equilibrium is the extremal solutions to the objective function of the deployment optimization. The game theoretical learning method enables NS platforms to be deployed in a distributed way without the global information on regions to be covered. Simulation results show that the proposed optimization method deploys the nodes of the MANET on demand, and can quickly achieve the optimal configuration.

Key words: near space platform, ad hoc network, game theory, learning algorithm

CLC Number:

TP393

ZONG Ru;GAO Xinbo;PENG Jianhua. Deployment optimization of the self-organized network on near space platforms based on the game theoretical learning algorithm[J].J4, 2013, 40(5): 188-193.

References

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Deployment optimization of the self-organized network on near space platforms based on the game theoretical learning algorithm

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