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Neural network based admission controller for proximity aware mobile services

机译:基于神经网络的准入控制器,用于接近感知移动服务

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Technological advancement in mobile devices is driving the demand for valued added services. Proximity aware mobile application is one such service which user consumes services offered by service providers in the user's environment. One key issue in providing proximity aware services in a wireless networking environment is congestion control at the server. To handle this problem, an effective connection admission control (CAC) mechanism is required. This paper investigate the feasibility of such a mechanism by comparing simulated back-propagation and learning vector quantization neural networks. The back-propagation neural networks was shown to have a higher performance.
机译:移动设备的技术进步正在推动对增值服务的需求。接近感知移动应用程序就是这样一种服务,用户可以在用户环境中使用服务提供商提供的服务。在无线网络环境中提供邻近感知服务的一个关键问题是服务器的拥塞控制。要解决此问题,需要有效的连接允许控制(CAC)机制。本文通过比较模拟反向传播和学习矢量量化神经网络来研究这种机制的可行性。反向传播神经网络被证明具有更高的性能。

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