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Neural Network Based Vertical Handoff Performance Enhancement in Heterogeneous Wireless Networks

机译:异构无线网络中基于神经网络的垂直切换性能增强

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Next generation wireless network (NGWN) is expected to integrate different access technologies, including cellular network, WLAN and mobile Ad Hoc network, etc., and to support users' seamless communication among different access networks. However, the heterogeneity and the diversity of access networks propose challenges on user handoff performance enhancement and system performance guarantee. In this paper, we address the problem of user QoS enhancement and system performance improvement in heterogeneous integrated system of WLAN and UMTS. Neural network (NN) based access network modeling method is proposed and an adaptive parameter adjustment algorithm based on NN model is presented. Applying the algorithm in handoff source and destination networks, user parameters can be determined optimally. The simulation results show that the proposed scheme enables the handoff user to adapt the destination network environment quickly and the variation of the throughput can be avoided efficiently. Furthermore, the network performance of the handoff source network can also be guaranteed.
机译:下一代无线网络(NGWN)有望集成包括蜂窝网络,WLAN和移动Ad Hoc网络等在内的不同接入技术,并支持用户在不同接入网络之间的无缝通信。然而,接入网络的异构性和多样性给用户切换性能增强和系统性能保证提出了挑战。在本文中,我们解决了WLAN和UMTS异构集成系统中用户QoS增强和系统性能提高的问题。提出了一种基于神经网络的接入网建模方法,提出了一种基于神经网络模型的自适应参数调整算法。将算法应用到切换源和目的网络中,可以最佳地确定用户参数。仿真结果表明,该方案能够使切换用户快速适应目的网络环境,有效避免了吞吐量的变化。此外,还可以保证切换源网络的网络性能。

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