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Optimized access point selection with mobility prediction using hidden Markov Model for wireless network

机译：使用隐马尔可夫模型的无线网络迁移率预测优化接入点选择

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摘要

Access point selection is an issue frequently faced by mobile user due to constant movement. By connecting to the best Wireless Local Area Network (WLAN) Access Point (AP), mobile users can enjoy the advantages of power consumption reduction while sustaining good communication quality. In this paper, a new approach to intelligently selecting access point in wireless local area network using Hidden Markov Model (HMM) is proposed. Hidden Markov Model is used as prediction tool to forecast the WLAN AP that can provide optimal Quality of Service (QoS) by observing the location histories of the mobile device. Besides, a location awareness AP selection algorithm is proposed to improve the number of connection to AP with a better signal quality. The effectiveness and performance of the proposed approach is evaluated through simulations and results showed that by using the proposed approach, the number of connection to high signal level AP increased and number of connection to low signal level AP decreased in comparison with conventional approach.
机译：接入点选择是移动用户由于不断移动而经常面临的一个问题。通过连接到最佳的无线局域网（WLAN）接入点（AP），移动用户可以享受降低功耗的优势，同时保持良好的通信质量。提出了一种利用隐马尔可夫模型（HMM）在无线局域网中智能选择接入点的新方法。隐马尔可夫模型用作预测工具，通过观察移动设备的位置历史记录来预测可以提供最佳服务质量（QoS）的WLAN AP。此外，提出了一种位置感知AP选择算法，以提高与AP的连接数量，并提高信号质量。通过仿真评估了该方法的有效性和性能，结果表明，与传统方法相比，该方法与高信号水平AP的连接数量增加，与低信号水平AP的连接数量减少。

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