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首页> 外文期刊>Advances in Science, Technology and Engineering Systems >Soft Handoff Evaluation and Efficient Access Network Selection in Next Generation Cellular Systems
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Soft Handoff Evaluation and Efficient Access Network Selection in Next Generation Cellular Systems

机译：下一代蜂窝系统中的软切换评估和有效的接入网络选择

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The increased motivation (by service providers) to offer user-centric and seamless communication services – that satisfies users’ quality of experience (QoE), has manifested a myriad of challenges in the field of wireless communication; and given the increased traffic capacity and sudden explosion of cellular devices, communication systems are constantly threatened by performance related issues – including soft handoff. Although intelligent techniques have evolved to provide solutions to these issues, they are yet to flourish in the area of soft handoff. This contribution therefore proposes a framework that integrates two components: (i) machine learning methodologies: self-organizing map (SOM) and pattern classification – for robust performance evaluation of available soft handoff data; (ii) multiple attribute decision making mechanisms (MADM): the Analytical Hierarchy Process (AHP) – which result feeds the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) – for efficient access network selection. Implementation of component one of the design revealed that SOM enabled a precise visualization of handoff features that influenced the system performance; and the error levels of training, validation and test dataset, with number and percentage of correct and incorrect classifications, were obtained from our pattern classifier. Implementation of component two of the design for four heterogeneous (access) networks indicated that although network two (N2) was selected as best access network by TOPSIS and network three (N3) by Synthetic Extent Analysis (SEA) – a method adopted in a related paper, for a particular application; both TOPSIS and SEA selected N1 as second best alternative access network and network four (N4) as third best alternative network, despite the issue of ranking abnormality in TOPSIS. Further, AHP and TOPSIS can effectively be applied as MADM algorithms in handoff decision framework for selecting the best available network for handoff.
机译：（服务提供商）提供满足用户体验质量（QoE）的，以用户为中心的无缝通信服务的动力越来越大，这表明无线通信领域面临着无数挑战。而且，由于通信能力的提高和蜂窝设备的突然爆炸，通信系统不断受到性能相关问题（包括软切换）的威胁。尽管已经发展了智能技术来为这些问题提供解决方案，但它们在软切换领域尚未兴起。因此，本文稿提出了一个框架，该框架集成了两个组件：（i）机器学习方法：自组织映射（SOM）和模式分类–用于对可用软切换数据进行可靠的性能评估; （ii）多种属性决策机制（MADM）：层次分析法（AHP）–其结果为“类似于理想解决方案的优先顺序排序技术”（TOPSIS）提供了有效的接入网络选择。该设计的第一个组件的实现表明，SOM能够精确可视化影响系统性能的切换功能;从我们的模式分类器中获得训练，验证和测试数据集的错误级别，以及正确和错误分类的数量和百分比。四个异构（访问）网络的设计第二部分的实现表明，尽管TOPSIS选择了第二网络（N2）作为最佳访问网络，而综合范围分析（SEA）选择了第三网络（N3）作为相关网络中采用的一种方法纸，用于特定应用;尽管TOPSIS中存在排名异常的问题，TOPSIS和SEA均选择N1作为第二最佳替代访问网络，并选择第四网络（N4）作为第三最佳替代网络。此外，AHP和TOPSIS可以有效地作为MADM算法应用于切换决策框架，以选择最佳可用网络进行切换。

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