基于Markov更新预测和径向Kronecker神经网络的无缝移动切换

Q3 Engineering
Sridhar Dhandapani, C. Chelliah
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引用次数: 0

摘要

目前流行的个人移动网络架构采用流线型的移动控制系统,完全理解集中在单端,导致数据量大时缺乏动态移动支持。当今的网络需要无缝连接,无论节点位置如何,个人网络(PAN)之间都必须实现连接。本文提出了一种基于Markov更新预测和径向Kronecker神经网络(MRP-RKNN)的PAN无缝移动性优化切换方法。本文采用马尔可夫更新预测模型,结合两跳网络结构,提出了一种转移概率函数,以缓解传统无线通信系统中的持续切换问题。提出的无缝移动性马尔可夫更新预测模型以高效的转移概率显著降低了无缝移动性切换执行时间和切换精度。在pan中,不可避免的低功耗汇聚节点的部署使得移动节点在服务质量(QoS)方面存在许多问题,这是由于高移动性带来的反复切换的复杂性。针对PAN部署中的切换优化问题,本工作提出了一个以成本效益的方式优化切换的模型。在这项工作中,径向Kronecker Delta神经网络用于处理基于接收信号强度和成本指标的频繁切换。在这里,所得到的期望输出是使用径向Kronecker函数作为两个变量的函数来获得的,该函数用于执行优化的切换。仿真结果从切换执行时间、无缝移动性预测精度、移动性切换成本和丢包率等方面验证了本文方法的性能和预测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility
Prevailing personal mobile network architectures make use of streamlined mobility control system, where the complete understanding is concentrated on single-end that results in scarce of dynamic mobility support when data volume is found to be large. The present-day networks necessitate seamless connections regardless of node position and connectivity that has to be accomplished between personal are network (PAN). In this work, a novel method called, Markov Renewal Prediction and Radial Kronecker Neural Network (MRP-RKNN) based optimized handover for seamless mobility in PAN is proposed. By employing a Markov Renewal Prediction model for Seamless Mobility along with the two-hop network architecture, in this paper, we propose a transition probabilities (TP) function to mitigate the persistent handover issue in conventional wireless communication systems. The proposed Markov Renewal Prediction model for Seamless Mobility significantly reduces handover execution time and seamless mobility handover accuracy with efficient transition probabilities. In PANs, the unavoidable deployment of low power sink nodes permits the mobile nodes with many issues in terms of Quality of Service (QoS) due to complication of recurrent handovers due to high mobility. Addressing this issue of handover optimization in the deployment of PAN, this work proposes a model called to optimize the handovers in a cost-efficient manner. In this work, Radial Kronecker Delta Neural Network is utilized for handling frequent handovers based on received signal strength and cost metrics. Here, the resultant desired output is obtained using the Radial Kronecker function being a function of two variables with which optimized handover is performed. Simulation results presented in the study exhibits the performance and prediction rate of the proposed method in terms of handover execution time, seamless mobility prediction accuracy, mobility handover cost and packet loss rate.
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来源期刊
Instrumentation Mesure Metrologie
Instrumentation Mesure Metrologie Engineering-Engineering (miscellaneous)
CiteScore
1.70
自引率
0.00%
发文量
25
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