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arXiv 1004.3563cs.NI
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一种基于QoS预配置循环神经网络的超3G网络呼叫接纳控制

A QoS Provisioning Recurrent Neural Network based Call Admission Control for beyond 3G Networks

Ramesh Babu H. S., Gowrishankar, Satyanarayana P. S

摘要:

呼叫接纳控制(CAC)是无线资源管理(RRM)技术中的一种,在下一代网络中对保证用户和应用所需的服务质量(QoS)起着重要作用。提出了一种基于模糊神经网络的多类业务下一代无线网络(NGWN)的呼叫接纳控制决策方法。建议的模糊神经呼叫准入控制(FNCAC)计划是一个综合的CAC模块,结合了语言控制能力的模糊逻辑控制器和学习能力的神经网络。该模型是基于递归径向基函数网络,具有更好的学习和适应性,可用于开发智能系统,以处理在异构网络环境中的传入流量。仿真结果表明,FNCAC算法的性能优于其他两种方法,并且与其他两种方法相比,呼叫阻塞率最小。

英文摘要:

The Call admission control (CAC) is one of the Radio Resource Management (RRM) techniques that plays influential role in ensuring the desired Quality of Service (QoS) to the users and applications in next generation networks. This paper proposes a fuzzy neural approach for making the call admission control decision in multi class traffic based Next Generation Wireless Networks (NGWN). The proposed Fuzzy Neural call admission control (FNCAC) scheme is an integrated CAC module that combines the linguistic control capabilities of the fuzzy logic controller and the learning capabilities of the neural networks. The model is based on recurrent radial basis function networks which have better learning and adaptability that can be used to develop intelligent system to handle the incoming traffic in an heterogeneous network environment. The simulation results are optimistic and indicates that the proposed FNCAC algorithm performs better than the other two methods and the call blocking probability is minimal when compared to other two methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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