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[电气工程与系统科学] 无线网络中多鼠的控制与管理:一种SDN 方法 [推广有奖]

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能者818 在职认证  发表于 2022-3-7 10:32:00 来自手机 |AI写论文

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摘要翻译:
电信运营商正在使用各种无线接入技术向移动用户提供服务。这一发展强调了对不同鼠的统一控制和管理的要求。尽管在当今的蜂窝网络中存在多个RAT,但每个RAT由一组不同的实体控制。这可能导致整体网络资源的次优利用。在本文中,我们回顾了工业界和学术界提出的多RAT控制的各种体系结构。我们还提出了一种新的基于SDN的网络架构,用于端到端控制和管理不同的鼠。该体系结构是可伸缩的,并且提供了一个框架,用于改进现有文献中的现有体系结构和建议的网络性能。我们的体系结构还提供了一个框架,用于以RAT不可知的方式部署应用程序。它促进了网络切片,并能够向最终用户提供服务质量(QoS)保证。我们还开发了一个基于ns-3的评估平台来评估体系结构提供的性能。使用该平台获得的实验结果证明了我们的体系结构所提供的好处。
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英文标题:
《Control and Management of Multiple RATs in Wireless Networks: An SDN
  Approach》
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作者:
Akshatha Nayak M., Arghyadip Roy, Pranav Jha, Abhay Karandikar
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最新提交年份:
2018
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分类信息:

一级分类:Electrical Engineering and Systems Science        电气工程与系统科学
二级分类:Signal Processing        信号处理
分类描述:Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用,包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、(生物)医学、图像、视频和多模态自然和人为信号,包括通信信号和数据。感兴趣的主题包括:统计信号处理、谱估计和系统辨识;滤波器设计;自适应滤波/随机学习;(压缩)采样、传感和变换域方法,包括快速算法;用于机器学习的信号处理和用于信号处理应用的机器学习;网络与图形信号处理;信号处理中的凸和非凸优化方法;雷达、声纳和传感器阵列波束形成和测向;通信信号处理;低功耗、多核、片上系统信号处理;信息物理系统的传感、通信、分析和优化,如电网和物联网。
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英文摘要:
  Telecom operators are using a variety of Radio Access Technologies (RATs) for providing services to mobile subscribers. This development has emphasized the requirement for unified control and management of diverse RATs. Although multiple RATs co-exist within today's cellular networks, each RAT is controlled by a set of different entities. This may lead to suboptimal utilization of the overall network resources. In this article, we review various architectures for multi-RAT control proposed by both industry and academia. We also propose a novel SDN based network architecture for end-to-end control and management of diverse RATs. The architecture is scalable and provides a framework for improved network performance over the present day architecture and proposals in existing literature. Our architecture also provides a framework for deployment of applications in a RAT agnostic fashion. It facilitates network slicing and enables the provision of Quality of Service (QoS) guarantees to the end user. We have also developed an evaluation platform based on ns-3 to evaluate the performance offered by the architecture. Experimental results obtained using the platform demonstrate the benefits provided by our architecture.
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PDF链接:
https://arxiv.org/pdf/1801.03819
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关键词:无线网络 与管理 无线网 Architecture Applications control management 整体 网络资源 architecture

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