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Accurate and secure clock synchronization in data packet networks

Accurate and secure clock synchronization in data packet networks
数据包网络中准确、安全的时钟同步
批准号:
436747-2012
负责人:
Kunz, Thomas
金额:
$1.46万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2013
资助国家:
加拿大
项目状态:
已结题
起止时间:
2013-01-01 至 2014-12-31

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中文摘要
翻译
这项研究和开发项目是与爱立信加拿大公司合作进行的,延续了之前与北电网络和爱立信加拿大公司的成功合作。我们的研究合作分析和模拟了由相互连接的计算机和无线基站组成的大型复杂网络的时间同步性质。长期目标是开发方法,以高度精确的方式安全地同步大型计算机网络和无线基站。时钟同步的重要性在于它可以使系统达到很高的数据速率。为了实现精确的同步,必须考虑到本地时钟频率、时钟偏置和不同传输延迟的微小差异。这些频率差异表现为时钟漂移。因此,我们需要不断地估计单个时钟漂移和偏差,并解释这些差异。还将有许多连接到GPS接收器,以测量GPS时钟时间。随着连接时钟和启用GPS的基站数量的增加,我们将能够改善时钟同步。长期目标将是将网络的同步作为网络拓扑的功能进行量化。网络拓扑会影响整个网络的传输时延。我们将使用各种系统识别和学习算法的方法来整合整个网络的所有时间戳测量,以实现高度的同步精度。
英文摘要
This research and development project is being carried out in collaboration with Ericsson Canada, continuing on a successful prior collaboration with both Nortel Networks and Ericsson Canada. Our research collaboration analyzes and models the nature of time synchronization across a large and complex network of interconnected computers and wireless base stations. The long term objective is to develop methods to securely synchronize a large network of computers and wireless base stations to a high degree of accuracy. The importance of clock synchronization is so that the system can achieve very high data rates. To achieve accurate synchronization one must account for small differences in local clock frequency, clock bias and varying transmission delays. These frequency differences manifest themselves as clock drift. Therefore, we need to continuously estimate individual clock drift and bias and account for these discrepancies. There will also be a number of connections to GPS receivers that will measure GPS clock time. As the number of connected clocks and GPS enabled base stations increase, we will be able to improve the clock synchronization. The long range goal will be to quantify the synchronization of the network as a function of the network topology. Network topology will affect transmission delays across the network. We will use various methods of system identification and learning algorithms to integrate all the time stamp measurements across the network to achieve a high degree of accuracy in synchronization.
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