课题基金 / 基金详情

Ultra-scalable clock and carrier sychronisation for optical and wireless networks using sequentially-locked optical frequency combs

Ultra-scalable clock and carrier sychronisation for optical and wireless networks using sequentially-locked optical frequency combs
使用顺序锁定光学频率梳实现光学和无线网络的超可扩展时钟和载波同步
批准号:
10089417
负责人:
金额:
$14.38万
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2024
资助国家:
英国
项目状态:
未结题
起止时间:
2024 至 --

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
_所有从设备到设备传输数据的电信系统,无论是通过大陆之间的光纤,还是通过移动电话和无线电天线塔之间的空中传输,基本上都依赖于1\。载波同步,确定用于发送数据的频率(无论是可见光、微波还是无线电波),和2\。时钟同步,确定数据传输速率。因此,这两种类型的同步对现代电信系统的性能都至关重要。此外,时钟和载波同步是精确时间同步的关键--这对于同步英国的关键国家基础设施(CNI)至关重要,包括国家电网中的发电站、我们的铁路以及我们的移动和宽带网络。__我们的建议旨在解决影响英国CNI的一个关键问题:我们的CNI目前是由全球卫星导航系统(GNSS)(如GPS和伽利略)同步的时钟。这是一个重大漏洞:由于太阳风暴、网络攻击、干扰或火山灰阻碍,GNSS提供的同步可能会丢失。另一种选择是通过我们现有的光纤基础设施分发高精度的时钟。然而,这带来了两大研究挑战:1.可扩展性:单个高精度时钟目前最多只能到达约1000个端点,2\。光纤变化:通过光纤分配时钟会导致不准确,原因是光纤介质因温度变化等原因而发生变化。还有一个重大的商业挑战:如何以低成本解决研究挑战1和2。__为了解决这些挑战,我们建议使用耦合光学频率梳,每个光学频率梳输出不同频率的光,所有这些都同步在一起。在这种方法中,来自中心极高时钟精度但昂贵梳子的数千个梳子频率通过100千米光纤同步下游廉价的光学频率梳子。这些下行光频梳各自具有数千个自己的梳频,每个时钟同步一个端点,允许从中央高精度频率梳同步数百万个端点,从而解决了可扩展性挑战。我们通过探索测量和补偿光纤介质变化的新的数字方法来解决光纤变化的挑战。我们通过探索基于超快激光的光学频率梳的小型化来解决成本挑战。我们将在由BT托管的极其精确的光学频率梳与由伦敦大学学院托管并由门希尔光电子公司开发的低成本超快激光频率梳之间的现场试验光纤链路中演示我们的方法。
英文摘要
_All telecommunications systems, which transmit data from device to device, whether through optical fibre between continents or through the air between mobile phones and radio masts, fundamentally rely on 1\. carrier synchronisation, determining the frequency used to send the data (whether that be visible, microwaves, or radio waves), and 2\. clock synchronisation, determining the data transmission rate. Consequently, both types of synchronisation are critical to modern telecommunications system performance. Additionally, clock and carrier synchronisation is key to accurate time synchronisation - essential for synchronising the UK's critical national infrastructure (CNI), including power stations in the National Grid, our railways and our mobile and broadband networks.__Our proposal aims to address a key issue impacting the UK's CNI: our CNI is currently clock synchronised by global satellite navigation systems (GNSSs), such as GPS and Galileo. This is a major vulnerability: synchronisation provided by GNSSs may be lost due to solar storms, cyberattacks, jamming or volcanic ash obstruction. An alternative is to distribute highly accurate clocks through our existing optical fibre infrastructure. However, this brings two major research challenges: 1\. scalability: a single highly accurate clock can currently only reach up to about 1000 endpoints, 2\. optical fibre variation: distribution of clocks through optical fibre introduces inaccuracy due to variation of the fibre medium due to e.g. temperature change. There is also a major commercial challenge: how to address research challenges 1 & 2 at low cost.__To address these challenges, we propose using coupled optical frequency combs, which each output a 'comb' of light of different frequencies, all synchronised together. In this approach, thousands of comb frequencies from a central extremely high clock accuracy but expensive comb each synchronise a downstream inexpensive optical frequency comb through \>100 km optical fibre. These downstream optical frequency combs each have thousands of comb frequencies of their own, each of which clock synchronise an endpoint, allowing synchronisation of millions of endpoints from the central highly accurate frequency comb, addressing the scalability challenge. We address the optical fibre variation challenge by exploring new digital methods of measuring and compensating for the optical fibre medium variation. We address the cost challenge by exploring the miniaturisation of ultra-fast laser-based optical frequency combs. We would demonstrate our approach in a field trial optical fibre link between an extremely accurate optical frequency comb hosted by BT and low-cost ultra-fast laser-based frequency combs hosted at UCL and developed by Menhir Photonics._
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis