课题基金 / 基金详情

Ultra-High-Capacity Optical Communications and Networking: Signal Processing for High-Data-Rate Optical Communications Systems

Ultra-High-Capacity Optical Communications and Networking: Signal Processing for High-Data-Rate Optical Communications Systems
超高容量光通信和网络:高数据速率光通信系统的信号处理
批准号:
0123409
负责人:
Tulay Adali
金额:
$25.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2002
资助国家:
美国
项目状态:
已结题
起止时间:
2002-01-01 至 2005-12-31

项目摘要

项目成果

Tulay Adali的其他基金

相似基金

相关文献

中文摘要
翻译
adali, TulayU of Maryland-Baltimore county在过去的15年里,骨干通信线路所能处理的最大数据速率增长了5个数量级。商用波分复用系统的出现是实现这一惊人增长的关键技术,它使系统设计人员能够比过去更有效地填充可用带宽。近年来,光纤传输线路中的物理损伤已成为限制可获得数据速率的主要因素。色散、光纤非线性、极化效应和放大器放大的自发发射噪声,所有这些相互作用限制了数据速率和/或传输距离。特别是偏振模色散(PMD),引入了符号间和载波间干扰,是增加已安装的地面光纤系统的传输速率和距离的主要限制。尽管已经注意到信号处理方法对于减轻光通信系统中的PMD和其他损害具有很大的希望,但该领域仍处于起步阶段,目前该领域的活动仅限于“现成的”技术,这些技术没有考虑到光域的特性,因此无法真正利用信号处理提供的可能性。通过引入两个互补研究领域的专业知识:通信信号处理和光通信,本研究通过考虑光传输介质的物理特性,开发了有效的光通信电域(后检测)方法。研究人员介绍了一种新型的光通信接收器结构,该结构利用极化分集,并通过对物理现象的精确建模和使用他们开发的高效仿真技术来研究其性能。这两个研究小组在与拟议工作相关的两个领域都有专门知识:(1)光通信系统的理论和计算研究和建模;(2)通信误差补偿/缓解技术的开发。他们在过去几年的合作已经证明了用这种方法开发的解决方案在光通信系统中具有显着性能提升的潜力。该项目的另一个重要好处是在两个研究团体之间建立了有意义的沟通,并强调了它们充分合作的重要性。这项研究还提供了对通信系统形成更统一观点的可能性。
英文摘要
Proposal #0123409Adali, TulayU of Maryland-Baltimore CountyWithin the last fifteen years, the maximum data rate that a backbone communications line can handle has grown by five orders of magnitude. A key enabling technology for this impressive growth has been theadvent of commercial wavelength-division-multiplexed systems that has allowed systems designers to fill the available bandwidth far more efficiently than in the past. Recently, the physical impairmentsin the optical fiber transmission lines have become the major factors limiting the obtainable data rates. The chromatic dispersion, fiber nonlinearities, polarization effects, and amplified spontaneousemission noise from the amplifiers, all interact limiting the data rates and/or transmission distances. Polarization mode dispersion (PMD), in particular, introduces intersymbol and intercarrier interference and is the primary limitation in increasing transmission rates and distances in installed terrestrial fiber systems. Though it has been noted that signal processing approaches hold great promise for mitigating PMD and other impairments in optical communications systems, the area is still in its infancy, and the current activity in the area is limited to ``off-the-shelf'' techniques that do not take into account characteristics of the optical domain, thus unable to truly take advantage of the possibilities that signal processing offer.By bringing in expertise from two complementary research areas: signal processing for communications and optical communications, this research develops effective electrical domain (post-detection) approaches for optical communications by taking into account the physical properties of the optical transmission medium. The investigators introduce a new class of receiver structures for optical communications that exploit polarization diversity and study their performance by accurate modeling of the physical phenomena and using efficient simulation techniques that they have developed.The two research groups have expertise in both areas relevant to the proposed work: (1) theoretical and computational study and modeling of optical communication systems and (2) development of errorcompensation/mitigation techniques for communications. Their collaboration within the last couple of years has demonstrated the potential of solutions developed with this approach for significant performance gains in optical communications systems. An important additional benefit of the project is establishing meaningful communication between the two research communities and the emphasis on the importance of their full collaboration. The research also offers the potential for a more unified view of communications systems.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Collaborative Research:CISE-ANR:CIF:Small:Learning from Large Datasets - Application to Multi-Subject fMRI Analysis
CIF: Small: Source Separation with an Adaptive Structure for Multi-Modal Data Fusion
CIF: Small: Collaborative Research: Entropy Rate for Source Separation and Model Selection: Applications in fMRI and EEG Analysis
III: Small: Collaborative Research: Canonical Dependence Analysis for Multi-modal Data Fusion and Source Separation
海外基金