CAREER: A Unified Approach to Iterative and Universal Methods for Signal Processing and Communications

职业：信号处理和通信的迭代和通用方法的统一方法

• 批准号: 0092598
• 负责人: Andrew Singer
• 金额: $ 30万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0092598&HistoricalAwards=false
• 关键词: CAREER; Unified; Approach; Iterative; Universal

Singer ABSTRACT:One of the most difficult aspects of the application of theoretical methods to practical systems lies in the translation of a real-world problem into a manageable theoretical model. Often there are a number of aspects of the problem that are either difficult to model concisely, or that are completely unknown. For example, in many commercial and military applications, compact, efficient devices are desired that can operate in a wide range of possible environments. This research is developing a framework for explicitly dealing with such uncertainties in a number of important areas such as wireless and underwater acoustic communications, signal estimation and detection, and forecasting.In particular, this research leverages some of the new and exciting methods for dealing with uncertainty and complexity from the information theory literature. Problems of uncertainty and variability are being addressed through the development of robust signal processing methods motivated by contemporary applications in lossless source coding and competitive on-line algorithms. Such universal algorithms can provide a particularly effective means for handling uncertainty in a variety of adaptive estimation problems. These include universal estimation and equalization strategies which without prior knowledge of the signal or channel can asymptotically achieve the performance of the optimal filter tuned to the signal or channel in use. These algorithms can be practically implemented and integrated into larger systems with relatively low complexity through the use of graphical models and iterative processing techniques used in the study of turbo codes and low-density parity check codes. Iterative algorithms such as factor graphs provide a natural framework for the joint optimization of a variety of tasks typically treated separately. Motivated by the near-capacity achieving performance of iterative decoding and turbo-codes, this research is developing practical, efficient iterative algorithms for joint detection, estimation and decoding of digital communications over single and multi-user channels with inter-symbol interference. The educational component of this research program includes significant curriculum development at both the undergraduate and graduate levels, and a strong investment in the mentoring of graduate students. A key aspect of this research includes the active involvement of undergraduates in ongoing research.Singer LEVEL OF EFFORT STATEMENT:At the recommended level of support, Andrew Singer will make every attempt to meet the original scope and level of effort of this project.

Singer摘要：将理论方法应用于实际系统的最困难的方面之一在于将现实世界的问题转化为可管理的理论模型。 通常，问题的许多方面要么难以简明地建模，要么完全未知。 例如，在许多商业和军事应用中，需要可以在广泛的可能环境中操作的紧凑、高效的装置。这项研究正在开发一个框架，明确地处理这种不确定性在一些重要领域，如无线和水声通信，信号估计和检测，并forecasting.In特别是，这项研究利用了一些新的和令人兴奋的方法来处理不确定性和复杂性的信息理论文献。 不确定性和可变性的问题正在解决，通过强大的信号处理方法的发展，当代无损源编码和竞争力的在线算法的应用程序的动机。 这样的通用算法可以提供用于处理各种自适应估计问题中的不确定性的特别有效的手段。 这些包括通用的估计和均衡策略，在没有信号或信道的先验知识的情况下，可以渐进地实现调谐到所使用的信号或信道的最佳滤波器的性能。 这些算法可以实际实现，并集成到较大的系统，通过使用图形模型和迭代处理技术，在研究中使用的Turbo码和低密度奇偶校验码的相对较低的复杂性。迭代算法，如因子图提供了一个自然的框架，联合优化的各种任务，通常单独处理。 受迭代译码和Turbo码性能接近容量的启发，本研究致力于在存在符号间干扰的单用户和多用户信道中，开发实用、高效的数字通信联合检测、估计和译码迭代算法。 该研究计划的教育部分包括本科和研究生两个层次的重大课程开发，以及对研究生指导的大力投资。 这项研究的一个关键方面包括本科生积极参与正在进行的研究。歌手努力水平声明：在建议的支持水平，安德鲁辛格将尽一切努力，以满足本项目的原始范围和努力水平。