EAGER: A Multi-User Communication and Information Theoretic Approach to the Sparse Signal Recovery Problem

EAGER：解决稀疏信号恢复问题的多用户通信和信息理论方法

• 批准号: 1144258
• 负责人: Bhaskar Rao
• 金额: $ 29.72万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144258&HistoricalAwards=false
• 关键词: EAGER; Multi; User; Communication; Information

This research project examines the theoretical, algorithmic, and computational issues that arise in compressed sensing (CS) and signal processing problems where there is a need to compute solutions to problems in which the solution vector has many zeros. In addition to the exciting compressed sensing area, this research will benefit numerous signal processing applications where the sparsity constraint on the solution vector naturally arises. Brain imaging techniques such as Magnetoencephalography (MEG) and Electroencephalography (EEG) are currently important examples. Sparse communication channels with large delay spread, high resolution spectral analysis, and direction of arrival estimation, are other important examples. An effective solution to this problem will have significant impact, by providing new and valuable tools to the practicing signal processing engineer. In addition, the tools will be of interest to researchers in cognitive science, neuroscience, and machine learning where sparsity issues naturally arise, such as sparse coding of signals in the brain or learning from data which is often assumed to lie on a low dimensional manifold. This project provides a comprehensive and tighter integration of the compressed sensing field and multi-user information theory. This makes it possible to utilize the rich results available in network information theory which have been successfully applied to the implementation of communication systems. The theoretical tools necessary to enable this integration are being developed by the investigators. This research enables significant advances in both theory and practice in the CS field. The information theoretic insights are leveraged to provide insights on performance limits and guidance on practical CS-based system design. The implementation experience gained from communication systems will be translated to practical algorithm development and efficient CS-based system design.

该研究项目研究压缩感知 (CS) 和信号处理问题中出现的理论、算法和计算问题，这些问题需要计算解向量具有多个零的问题的解。除了令人兴奋的压缩传感领域之外，这项研究还将有益于许多信号处理应用，其中解向量的稀疏性约束自然出现。脑磁图（MEG）和脑电图（EEG）等脑成像技术是目前重要的例子。具有大延迟扩展的稀疏通信信道、高分辨率频谱分析和到达方向估计是其他重要的例子。该问题的有效解决方案将为信号处理工程师提供新的、有价值的工具，从而产生重大影响。此外，这些工具也会引起认知科学、神经科学和机器学习领域的研究人员的兴趣，这些领域自然会出现稀疏问题，例如大脑中信号的稀疏编码或从通常假设位于低维流形上的数据中学习。该项目提供了压缩感知领域和多用户信息理论的全面且更紧密的集成。这使得利用网络信息论中丰富的成果成为可能，这些成果已成功应用于通信系统的实现。研究人员正在开发实现这种整合所需的理论工具。这项研究使计算机科学领域的理论和实践取得了重大进展。利用信息论见解来提供有关性能限制的见解以及基于 CS 的实际系统设计的指导。从通信系统中获得的实施经验将转化为实用的算法开发和高效的基于CS的系统设计。