Robust Inference and Communication: Theory, Algorithms and Performance Analysis

稳健的推理和交流：理论、算法和性能分析

• 批准号: 0729031
• 负责人: Sean Meyn
• 金额: $ 38万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729031&HistoricalAwards=false
• 关键词: Robust; Inference; Communication; Theory; Algorithms

Robust Inference and Communication: Theory, Algorithms and Performance Analysis Sean P. Meyn and Venugopal V. VeeravalliAs sensors and wireless communication become increasingly pervasive, and network topologies increasingly complex, there is an urgent need for new techniques for inference and communication in complex environments, as well as techniques to evaluate their performance. The goal of this investigation is to respond to these needs by following two complementary tracks. The first concerns methods for constructing inference and decoding algorithms for complex models, with possible modeling uncertainty, based on the geometry surrounding Kullback-Leibler (K-L) divergence and related methods developed by the investigators in their prior research. The second track treats performance evaluation and performance improvement. Both performance evaluation and algorithm selection are performed using Monte-Carlo or related sample path learning techniques. These approaches are chosen primarily because of their ease of application when compared to deterministic numerical techniques. The application of Monte-Carlo techniques comes at a price in the form of high variance. Efficient simulation and learning techniques are developed in concert with research on hypothesis testing and communication to construct faster algorithms for performance evaluation and adaptation. In addition to theoretical research on these topics, the investigators will transfer technology to industry and community organizations, including the Motorola Communications Center at Illinois, United Technologies Research Center, Vodafone, and the community wireless group CUWiN. Both graduate and undergraduate students will be engaged in applied and theoretical research.

鲁棒推理和通信：理论、算法和性能分析 Sean P. Meyn 和 Venugopal V. Veeravalli 随着传感器和无线通信变得越来越普遍，网络拓扑日益复杂，迫切需要在复杂环境中进行推理和通信的新技术，以及评估其表现的技术。 这项调查的目标是通过遵循两个互补的途径来满足这些需求。第一个涉及为复杂模型构建推理和解码算法的方法，该模型可能具有建模不确定性，基于 Kullback-Leibler (K-L) 散度的几何形状以及研究人员在先前研究中开发的相关方法。第二个轨道涉及绩效评估和绩效改进。 性能评估和算法选择都是使用蒙特卡罗或相关样本路径学习技术来执行的。 选择这些方法主要是因为与确定性数值技术相比，它们易于应用。蒙特卡罗技术的应用是以高方差为代价的。 高效的模拟和学习技术与假设检验和通信研究相结合，构建更快的性能评估和适应算法。除了这些主题的理论研究外，研究人员还将向行业和社区组织转让技术，包括伊利诺伊州摩托罗拉通信中心、联合技术研究中心、沃达丰和社区无线组织 CUWiN。 研究生和本科生都将从事应用和理论研究。