Adaptive Signal Processing for Communications by Maximum Partial Likelihood Estimation

通过最大部分似然估计进行通信的自适应信号处理

• 批准号: 9614236
• 负责人: Tulay Adali
• 金额: $ 6万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-01 至 1998-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9614236&HistoricalAwards=false
• 关键词: Adaptive; Signal; Processing; Communications; Maximum

This research aims at developing a unified statistical framework for nonlinear signal processing for communications by using a recent extension of maximum likelihood estimation, the maximum partial likelihood (MPL) estimation theory, which allows for dependent and missing observations, and sequential processing of data using only the information that is available at the time of processing. Previous research has established the theoretical foundation for the statistical analysis of MPL estimation with a general nonlinear probability model and provided a very useful information theoretic connection. The inclusion of the dependent data in the framework allows addressing of problems in coding and modulation in the presence of sources and channels with memory. This research includes development of a new class of realtime adaptive signal processing algorithms based on MPL estimation by using gradient optimization and information theoretic alternating projections, and the study of their statistical and dynamic properties and implementations in specific communications applications such as equalization in long distance optical fiber communications and voiced/unvoiced/transition classification in variable rate speech coding. Another important part of the effort is the investigation of the choices for the conditional probability model within the MPL framework, for which, along with other nonlinear models, such as polynomials, finite normal mixtures, and neural networks, piecewise linear models are used to provide a highly desirable compromise between the approximation ability of nonlinear structures and the efficiency and theoretical accessibility of the linear domain to develop new adaptive algorithms. The techniques which result from this work are very likely to lead to breakthroughs in realtime signal processing for communications via new algorithms with superior performances and in the general theory of nonlinear techniques in communications via rigorous treatment of t he underlying dependent time series problems.

本研究旨在通过使用最大似然估计的最新扩展，最大部分似然（MPL）估计理论，开发用于通信非线性信号处理的统一统计框架，该理论允许依赖和缺失的观察，以及仅使用数据的顺序处理处理时可用的信息。 前期研究为用一般非线性概率模型进行MPL估计的统计分析奠定了理论基础，并提供了非常有用的信息论联系。 将相关数据包含在框架中允许在存在具有存储器的源和通道的情况下解决编码和调制中的问题。 这项研究包括使用梯度优化和信息论交替投影开发基于 MPL 估计的新型实时自适应信号处理算法，并研究其统计和动态特性以及在长距离光均衡等特定通信应用中的实现。光纤通信和可变速率语音编码中的有声/无声/转换分类。 这项工作的另一个重要部分是研究 MPL 框架内条件概率模型的选择，为此，与其他非线性模型（例如多项式、有限正态混合模型和神经网络）一起使用分段线性模型来提供非线性结构的逼近能力与线性域的效率和理论可访问性之间非常理想的折衷，以开发新的自适应算法。 这项工作产生的技术很可能通过具有卓越性能的新算法在通信实时信号处理方面取得突破，并通过严格处理潜在的相关时间序列问题在通信中非线性技术的一般理论方面取得突破。