Adaptive Signal Processing for Communications by Maximum Partial Likelihood Estimation

本研究的目的是开发一个统一的统计框架的非线性信号处理的通信，通过使用最近扩展的最大似然估计，最大偏似然（MPL）估计理论，它允许依赖和缺失的观察，和顺序处理的数据，只使用的信息，是在处理的时候。 前人的研究为一般非线性概率模型下MPL估计的统计分析奠定了理论基础，提供了非常有用的信息论联系。 在框架中包含依赖数据允许解决在存在具有存储器的源和信道的情况下的编码和调制中的问题。 本研究包括一类新的实时自适应信号处理算法的开发MPL估计的基础上，通过使用梯度优化和信息论交替投影，和他们的统计和动态特性的研究和具体的通信应用，如均衡在长距离光纤通信和可变速率语音编码中的浊音/清音/过渡分类的实现。 这项工作的另一个重要部分是调查MPL框架内条件概率模型的选择，对于MPL框架，沿着其他非线性模型，如多项式、有限正态混合和神经网络，分段线性模型用于在非线性结构的近似能力与线性模型的效率和理论可达性之间提供高度期望的折衷。域开发新的自适应算法。 从这项工作中产生的技术很可能导致突破，在实时信号处理的通信通过新的算法与上级性能和在一般理论的非线性技术的通信通过严格的治疗的基础相关的时间序列问题。

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.