CAREER: Maximum Partial Likelihood Methods for Communications

我们建议利用最大似然估计的最新扩展，即最大部分似然（MPL）理论，为通信非线性信号处理开发一个统计框架。MPL允许只使用处理时可用的信息对数据进行依赖和丢失的观察和顺序处理。在框架中包含相关数据允许开发在具有内存的源和通道存在的情况下进行联合检测和估计的技术，例如将最大似然序列检测与自适应均衡相结合。本文提出的研究包括基于梯度优化和信息论交替投影的MPL估计开发一类新的实时自适应信号处理算法，研究它们的统计和动态特性，将顺序/复杂度确定纳入方案，并在均衡、联合均衡和序列估计以及可变速率语音编码中实现。PI的教育目标是通过承诺为学生提供必要的理论和实践设施来解决现实世界的问题；帮助他们成长为足智多谋、富有创造力的工程师，能够应对这个日益复杂的学科所带来的挑战。有关该领域的项目和最大部分似然研究的信息可在http://engr.umbc.edu/~adali上找到。

We propose to develop a statistical framework for nonlinear signal processing for communications by using a recent extension of maximum likelihood estimation, the maximum partial likelihood (MPL) theory. MPL allows for dependent and missing observations and sequential processing of data using only the information that is available at the time of processing. The inclusion of the dependent data in the framework allows development of techniques for joint detection and estimation in the presence of sources and channels with memory, for example to combine maximum likelihood sequence detection with adaptive equalization. The proposed research includes development of a new class of real-time adaptive signal processing algorithms based on MPL estimation by using both gradient optimization and information-theoretic alternating projections, study of their statistical and dynamic properties, incorporation of order/complexity determination into the scheme, and implementations in equalization, joint equalization and sequence estimation, and variable rate speech coding. The educatimn goals of the PI are shaped by the commitment to equip the students with the necessary theoretical and practical facilities to solve real-world problems; to help them mature into resourceful and creative engineers who could meet the challenges of a discipline rapidly growing in complexity. Information on projects in this area and on maximum partial likelihood research can be found at http://engr.umbc.edu/~adali.