Adaptive Receivers for Uncertain, Time-Varying Channels

本研究的目标是利用通用分类和数据压缩理论的成果，开发一种非参数自适应通信接收机策略。基于数据“典型性”的分析已成为现代信息论的主要理论和实践概念之一。所谓的“类型”（适当的振幅分布的直方图估计器）不仅被证明是分类和压缩的足够统计量，而且还被证明以指数速度收敛到真实的底层分布。由于基于类型的检测器对信道特性没有先验假设，并且测量提供渐近最佳接收所需的数量，因此这些检测器理论上可以实现与完全了解信道特性并最佳使用信道特性的千里眼接收器相同的指数错误率。在后续传输期间进行的测量，可能在决策反馈范例中使用数据传输本身，可用于跟踪信道变化。该方法将扩展到处理样本间依赖性和彩色噪声，通过使用通道测量以最有效的方式。从数据压缩工作中提取的两种技术，上下文树和Lempel-Ziv（通用）编码，将进行检查，以确定哪一种技术最有效地代表了通道诱导的依赖关系，哪一种技术适应性最强。通过合并基于这两种理论的系统，将开发出一种适应未知时变信道的接收机。它将被证明，该接收机可以用于多用户信道，无线和光纤，几乎没有修改。该研究将发展这种自适应接收机策略的理论基础，并将开发一个完整的接收机软件实现，使用实际通信信道记录作为测试数据。这项工作将由莱斯大学和乔治梅森大学的研究人员密切合作完成。

In this research the goal is to develop a nonparametric adaptive communications receiver strategy by exploiting results from the theory of universal classification and data compression. Analysis based upon the "typicality" of the data has become one of the primary theoretical and practical concepts in modern information theory. The so-called "Type" (appropriate histogram estimators of the amplitude distribution) have not only been shown to be sufficient statistics for both classification and compression, but have also been shown to converge exponentially fast to the true underlying distribution. Because type-based detectors make no a priori assumptions about the channel characteristics and measure the quantities needed to provide asymptotically optimal reception, these detectors can theoretically achieve the same exponential error rate as the clairvoyant receiver that knows the channel characteristics perfectly and uses them optimally. Measurements made during succeeding transmissions, possibly using the data transmissions themselves in a decision feedback paradigm, can be used to track channel variations. The approach will be extended to deal with intersample dependencies and colored noise by using channel measurements in an optimally effective way. Two techniques drawn from data compression work, context trees and Lempel-Ziv (universal) coding, will be examined to determine which represents channel-induced dependencies most efficiently and which is most adaptable. By merging systems based on these two theories, a receiver that adapts to unknown, time-varying channels will be developed. It will be demonstrated that this receiver can be used in multiuser channels, both wireless and optical fiber, with little modification. The research will develop the theoretical underpinnings of this adaptive receiver strategy, and will develop a complete software implementation of the receiver, using as test data actual communication channel recordings. This work will be performed by a close collaboration of researchers from Rice University and George Mason University.