Adaptive Receivers for Uncertain, Time-Varying Channels

适用于不确定、时变信道的自适应接收器

• 批准号: 9628294
• 负责人: Geoffrey Orsak
• 金额: --
• 依托单位: George Mason University
• 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 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9628294&HistoricalAwards=false
• 关键词: Adaptive; Receivers; Uncertain; Time; Varying

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.

在这项研究中，目标是利用通用分类和数据压缩理论的结果来开发一种非参数自适应通信接收策略。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”(幅度分布的适当直方图估计器)不仅被证明是用于分类和压缩的足够的统计，而且还被证明以指数的速度收敛到真实的基本分布。由于基于类型的检测器不对信道特性进行先验假设，并测量提供渐近最优接收所需的量，因此这些检测器在理论上可以达到与透视式接收器相同的指数误码率，后者完全了解信道特性并最佳地使用它们。在后续传输期间进行的测量，可能使用判决反馈范例中的数据传输本身，可以用来跟踪信道变化。该方法将被扩展到通过以最佳有效的方式使用信道测量来处理样本间相关性和有色噪声。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，将被检查以确定哪种技术最有效地表示通道诱导的依赖关系，哪种技术最具适应性。通过将基于这两种理论的系统合并，将开发出一种适应未知、时变信道的接收机。这将证明，该接收机可以用于多用户信道，无论是无线还是光纤，几乎不需要改动。这项研究将为这种自适应接收器策略奠定理论基础，并将使用实际通信信道记录作为测试数据来开发完整的接收器软件实现。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。