Blind Adaptive Fractionally-Spaced Linear Equalizer Behavior

盲自适应分数间隔线性均衡器行为

• 批准号: 9509011
• 负责人: C. Richard Johnson
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-01 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9509011&HistoricalAwards=false
• 关键词: Blind; Adaptive; Fractionally; Spaced; Linear

The central theme of this research is the development of a fuller theoretical description of the behavior of memoryless-error- function (or Bussgang) type blind equalization algorithms, in particular CMA, as used in practice with finite-length, fractional spacing, higher-order (non-constant modulus, non-uniformly distributed) source constellations, (deterministic and stochastic) correlated source time series, and modest channel noise. The initial target is the behavior of fractionally-spaced CMA with a correlated (or periodic) source, which appears capable of causing misbehavior in practice. We plan to use analytical tools from topologically based, algebraic-geometry (in particular Bernstein's theorem, relaxation methods, and Morse theory) non typically associated with the study of adaptive filters in addition to averaging theory tools popular in adaptive systems analysis. The ultimate intent is to convert an expanded theory into situation dependent design guidelines for stepsize, length, fractional- spacing, initialization selection, and failure recovery tricks for the use of CMA as a start-up scheme (with a subsequent switch to decision-direction) for high data throughput applications. We plan to translate our analytical advances on CMA to fractionally-spaced realizations of other blind equalizer algorithms of the memoryless error function class (including refinements to CMA), for which actual operating data can be obtained industrially as for CMA. We also plan to use our behavior insights into CMA to help provide fair comparisons on actual operating data to competing schemes such as symbol-spaced decision feedback equalizers and similar complexity algorithms based on second-order correlation statistics of single-input, multiple- output models of fractionally-spaced equalization.

这项研究的中心主题是开发对无内存 - 错误功能（或bussgang）类型的盲人均衡算法的行为的完整理论描述，尤其是CMA，尤其是CMA，在实践中使用有限的，有限的长度，分数间距，高阶高（非恒定模量），非恒定模量，不均匀分布的源源和噪声序列（确定噪声），并确定了噪声量，并进行了噪声序列，并进行了噪声序列，并进行了噪声。 最初的目标是具有相关（或周期性）源的分数间隔CMA的行为，该行为似乎能够在实践中引起行为不当。 我们计划使用基于拓扑的代数几何数（特别是伯恩斯坦定理，放松方法和莫尔斯理论）的分析工具，通常与自适应系统分析中流行的平均理论工具外，通常与自适应滤波器的研究无关。 最终的意图是将扩展的理论转换为情况依赖性设计指南，以进行步骤，长度，分数 - 间距，初始化选择和故障恢复技巧，用于将CMA用作启动方案（随后切换到决策方案）用于高数据吞吐量。 我们计划将CMA的分析进步转化为无内存错误函数类的其他盲人均衡器算法（包括对CMA的细化）的分数间隔实现，对于CMA，可以在工业上获得实际的操作数据。 我们还计划将我们的行为洞察力用于CMA，以帮助对竞争方案进行公平比较，例如基于单输入的二阶相关统计，符号间隔的决策反馈均衡器和类似的复杂性算法，这是分数均值平等的多输出模型。