On hybrid factor graphs and adaptive equalization

A. Loeliger
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引用次数: 3

Abstract

Factor graphs may serve as a unifying framework for a wide variety of system models. Since the work of Wiberg (1996), there has been some awareness that factor graphs are also useful for tasks like equalization, channel estimation, synchronization, etc. In particular, factor graphs would allow the seamless integration of such sub-tasks into some "global" detection or estimation algorithm. A main issue in such applications is how to deal with continuous variables and with hybrid systems involving both discrete and continuous variables. These issues are addressed in the present paper, with a focus on equalization. It is shown that a variety of equalizing algorithms-some of them adaptive, all of them suitable for joint iterative decoding and equalization-fall right out of the general framework. Versions of most of these algorithms have previously been investigated by other authors using other notation, but some algorithms appear to be new.
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混合因子图与自适应均衡
因子图可以作为各种系统模型的统一框架。自Wiberg(1996)的工作以来,人们已经意识到因子图对于均衡、信道估计、同步等任务也很有用。特别是,因子图将允许这些子任务无缝集成到一些“全局”检测或估计算法中。这类应用中的一个主要问题是如何处理连续变量和包含离散变量和连续变量的混合系统。本文讨论了这些问题,重点是均衡问题。结果表明,各种均衡算法(其中一些算法是自适应的,所有算法都适合于联合迭代解码和均衡)都脱离了一般框架。这些算法的大多数版本以前已经被其他作者使用其他符号研究过,但有些算法似乎是新的。
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