迭代译码与Turbo均衡：Z-折痕现象
Iterative Decoding and Turbo Equalization: The Z-Crease Phenomenon
摘要：
迭代概率推理，通常被称为软迭代范式，在广泛的通信应用中有很大的用途，包括turbo解码和turbo均衡。分析迭代方法的经典方法不可避免地使用具有总体平均味道的统计和信息理论工具。本文考虑了分块出错率性能，并利用非线性动力学理论对其进行了分析。通过将迭代处理器建模为非线性动力系统，我们报告了一个普遍的“Z-折痕现象“：随着迭代次数的增加，每个块的误差会呈锯齿形或上下波动，而不是单调下降。使用涡轮解码器作为一个例子，我们还报告了几个有趣的运动现象，这是以前没有报道，并出现对应的概念，以及“伪码字”和“停止/捕获集。“我们进一步提出了一个启发式停止标准来控制Z-折痕并确定最佳迭代。我们的停止准则是最有用的控制最坏情况下的每块错误，并有助于显着减少平均迭代次数。
英文摘要：
Iterative probabilistic inference, popularly dubbed the soft-iterative paradigm, has found great use in a wide range of communication applications, including turbo decoding and turbo equalization. The classic approach of analyzing the iterative approach inevitably use the statistical and information-theoretical tools that bear ensemble-average flavors. This paper consider the per-block error rate performance, and analyzes it using nonlinear dynamical theory. By modeling the iterative processor as a nonlinear dynamical system, we report a universal "Z-crease phenomenon:" the zig-zag or up-and-down fluctuation -- rather than the monotonic decrease -- of the per-block errors, as the number of iteration increases. Using the turbo decoder as an example, we also report several interesting motion phenomenons which were not previously reported, and which appear to correspond well with the notion of "pseudo codewords" and "stopping/trapping sets." We further propose a heuristic stopping criterion to control Z-crease and identify the best iteration. Our stopping criterion is most useful for controlling the worst-case per-block errors, and helps to significantly reduce the average-iteration numbers.
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