论文标题
线性系统的二进制输入重建：性能分析
Binary input reconstruction for linear systems: a performance analysis
论文作者
论文摘要
从其（嘈杂）输出中恢复时间差异线性系统的数字输入是数据传输，反卷积，通道均衡和反向建模领域的重大挑战。在过去的几十年中，为此目的开发了各种算法，涉及不同的模型和性能/复杂性要求。在本文中，我们实施了一种直接的算法，以重建具有已知概率属性的一维线性系统的二进制输入。尽管该算法次优，但该算法具有两个主要优点：它在线工作（鉴于当前的输出测量，它是解码当前输入位的），并且复杂性非常低。此外，我们可以从理论上分析其性能：使用概率度量，马尔可夫过程和迭代的随机函数的结果，我们根据均值正方形误差评估了其长期行为。
Recovering the digital input of a time-discrete linear system from its (noisy) output is a significant challenge in the fields of data transmission, deconvolution, channel equalization, and inverse modeling. A variety of algorithms have been developed for this purpose in the last decades, addressed to different models and performance/complexity requirements. In this paper, we implement a straightforward algorithm to reconstruct the binary input of a one-dimensional linear system with known probabilistic properties. Although suboptimal, this algorithm presents two main advantages: it works online (given the current output measurement, it decodes the current input bit) and has very low complexity. Moreover, we can theoretically analyze its performance: using results on convergence of probability measures, Markov Processes, and Iterated Random Functions we evaluate its long-time behavior in terms of mean square error.
