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Binary input reconstruction for linear systems: A performance analysis
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DOI：10.1016/j.nahs.2012.07.007.png)
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Abstract
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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. (c) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Linear systems
Binary input reconstruction
Online decoding
Markov processes
Iterated random functions
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