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.
|Titolo:||Binary input reconstruction for linear systems: A performance analysis|
|Data di pubblicazione:||2013|
|Digital Object Identifier (DOI):||10.1016/j.nahs.2012.07.007|
|Appare nelle tipologie:||1.1 Articolo in rivista|