A Simple and Efficient Method for Lowering the Error Floors of Turbo Codes that Use Structured Interleavers

2012 ◽  
Vol 16 (3) ◽  
pp. 392-395 ◽  
Author(s):  
Youssouf Ould-Cheikh-Mouhamedou
Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 633 ◽  
Author(s):  
Josu Etxezarreta Martinez ◽  
Pedro M. Crespo ◽  
Javier Garcia-Frías

Quantum turbo codes (QTC) have shown excellent error correction capabilities in the setting of quantum communication, achieving a performance less than 1 dB away from their corresponding hashing bounds. Existing QTCs have been constructed using uniform random interleavers. However, interleaver design plays an important role in the optimization of classical turbo codes. Consequently, inspired by the widely used classical-to-quantum isomorphism, this paper studies the integration of classical interleaving design methods into the paradigm of quantum turbo coding. Simulations results demonstrate that error floors in QTCs can be lowered significantly, while decreasing memory consumption, by proper interleaving design without increasing the overall decoding complexity of the system.


2009 ◽  
Vol 27 (6) ◽  
pp. 944-953 ◽  
Author(s):  
Ioannis Chatzigeorgiou ◽  
Miguel D. Rodrigues ◽  
Ian Wassell ◽  
Rolando Carrasco

Author(s):  
Guilherme Barufaldi ◽  
Marcus Victor ◽  
Luiz Carlos Sandoval Góes ◽  
ROBERTO GIL ANNES DA SILVA

2019 ◽  
Author(s):  
Joseph John Pyne Simons ◽  
Ilya Farber

Not all transit users have the same preferences when making route decisions. Understanding the factors driving this heterogeneity enables better tailoring of policies, interventions, and messaging. However, existing methods for assessing these factors require extensive data collection. Here we present an alternative approach - an easily-administered single item measure of overall preference for speed versus comfort. Scores on the self-report item predict decisions in a choice task and account for a proportion of the differences in model parameters between people (n=298). This single item can easily be included on existing travel surveys, and provides an efficient method to both anticipate the choices of users and gain more general insight into their preferences.


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