scholarly journals Efficient Decoding of Turbo Codes with Nonbinary Belief Propagation

2008 ◽  
Vol 2008 (1) ◽  
Author(s):  
Charly Poulliat ◽  
David Declercq ◽  
Thierry Lestable
Keyword(s):  
Turbo Codes ◽  
Belief Propagation ◽  
Efficient Decoding

scholarly journals A performance enhancement of physical layer at Wi-MAX system with RLDPC code

2021 ◽  
Vol 21 (3) ◽  
pp. 1557
Author(s):  
Sandeep Bawage ◽  
Manjula S ◽  
A. M. Bhavikatti
Keyword(s):  
Turbo Codes ◽  
Error Control ◽  
Belief Propagation ◽  
Performance Enhancement ◽  
Physical Layer ◽  
Error Correction Codes ◽  
Wireless Communication Network ◽  
Channel Codes ◽  
Model Channel ◽  
Forward Error

<span>In present wireless communication network, the error correction codes plays the major role for efficient data transmission in noisy environments. To get minimum BER and PAPR has been the main aim towards the field in forward error control coding. Majority of the researchers has considered turbo codes at specific SNR over AWGN channel but have complexity issues with the iterative output decoder and causes degradation in the Wi-Max network system. In this paper, the author presents and evaluates WiMAX physical layer performance with using MIMO technologies, where a Robust-LDPC technique of coding and decoding in OFDM based WiMAX system is consider. The decoding method of RLDPC has processed by Belief Propagation at the logarithmic domain in an iterative manner, the proposed methodology shows good decoding outcome for RLDPC codes at Rician and Rayleigh channel. Moreover, the applicability of our proposed model channel codes is defined under IEEE Wi-MAX standard and the results analysis is done under different code-rate and modulation schemes.</span>

Efficient decoding of block turbo codes

2018 ◽  
Vol 20 (4) ◽  
pp. 345-353
Author(s):  
Jaeyong Son ◽  
Jun Jin Kong ◽  
Kyeongcheol Yang
Keyword(s):  
Turbo Codes ◽  
Block Turbo Codes ◽  
Efficient Decoding

An efficient decoding algorithm for block turbo codes

2001 ◽  
Vol 49 (1) ◽  
pp. 41-46 ◽  
Author(s):  
S. Dave ◽  
Junghwan Kim ◽  
S.C. Kwatra
Keyword(s):  
Turbo Codes ◽  
Decoding Algorithm ◽  
Block Turbo Codes ◽  
Efficient Decoding

scholarly journals Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology

2001 ◽  
Vol 13 (10) ◽  
pp. 2173-2200 ◽  
Author(s):  
Yair Weiss ◽  
William T. Freeman
Keyword(s):  
Graphical Models ◽  
Turbo Codes ◽  
Markov Random Fields ◽  
Belief Propagation ◽  
Analytical Formula ◽  
Sufficient Conditions ◽  
Posterior Probabilities ◽  
Theoretical Understanding ◽  
Single Loop ◽  
Loopy Belief Propagation

Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. Local “belief propagation” rules of the sort proposed by Pearl (1988) are guaranteed to converge to the correct posterior probabilities in singly connected graphs. Recently, good performance has been obtained by using these same rules on graphs with loops, a method we refer to as loopy belief propagation. Perhaps the most dramatic instance is the near Shannon-limit performance of “Turbo codes,” whose decoding algorithm is equivalent to loopy propagation. Except for the case of graphs with a single loop, there has been little theoretical understanding of loopy propagation. Here we analyze belief propagation in networks with arbitrary topologies when the nodes in the graph describe jointly gaussian random variables. We give an analytical formula relating the true posterior probabilities with those calculated using loopy propagation. We give sufficient conditions for convergence and show that when belief propagation converges, it gives the correct posterior means for all graph topologies, not just networks with a single loop. These results motivate using the powerful belief propagation algorithm in a broader class of networks and help clarify the empirical performance results.

An Accelerated Cue Combination Principle Accounts for Multi-cue Depth Perception

2020 ◽  
Vol 3 (1) ◽  
pp. 10501-1-10501-9
Author(s):  
Christopher W. Tyler
Keyword(s):  
Belief Propagation ◽  
Probability Distributions ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Fusion Model ◽  
Cue Combination ◽  
Depth Estimate ◽  
Appropriate Behavior ◽  
Depth Cues ◽  
Bayesian Averaging

Abstract For the visual world in which we operate, the core issue is to conceptualize how its three-dimensional structure is encoded through the neural computation of multiple depth cues and their integration to a unitary depth structure. One approach to this issue is the full Bayesian model of scene understanding, but this is shown to require selection from the implausibly large number of possible scenes. An alternative approach is to propagate the implied depth structure solution for the scene through the “belief propagation” algorithm on general probability distributions. However, a more efficient model of local slant propagation is developed as an alternative.The overall depth percept must be derived from the combination of all available depth cues, but a simple linear summation rule across, say, a dozen different depth cues, would massively overestimate the perceived depth in the scene in cases where each cue alone provides a close-to-veridical depth estimate. On the other hand, a Bayesian averaging or “modified weak fusion” model for depth cue combination does not provide for the observed enhancement of perceived depth from weak depth cues. Thus, the current models do not account for the empirical properties of perceived depth from multiple depth cues.The present analysis shows that these problems can be addressed by an asymptotic, or hyperbolic Minkowski, approach to cue combination. With appropriate parameters, this first-order rule gives strong summation for a few depth cues, but the effect of an increasing number of cues beyond that remains too weak to account for the available degree of perceived depth magnitude. Finally, an accelerated asymptotic rule is proposed to match the empirical strength of perceived depth as measured, with appropriate behavior for any number of depth cues.

Parallel algorithm for Turbo codes

2010 ◽  
Vol 24 (7) ◽  
pp. 638-642
Author(s):  
Linli Cui ◽  
Fan Yang ◽  
Qicong Peng
Keyword(s):  
Parallel Algorithm ◽  
Turbo Codes
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