I.V. Sviridova, М.В. Хорошайлова, D.V. Lyalin · Radioengineering · 2024
Problem statement. Deep neural networks are successfully used in channel coding to improve the quality of decoding. However, modern neural channel decoders cannot achieve high decoding performance and low complexity at the same time. To overcome this problem, it is necessary to develop a decoder with a double residual neuron. By integrating residual input and residual learning into the neural channel decoder architecture, it will significantly improve decoding performance while maintaining low complexity. Goal. Development of a decoder architecture with a dual residual neural network, which provides high decoding performance in channel coding with low model complexity and computational costs. Results. The simulation results show that the proposed decoder with a double residual neural network provides a significant increase in decoding performance. Compared to a neural decoder based on a trust propagation algorithm, a decoder with a dual residual neural network provides an additional 0.5-1.8 dB encoding gain compared to different channel codes. Compared to the hypergraph neural decoder, which has the best error correction capability among all existing neural channel decoders, the dual residual neural network decoder also provides similar or even better decoding performance for different channel codes. Practical significance. The results obtained can be used to improve decoding efficiency and improve performance in channel coding. The work was carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation within the framework of the state assignment "youth laboratory" No. FZGM-2024-0003.
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Sumitha Krishnamurthi · OhioLink ETD Center (Ohio Library and Information Network) · 2003
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Amirhossein Pourabdollah · OSF Preprints (OSF Preprints) · 2025
This project constitutes the computational preprocessing phase of a Master's thesis investigating the Neurovisceral Integration Model. It implements a reproducible signal processing pipeline to transform raw Electrocardiography (ECSMP dataset) signals into validated Heart Rate Variability (HRV) biomarkers. By isolating specific autonomic indices (Time-domain, Frequency-domain, and Non-linear), this repository generates the physiological feature matrix required to empirically test the relationship between autonomic regulation and executive function.
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Yaoda Xu (11053765) · Figshare · 2024
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Xinyan Xu · 2024
Turbo codes are among the most effective error correction codes currently in use, providing outstanding performance and a vast array of applications. Even though Turbo codes have the theoretical capacity to approximate the limiting code rate of Shannon's theorem through iterations, the substantial computational complexity of the decoding process poses challenges for the optimization of Turbo codes performance in practical applications. Additionally, the computational delay hampers its use in communication systems that require low latency, such as mobile phones. This paper delves into the coding process of Turbo codes, scrutinizes the decoding concepts, and summarizes Soft Output Viterbi Algorithm, Maximum A Posteriori, and Logarithmic Maximum A Posteriori decoding methods through a systematic review approach. It also compares the decoding performance of these diverse methods considering parameters such as time delay, computational complexity, storage space, and Bit Error Rate. Through our analysis, we found that the Maximum A Posteriori algorithm is the most complex, the Logarithmic Maximum A Posteriori algorithm is somewhat simplified in comparison, and the Soft Output Viterbi Algorithm possesses the least computational complexity. Regarding decoding performance, the Maximum A Posteriori algorithm has the lowest Bit Error Rate, followed by the Logarithmic Maximum A Posteriori algorithm, with the Soft Output Viterbi Algorithm performing the worst. This study provides valuable insights into the key factors for enhancing the performance of Turbo codes from a theoretical perspective. It holds substantial significance for the practical application and future development of Turbo codes.
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Kazi Takpaya · Journal of Electronics (China) · 2003
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Iyer, Pavithran, Jain, Aditya, Bartlett, Stephen D. et al. · ArXiv.org · 2025
Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on Cycle Error Reconstruction (CER), which yields Pauli error rates on the $n$ qubits of an error-correcting code. Although the total number of Pauli error rates needed to describe a general noise process is exponentially large in $n$, we show that only a few of the largest few Pauli error rates are needed and that a heuristic technique can complete the Pauli error distribution for ML decoding from this restricted dataset. Using these techniques, we demonstrate significant performance improvements for decoding quantum codes under a variety of physically relevant error models. For instance, with CER data that constitute merely $1\%$ of the Pauli error rates in the system, we achieve a $10X$ gain in performance compared to the case where decoding is based solely on the fidelity of the underlying noise process. Our conclusions underscore the promise of recent error characterization methods for improving quantum error correction and lowering overheads.
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Jun Heo, K.M. Chugg · 2001
We introduce a modification to the standard iterative decoding (message passing) algorithm that yields improved performance at the cost of higher complexity. This modification is to run multiple iterative decoders, each with a different constraint on a system variable (e.g., input value, state value etc.). This constrained iterative decoding (CID) implements optimal MAP decoding for systems represented by single-cycle graphs (e.g., tail-biting convolutional codes). For more complex graphical models, the CID is suboptimal, but outperforms the standard decoding algorithm because it negates the effects of some cycles in the model. We show that the CID outperforms the standard ID for a serially concatenated convolutional code (SCCC) system and low-density-parity-check (LDPC) code system, especially when the interleaver size is small. Density evolution analysis is used to show how CID improves the convergence relative to that of standard ID by showing that the threshold of the CID is lower than that of the standard ID.
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Lajos Hanzo, T.H. Liew, B.L. Yeap · 2002
This chapter contains sections titled: Introduction BCH Codes Trellis Decoding Soft Input Algebraic Decoding Summary and Conclusions
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Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2005
Elsevier’s Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.
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Patrick M. Donnelly (7304600), Kevin Larson (9740089), Tanya Matskewich (9740092) et al. · Figshare · 2020
<p>Example stimuli from the real-word (A) and the pseudo-word (B) lists with Sound it Out phonemic cues below the highlighted vowels. Bar plots show difference scores (number of words read correctly) from the first and second sessions for both the control and intervention groups on real word (C) and pseudo word (D) lists. Bar heights represent the additional words read on the second session. Error bars reflect +/- 1 SEM. Below the bar plots, violin density plots show group performance on these measures for both sessions with superimposed line plots of individual performance.</p>
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Umut Güçlü (611046), Marcel A. J. van Gerven (397464) · Figshare · 2015
<p>The decoding performance was defined as the accuracy of identifying the 120 images in the validation set from a set of 9264 candidate images. The decoding performance of the SC2 model was significantly higher than that of the GWP2 model. Error bars show 1 SEM across the images in the validation set (bootstrapping method). A more detailed figure that shows the identified images is provided at <a href="http://www.ccnlab.net/research/" target="_blank">http://www.ccnlab.net/research/</a>.</p>
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Emil Janulewicz · 2010
lnternet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats.
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Grace Oletu, Titus Eneh, Predrag Rapajic et al. · 2011
The MMSE-DFE is a well established Intersymbol Interference (ISI) mitigating structure on linear, noisy, and dispersive channels. Time invariant communication channels exhibits ISI for severe frequency-selective channels. In this paper we investigate the iterative decoding with imperfect MMSE decision feedback equalizer using different modulation scheme. We assume that soft outputs from channel decoder are independent identically distributed Gaussian random variables with known mean and variance. The Imperfect MMSE DFE using different modulation outperforms other turbo equalization algorithms of similar computational complexity in terms of bit-error rate. The achieved improvement is up to 3dB for severe frequency-selective channels.
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Simon Huettinger, Johannes Huber · 2000
In this paper we propose random coding bounds as a tool to analyze concatenated codes constructed from convolutional inner and Reed{Solomon outer codes. Using theses bounds we are able to construct hierarchically concatenated codes with nearly equal error protection. These codes can provide a signicantly higher power eciency than a conventional design.
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Tadafumi Takata, Ken‐ichi Tomita, Tadao Kasami et al. · 2002
The authors study closest coset decoding for decomposable codes. They analyse the proposed two-stage suboptimum decoding. The new upper bound, the conventional union bound and the simulation result are shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Emil Janulewicz, Amir H. Banihashemi · 2010
Density evolution is often used to determine the performance of an ensemble of low-density parity-check (LDPC) codes under iterative message-passing algorithms. Conventional density evolution techniques over memoryless channels are based on the independence assumption amongst all the processed messages in variable and check nodes. This assumption is valid for many algorithms such as standard belief propagation (BP) and min-sum (MS) algorithms. However, there are other important iterative algorithms such as successive relaxation (SR) versions of BP and MS, and differential decoding with binary message passing (DD-BMP) algorithm of Mobini et. al., for which this assumption is not valid. The dependence created among messages for these algorithms is due to the introduction of memory in the iterative algorithm. In this work, we propose a model for iterative decoding algorithms with memory which covers SR and DD-BMP algorithms as special cases. Based on this model, we derive a Bayesian network for iterative algorithms with memory over memoryless channels and use this representation to analyze the algorithms using density evolution. The density evolution technique is developed based on truncating the memory of the decoding process and approximating it with a finite order Markov process, and can be implemented efficiently. As an example, we apply our technique to analyze the performance of DD-BMP on regular LDPC code ensembles, and make a number of interesting observations with regard to the performance/complexity trade off of DD-BMP in comparison with BP and MS algorithms.
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Marc Fossorier, S. Liu · 2002
The statistical approach proposed by D. Agrawal and A. Vardy (see IEEE Trans. Inform. Theory, vol.46, pp.60-83, Jan. 2000) to evaluate the error performance of the generalized minimum distance (GMD) decoding is extended to other reliability based decoding algorithms for binary linear block codes, namely Chase-type, combined GMD and Chase-type, and ordered statistic decoding. In all cases, tighter and simpler bounds than previously proposed ones have been obtained with this approach.
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Li Wan, Wenyi Zhang · arXiv (Cornell University) · 2024
Guessing random additive noise decoding (GRAND) is a recently proposed decoding paradigm particularly suitable for codes with short length and high rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits soft information in a simple and effective fashion to schedule its queries, thereby allowing efficient hardware implementation. Compared with maximum likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable performance loss in terms of block error rate (BLER). In order to improve the performance of ORBGRAND while still retaining its amenability to hardware implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose basic idea is to reshuffle the queries of ORBGRAND so that the expected number of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to noticeable gains compared with ORBGRAND and its existing variants, and is only 0.1dB away from ML decoding, for BLER as low as $10^{-6}$.
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Christoph Studer, M. Wenk, Andreas Burg et al. · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2006
Multiple-input multiple-output (MIMO) detection algorithms providing soft information for a subsequent channel decoder pose significant implementation challenges due to their high computational complexity. In this paper, we show how sphere decoding can be used as an efficient tool to implement soft-output MIMO detection with flexible trade-offs between computational complexity and (error rate) performance. In particular, we demonstrate that single tree search, ordered QR decomposition, channel matrix regularization, and log-likelihood ratio clipping are the key ingredients for realizing soft-output MIMO detectors with near max-log performance at a computational complexity that is reasonably close to that of hard-output sphere decoding.
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