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About 10,000 papers for “Convolutional code”

Some periodic convolutional codes better than any fixed code (Corresp.)

M. Mooser · IEEE Transactions on Information Theory · 1983

Some <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n_{o}=2; k_{o}=1)</tex> noncatastrophic periodic convolutional codes with memory <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M=4</tex> are given that have the same free distance, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d_{\infty}</tex> , as the best fixed code but have both a smaller number of paths of weight <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d_{\infty}</tex> per time instant and a smaller average number of information bit errors per such path for periods <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T=2, 3, 4</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</tex> . It is also shown that an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n_{0}, k_{0})</tex> code of period <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</tex> is equivalent to a fixed code with parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(Tn_{0}, Tk_{0})</tex> .

Cited by 33

An Over-the-Air Multi-User Convolutional Code for URLLC

Rafael Santos, Daniel Castanheira, Adão Silva et al. · IEEE Open Journal of the Communications Society · 2025

URLLC applications impose stringent latency and reliability requirements, making its compliance challenging due to the inherent trade-off between them. These applications typically involve the exchange of small information blocks. Convolutional codes (CC) exhibit near-optimal performance when encoding short blocks. To enable packet-based transmissions, CCs require some kind of termination. A zero-terminated CC (ZTCC) enables efficient maximum likelihood (ML) decoding through the Viterbi algorithm, but suffers from a rate-loss particularly prominent in short blocks. A tail-biting CC (TBCC) avoids rate-loss but entails significantly higher ML decoding complexity. Despite the ZTCC having lower ML decoding complexity and similar error performance, TBCC has received preference by wireless standards, essentially due to ZTCC rate-loss. This work proposes a novel distributed multi-user ZTCC (MU-ZTCC) coding scheme, which eliminates rate-loss by encoding multiple physically separated users over-the-air. Local user data undergoes standard ZTCC encoding followed by multi-user encoding via over-the-air summation. Due to its zero termination, ML decoding of MU-ZTCC is accomplished with a single Viterbi execution. Simulation results show that MU-ZTCC approaches the performance of orthogonal transmissions as SNR increases, while increasing the transmission rate by up to 47% for the selected parameters. This scheme can be viewed as a non-orthogonal multiple access scheme, whose structure enables ML joint detection and decoding with the complexity of standard Viterbi algorithm.

Cited by 1Open access

802.16e WiMAX Using Reed Solomon Code and Convolutional Code under Different Modulation Techniques and Communication Channels

Archana Patil, Pratik Soni, Virendra Verma · 2015

Abstract — An evergreen listening Radio spectrum system informs that future demands must be met using more data throughput wireless technologies. Since system bandwidth is limited and user request goes on to grow. This problem could be solved by WiMAX (Worldwide Interoperability for Microwave Access) technology. WiMAX is proposed by the Institute of Electrical and Electronic Engineers (IEEE) which is standard designated 802.16d-2004 (used in fixed wireless applications) and 802.16e-2005 (mobile wireless) to provide a worldwide interoperability for microwave access. Since WiMAX provides good quality of services. In this paper the performance of WiMAX system is analyzed with the help of Convolutinal code and Reed Solomon code with Convolutional code over different channels like Additive White Gaussian Noise (AWGN) channel and fading channels (Rayleigh and Rician channels) with different modulation techniques such as BPSK, QPSK and QAM (Both 16 and 64) to find out the best performance of physical layer for 802.16e WiMAX. And the performance of each channel is compared. OFDM has been used to achieve high data rate necessary for intensive application. The performance has been concluded based on BER and SNR output through MATLAB Simulation.

Cited by 0

Basic Study on FSK Transmission Using Convolutional Code in Terahertz Band

Masafumi Moriyama, Kazuya Takashima, Hirokazu Sawada et al. · 2024

Terahertz-band wireless transmission enables high-speed communication due to its ultra-wide transmission bandwidth. However, significant propagation losses in the terahertz range make long-distance transmission challenging. Additionally, the development of high-efficiency signal generators, filters, and power amplifiers remains a critical issue for the realization of terahertz communication. In this study, we conducted terahertz transmission experiments under the assumption that the system is power-limited rather than bandwidth-limited. We focused on frequency shift keying (FSK), one of the constant-envelope modulations favorable for power amplification. Furthermore, since forward error correction (FEC) has not been well investigated in terahertz communication experiments, we examined the application of convolutional codes as FEC in this study. We conducted wireless transmission experiments at a carrier frequency of 289.44 GHz and analyzed the results. The experiments demonstrated that the maximum error-free transmission distance for $0.5 \mathrm{Gbit} / \mathrm{s}$ of data bits was as far as 1.5 meters when using convolutional code with block interleaver and soft decision.

Cited by 1

Evaluation of Wireless Body Area Network Utilizing Super Orthogonal Convolutional Code

Kento Takabayashi, Hirokazu Tanaka, Katsumi Sakakibara · 2019

This paper provides an evaluation of IEEE 802.15.6 ultra-wideband (UWB) physical layer (PHY) based wireless body area network (WBAN) using a super orthogonal convolutional code (SOCC) as an error correcting code. Numerical results show that a low coding rate SOCC has better payload error probability performance in a constant process gain case. In addition, small delay is achieved with no retransmission in the case of a low coding rate SOCC.

Cited by 1

Cracking the neural code for word recognition in convolutional neural networks

Aakash Agrawal, Stanislas Dehaene · PLoS Computational Biology · 2024

Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of positions, sizes and fonts. How neural circuits achieve invariant word recognition remains unknown. Here, we address this issue by recycling deep neural network models initially trained for image recognition. We retrain them to recognize written words and then analyze how reading-specialized units emerge and operate across the successive layers. With literacy, a small subset of units becomes specialized for word recognition in the learned script, similar to the visual word form area (VWFA) in the human brain. We show that these units are sensitive to specific letter identities and their ordinal position from the left or the right of a word. The transition from retinotopic to ordinal position coding is achieved by a hierarchy of "space bigram" unit that detect the position of a letter relative to a blank space and that pool across low- and high-frequency-sensitive units from early layers of the network. The proposed scheme provides a plausible neural code for written words in the VWFA, and leads to predictions for reading behavior, error patterns, and the neurophysiology of reading.

Cited by 11Open access

Application of Graph Convolutional Networks to Classification of Building Code Requirements

Fan Yang, Jiansong Zhang · 2024

A building must meet various requirements during the design and construction process to ensure the benefits of stakeholders and well-being of construction workers and occupants. These requirements may come from different functional areas such as structure, electricity, and fire protection, and focus on different building materials, such as concrete, steel, and glass. They may overlap or even conflict with each other. In order to identify the sources and focus of building code requirements and further clarify the relationships between them, this paper presents some recent results on using graphic convolutional networks (GCN) to classify building code requirements. One hundred building code provisions were randomly selected from the International Building Code 2015 and labeled into six categories manually, and a cutting-edge GCN model was trained to classify them. Experimental results showed an average precision of 91.67% and an average recall of 94.44% when 10% of the data was used for testing, which is comparable to the 84.30% precision and 97.30% recall of the state-of-the-art machine learning-based approaches applied on construction document classification. The effect of the size of training data on testing accuracy was also discussed in this paper.

Cited by 1

A Convolutional Attention Network for Extreme Summarization of Source\n Code

Miltiadis Allamanis, Hao Peng, Charles Sutton · arXiv (Cornell University) · 2016

Attention mechanisms in neural networks have proved useful for problems in\nwhich the input and output do not have fixed dimension. Often there exist\nfeatures that are locally translation invariant and would be valuable for\ndirecting the model's attention, but previous attentional architectures are not\nconstructed to learn such features specifically. We introduce an attentional\nneural network that employs convolution on the input tokens to detect local\ntime-invariant and long-range topical attention features in a context-dependent\nway. We apply this architecture to the problem of extreme summarization of\nsource code snippets into short, descriptive function name-like summaries.\nUsing those features, the model sequentially generates a summary by\nmarginalizing over two attention mechanisms: one that predicts the next summary\ntoken based on the attention weights of the input tokens and another that is\nable to copy a code token as-is directly into the summary. We demonstrate our\nconvolutional attention neural network's performance on 10 popular Java\nprojects showing that it achieves better performance compared to previous\nattentional mechanisms.\n

Cited by 323Open access

Performance of rate selective punctured convolutional codes of equal code length

Hui Li, P. Noll · 1991

A family of (PC) codes of rate v/N is presented, where N is constant and even and v can be varied between N/2 and (N-1). These PC codes are called rate selective punctured convolutional codes of equal code length (RSPC codes). From best known rate 1/2 codes, a set of good RSPC codes of rates 7/12 to 11/12 having memory 2<=M<=6 have been derived, and the maximum free distance d/sub f/ and minimum information weight C(d/sub f/) of these RSPC codes are provided. The RSPC coding/Viterbi decoding scheme with soft decision has been simulated. The obtained bit error rate (BER) performances with rates 7/12 to 11/12 for a Gaussian channel are provided.

Cited by 0

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

Shaoxiong Ji, Erik Cambria, Pekka Marttinen · 2020

Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems. The emergence of deep models in natural language processing has boosted the development of automatic assignment methods. However, recent advanced neural architectures with flat convolutions or multi-channel feature concatenation ignore the sequential causal constraint within a text sequence and may not learn meaningful clinical text representations, especially for lengthy clinical notes with long-term sequential dependency. This paper proposes a Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size. Experiments on a real-world clinical dataset empirically show that our model improves the state of the art.

Cited by 18Open access

Graph Convolutional Networks for Mapping Source Code Entities to Architectural Modules

Rakhshanda Jabeen, Morgan Ericsson, Jonas Nordqvist et al. · 2025

Mapping source code entities manually to architectural modules is labor-intensive and time-consuming. Automating this process can help adopt static architecture compliance checking techniques like reflexion modeling. We propose a graph-based semi-supervised learning approach to automate this mapping. Our method leverages syntactic and semantic dependencies extracted from compiled software artifacts and formulates the problem as a node classification task on a graph. We investigate two Graph Convolutional Network (GCN) variants: (1) a homogeneous graph approach that treats all dependencies uniformly and (2) a heterogeneous graph approach that maintains distinct dependency types, utilizing a Relational Graph Convolutional Network (RGCN). The proposed method was evaluated on eight open-source Java systems and benchmarked against the state-of-the-art Naive Bayes classifier. Results demonstrate that the RGCN variant outperforms the baseline, offering improved accuracy for mapping source code entities to architectural modules.

Cited by 0

Improving the Thresholds of Generalized LDPC Codes With Convolutional Code Constraints

Muhammad Umar Farooq, Alexandre Graell i Amat, Michael Lentmaier · IEEE Communications Letters · 2023

CC-GLPDC codes are a class of generalized low-density parity-check (GLDPC) codes where the constraint nodes (CNs) represent convolutional codes. This allows for efficient decoding in the trellis with the forward-backward algorithm, and the strength of the component codes easily can be controlled by the encoder memory without changing the graph structure. In this letter, we extend the class of CC-GLDPC codes by introducing different types of irregularity at the CNs and investigating their effect on the BP and MAP decoding thresholds for the binary erasure channel (BEC). For the considered class of codes, an exhaustive grid search is performed to find the BP-optimized and MAP-optimized ensembles and compare their thresholds with the regular ensemble of the same design rate. The results show that irregularity can significantly improve the BP thresholds, whereas the thresholds of the MAP-optimized ensembles are only slightly different from the regular ensembles. Simulation results for the AWGN channel are presented as well and compared to the corresponding thresholds.

Cited by 1

A modified lightweight quantum convolutional neural network for malicious code detection

Qibing Xiong, Yangyang Fei, Qiming Du et al. · Quantum Science and Technology · 2024

Abstract Quantum neural network fully utilize the respective advantages of quantum computing and classical neural network, providing a new path for the development of artificial intelligence. In this paper, we propose a modified lightweight quantum convolutional neural network (QCNN), which contains a high-scalability and parameterized quantum convolutional layer and a quantum pooling circuit with quantum bit multiplexing, effectively utilizing the computational advantages of quantum systems to accelerate classical machine learning tasks. The experimental results show that the classification accuracy (precision, F1-score) of this QCNN on DataCon2020, Ember and BODMAS have been improved to 96.65% (94.3%, 96.74%), 92.4% (91.01%, 92.53%) and 95.6% (91.99%, 95.78%), indicating that this QCNN has strong robustness as well as good generalization performance for malicious code detection, which is of great significance to cyberspace security.

Cited by 2