17 papers · ranked by Valyu relevance
Guoda Qiu, Ling Liu, Yuejun Wei, Liping Li
—This paper proposes a novel maximum-likelihood (ML) soft-decision decoding framework for linear block codes, termed error-building decoding (EBD). The complete decoding process can be performed using only the parity-check matrix, without requiring any other pre-constructed information (such as trellis diagrams or…
Shaikha S. Al-Qahtani, Siyao Li, Joseph J. Boutros
We establish conditions and give proofs on how an error-correcting code can attain infinite diversity in a time-entanglement quantum key distribution (TE-QKD) reconciliation. The shocking result, never encountered in the literature on coding and communication theory, is that a decoder exhibits an infinite diversity…
Lulu Ding, Kun Wang, Hongmei Zhang, Shaohui Xie + 5 more
DNA storage offers exceptional information density and archival longevity, but is constrained by the complex, heterogeneous errors inherent to synthesis, storage, and sequencing. Conventional error-correction schemes often rely on excessive logical redundancy to mitigate these biochemical imperfections, thereby…
Jingyu Lin, Li Chen, Xiaoqian Ye
—Non-binary linear block codes (NB-LBCs) are an important class of error-correcting codes that are especially competent in correcting burst errors. They have broad applications in modern communications and storage systems. However, efficient soft-decision decoding of these codes remains challenging. This paper proposes…
Lu Xu, Xu Chen, Yixin Ma, Rui Shi + 4 more
Due to the critical role of channel coding, convolutional code recognition has attracted growing interest, particularly in non-cooperative communication scenarios such as spectrum surveillance. Deep learning-based approaches have emerged as promising techniques, offering improved classification performance. However…
Oleg Nesterenkov, Kirill Andreev, Alexey Frolov, Pavel Rybin
This paper studies low-complexity soft-output decoding of turbo product codes with extended Bose--Chaudhuri--Hocquenghem component codes. Recent soft-output from covered space (SOCS) decoding substantially improves the quality of extrinsic information compared with the conventional Chase--Pyndiah decoder, but its…
Sisi Miao, Laurent Schmalen
Generalized product codes (GPCs) combine excellent high-rate performance with low-complexity hardware implementations. We propose the refined dynamic reliability score decoder (\acs{RDRSD}), a hard-message-passing iterative error-and-erasure decoder that uses dynamic reliability scores. Its syndrome-domain…
David Dentelski
To utilize quantum error-correcting codes, a decoder must infer the logical sector from the measured syndrome. Beyond producing a hard logical decision, some decoders provide soft information that estimates the reliability of that decision. For minimum-weight perfect matching (MWPM), a common confidence measure is the…
Raphaël Le Bidan, Ahmad Ismail, Elsa Dupraz, Charbel Abdel Nour
Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. However, this approach still has substantial room for improvement. In this paper, we show how to leverage code automorphisms to enhance the ability of existing SBND models…
Guoming Song, Dongming Pi, Shancheng Zhao, Chi Wan Sung
Product codes (PCs) are widely used in high-speed communication systems due to their attractive trade-off between error-correction performance and complexity. To further meet the rapidly growing demand for higher data rates, soft-aided hard-decision decoders (SA-HDDs) have been developed. In this paper, we present a…
Pengxi Fu, Zhen Wang, Jianxin Guo, Yushuai Zhang + 4 more
Modern communication systems increasingly leverage multiple information streams-including channel observations, statistical models, and contextual knowledge-to enhance decoding reliability. However, the varying and often unpredictable quality of these sources poses a critical challenge: rigid combination rules fail…
Wenbo Shi, Wenlong Xie, Jiashen Hu, Lishan Liu + 1 more
Future wireless networks require channel coding schemes that can provide high reliability, low latency, and strong adaptability under finite-blocklength and structurally heterogeneous transmission scenarios. Globally coupled low-density parity-check (GC-LDPC) codes are promising for such systems because their coupled…
Sophia Gimple, Maxime Verwoert, Laura Marras, Paul Weger + 7 more
Decision-making is an essential cognitive function. It can be impaired due to a number of neurological and psychiatric disorders as well as external factors such as time pressure or stress. To assist users during decision-making, we propose a decision-making brain computer interface (BCI) that can alert to uninformed…
Ömer Karakoç, Samet Memiş, Bahar Sennaroglu, Rajesh Kumar
This study provides a comprehensive evaluation and classification of 35 soft decision-making (SDM) algorithms based on fuzzy parameterized fuzzy soft matrices (fpfs-matrices). Although fpfs-matrices offer a strong mathematical framework for modeling uncertainty, there has been a lack of large-scale comparisons of their…
Alessandro Livi, Manning Zhang, Camillo Padoa-Schioppa, Timothy E. Holy
Economic choices are believed to depend on the orbitofrontal cortex (OFC). Work in primates and rodents indicates that neurons in OFC participate in computing and comparing subjective values, suggesting that different groups of cells constitute the building blocks of a decision circuit. In a recent study (12), we…
Yizheng Liu, Qian Hu, Xing Wang, Damith Herath + 3 more
Soft robotics enables inherently safe, compliant interaction, yet integrating brain-computer interfaces (BCIs) remains hindered by a fundamental mismatch: BCIs typically output low-bandwidth, discrete commands, whereas soft robots possess high-dimensional, nonlinear dynamics. In this position paper, we argue that…
Lorenzo Posani
Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for…