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Search · four archives
18 papers · ranked by Valyu relevance
Li Yuan, Yang Chen, O-Joun Lee
The role played by communication technology in daily life is gradually increasing. However, there are problems such as complex types of signals, huge amount of data and noise interference, and the recognition accuracy of existing modulation classification methods is low. Therefore, the study proposes a signal automatic…
Indiwara Nanayakkara, Dehan Jayawickrama, Dasuni Jayawardena, Vijitha Herath + 1 more
—Automatic Modulation Classification (AMC) is a vital component in the development of intelligent and adaptive transceivers for future wireless communication systems. Existing statistically-based blind modulation classification methods for Orthogonal Frequency Division Multiplexing (OFDM) often fail to achieve the…
Chee-An Yu, Young-Kai Chen, C. -C. Jay Kuo
In this work, we propose an interpretable, robust, and lightweight machine learning method for automatic modulation classification (AMC) under dynamic and noisy channel conditions. It is called green automatic modulation classification (GAMC) and targets edge artificial intelligence (AI) with low computational…
Ha-Khanh Le, Van-Phuc Hoang, Van-Sang Doan, Neng Ye
In this paper, a convolutional neural network (CNN) model, named DFENet, is composed of multi-branch blocks for diverse feature extraction (DFE) to improve the accuracy of automatic modulation classification (AMC) in wireless communication systems. The DFE blocks primarily involve advanced processing sub-blocks…
Teng Wu, Quan Zhu, Runze Mao, Changzhen Hu + 1 more
In complex wireless communication environments, automatic modulation classification (AMC) faces two critical challenges: the lack of robustness under low-signal-to-noise ratio (SNR) conditions and the inefficiency of integrating multi-scale feature representations. To address these issues, this paper proposes CAIC-Net…
Chee-An Yu, Young-Kai Chen, C. -C. Jay Kuo
In this work, we propose an efficient and transparent green learning pipeline to address the automatic modulation classification (AMC) problem. This pipeline aims to enable receivers to blindly identify the modulation modes of the incoming signals in a computationally efficient way with a small model size. Our method…
Dinanath Padhya, Krishna Prasad Acharya, Dinesh Baniya Kshatri
—Automatic Modulation Classification (AMC) is a core technology for future wireless communication systems, enabling the identification of modulation schemes without prior knowledge. This capability is essential for applications in cognitive radio, spectrum monitoring, and intelligent communication networks. We propose…
Padma Charan Sahu, Bibhu Prasad, Ratnakar Dash, Debendra Muduli + 3 more
Automatic modulation classification (AMC) is a key component in modern wireless communication systems, supporting efficient spectrum utilization and reliable data transmission. This paper presents a novel AMC model named as XGEM-Net, an explainable grey wolf optimized extreme learning machine model designed for…
Osman Tokluoglu, Emin Keresteci
Automatic modulation classification (AMC) plays a critical role in modern wireless communication systems, particularly in non-cooperative scenarios where prior knowledge of the transmitted signal is unavailable. In this study, a gated recurrent unit (GRU)-based deep learning framework is investigated for the…
Chenxu Wang, Shuang Wang, Lirong Han, Xinyu Hu + 3 more
Deep learning-based AMC methods have achieved remarkable performance, but their practical deployment remains constrained by the high cost of labeled data. Although self-supervised learning (SSL) reduces the reliance on labels, existing SSL-based AMC methods often rely on task-agnostic pretext objectives misaligned with…
Prakash Suman, Yanzhen Qu
This study addresses a key limitation in deep learning Automatic Modulation Classification (AMC) models, which perform well at high signal-to-noise ratios (SNRs) but degrade under noisy conditions due to conventional feature extraction suppressing both discriminative structure and interference. The goal was to develop…
Nurettin Safak, Durdu Can Yerdeyatar, Muhammet Sefa Demirel, Alperen Marasli + 2 more
Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods. This paper proposes an…
Qiancheng Zhang, Hongbing Ji, Lin Li, Jose F. Monserrat
In increasingly complex electromagnetic environments, wireless communication systems face the severe challenge of non-Gaussian impulse noise. The moments of impulse noise tend toward infinity, reducing the distinguishability of signal features and thereby limiting improvements in signal modulation recognition rates.…
Lily Cassandra Paulick, Helia Relaño-Iborra, Torsten Dau
Spectro-temporal modulation (STM) sensitivity has been proposed as a sensitive marker of speech intelligibility in challenging listening conditions, yet the underlying auditory mechanisms involved in STM detection remain incompletely understood. The present study measured STM detection thresholds in young…
Johanna B. Fritzinger, Laurel H. Carney
The neural representation of pitch and timbre in complex sounds has previously been studied using synthetic, controlled stimuli to investigate underlying encoding mechanisms. These studies provide information about how single attributes of sound are represented in the inferior colliculus (IC), a critical hub of the…
Giulio Ruffini
Hierarchical encoding is a structural element of the Free Energy Principle and related information-centric accounts of brain function, but a concrete circuit-level mechanism for it remains elusive. Here we examine Hierarchical Amplitude Modulation (HAM). In this computationally grounded scheme, information is encoded…
Authors not listed
This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…
Onnipekka V. Varis, Ilkka A. Muukkonen, Patrik A. Wikman
The human auditory system represents sounds at multiple levels, from low-level acoustic features to abstract category- and object-level information. Although selective attention enables listening in complex natural soundscapes, it remains unclear which representational levels are modulated by attention and how this…