Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu + 2 more
'Yonina C. Eldar'] In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a GNU radio-based data set that mimics the imperfections in a…
Dong Wang, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang + 2 more
'Marcin Woźniak'] In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time-frequency images as inputs to convolutional neural networks (CNNs) or recurrent neural networks (RNNs).…
Hui Han, Zhiyuan Ren, Lin Li, Zhigang Zhu + 1 more
Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key…
Ola Fekry Abd-Elaziz, Mahmoud Abdalla, Rania A. Elsayed
Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural…
Zhan Ge, Hongyu Jiang, Youwei Guo, Jie Zhou + 1 more
A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order…
Yangjie Wei, Shiliang Fang, Xiaoyan Wang
Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio…
Zhenkai Liu, Bibo Zhang, Hao Luo, Hao He + 1 more
Orthogonal time-frequency space (OTFS) modulation has emerged as a promising technology to alleviate the effects of the Doppler shifts in high-mobility environments. As a prerequisite to demodulation and signal processing, automatic modulation classification (AMC) is essential for OTFS systems. However, a very limited…
Clayton Harper, Mitchell A. Thornton, Eric B. Larson
—Automatic modulation classification is a desired feature in many modern software-defined radios. In recent years, a number of convolutional deep learning architectures have been proposed for automatically classifying the modulation used on observed signal bursts. However, a comprehensive analysis of these differing…
Srinivas Rahul Sapireddy, Mostafizur Rahman
— Modulation classification plays a crucial role in wireless communication systems, enabling applications such as cognitive radio, spectrum monitoring, and electronic warfare. Conventional techniques often involve deep learning or complex feature extraction, which, while effective, require substantial computational…
Jiawei Zhang, Tiantian Wang, Zhixi Feng, Shuyuan Yang
Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have…
Hao Zhang, Fuhui Zhou, Qihui Wu, Wei Wu + 1 more
Automatic modulation classification enables intelligent communications and it is of crucial importance in today's and future wireless communication networks. Although many automatic modulation classification schemes have been proposed, they cannot tackle the intra-class diversity problem caused by the dynamic changes…
Jafar Norolahi, Mohammad Mehrnia, Paeiz Azmi
— In this study, an algorithm to blind and automatic modulation classification has been proposed. It well benefits combined machine leaning and signal feature extraction to recognize diverse range of modulation in low signal power to noise ratio (SNR). The presented algorithm contains four. First, it advantages…
Gaurav Duggal, Tejas Gaikwad, Bhupendra Sinha
The Internet of Things (IoT) plays a significant role in building smart cities worldwide. Smart cities use IoT devices to collect and analyze data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication. These new-age networks use artificial intelligence (AI)…
Rui Ding, Hao Zhang, Fuhui Zhou, Qihui Wu + 1 more
—Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the…
Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan, Huaxia Wang + 2 more
'Nikhil Muralidhar' 'Yu-Dong Yao'] Abstract—Automatic Modulation Classification (AMC) is critical for efficient spectrum management and robust wireless communications. However, AMC remains challenging due to the complex interplay of signal interference and noise. In this work, we propose an innovative framework that…
Tomoya Nakai, Naoko Koide-Majima, Shinji Nishimoto
Music genre is an essential category for understanding human musical preferences and is provided based on the abstract categorization upon complex auditory stimuli. Previous neuroimaging studies have reported the involvement of the superior temporal gyrus (STG) in response to general music-related features. However, it…
Sara Magits, Arturo Moncada-Torres, Lieselot Van Deun, Jan Wouters + 2 more
The understanding of speech in noise relies (at least partially) on spectrotemporal modulation sensitivity. This sensitivity can be measured by spectral ripple tests, which can be administered at different presentation levels. However, it is not known how presentation level affects spectrotemporal modulation…
Jonathan Regev, Johannes Zaar, Helia Relaño-Iborra, Torsten Dau
The perception of amplitude modulations (AMs), which is characterized by a frequency-selective process in the modulation domain, is considered critical for speech intelligibility. Previous studies have provided evidence of an age-related decline in AM frequency selectivity, as well as a notable sharpening of AM tuning…
Benjamin Hoar, Weitong Zhang, Shuangning Xu, Rana Deeba + 3 more
For decades, employing cyclic voltammetry for mechanistic investigation demands manual inspection of voltammograms. Here we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a electrochemical probable mechanism among five of the most common ones in homogenous…
Jonathan Regev, Helia Relaño-Iborra, Johannes Zaar, Torsten Dau
The processing and perception of amplitude modulation (AM) in the auditory system reflect a frequency-selective process, often described as a modulation filterbank. Previous studies on perceptual AM masking reported similar results for older listeners with hearing impairment (HI) and young listeners with normal hearing…