Search · four archives
Search · four archives
15 papers · ranked by Valyu relevance
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…
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…
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…
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).…
Fan Wang, Tao Shang, Chenhan Hu, Qing Liu + 1 more
Automatic modulation classification (AMC) plays an important role in intelligent wireless communications. With the rapid development of deep learning in recent years, neural network-based automatic modulation classification methods have become increasingly mature. However, the high complexity and large number of…
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…
Ben Dgani, Israel Cohen, Qiong Wu, Bruce Denby
This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received…
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…
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…
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…
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)…
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…
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…