Papers Automatic Modulation Recognition
“Automatic Modulation Recognition” 태그가 달린 논문 19편 · 필터 해제
STF-GCN: A Multi-Domain Graph Convolution Network Method for Automatic Modulation Recognition via Adaptive Correlation
Automatic Modulation Recognition (AMR) is an essential part of Intelligent Transportation System (ITS) dynamic spectrum allocation. However, current deep learning-based AMR (DL-AMR) methods are challenged to extract disc…
Automatic Modulation RecognitionUltralight Signal Classification Model for Automatic Modulation Recognition
The growing complexity of radar signals demands responsive and accurate detection systems that can operate efficiently on resource-constrained edge devices. Existing models, while effective, often rely on substantial com…
Automatic Modulation RecognitionClassificationmodelParameter Estimation based Automatic Modulation Recognition for Radio Frequency Signal
Automatic modulation recognition (AMR) critically contributes to spectrum sensing, dynamic spectrum access, and intelligent communications in cognitive radio systems. The introduction of deep learning has greatly improve…
Automatic Modulation Recognitionparameter estimationMDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis
Recently, deep learning technology has been successfully introduced into Automatic Modulation Recognition (AMR) tasks. However, the success of deep learning is all attributed to the training on large-scale datasets. Such…
Automatic Modulation RecognitionDataset DistillationEnhancing Automatic Modulation Recognition for IoT Applications Using Transformers
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an…
Automatic Modulation RecognitionEdge-computingEnhancing Automatic Modulation Recognition through Robust Global Feature Extraction
Automatic Modulation Recognition (AMR) plays a crucial role in wireless communication systems. Deep learning AMR strategies have achieved tremendous success in recent years. Modulated signals exhibit long temporal depend…
Automatic Modulation RecognitionData AugmentationClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation
With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments …
Automatic Modulation RecognitionKnowledge DistillationClass Information Guided Reconstruction for Automatic Modulation Open-Set Recognition
Automatic Modulation Recognition (AMR) is a crucial technology in the domains of radar and communications. Traditional AMR approaches assume a closed-set scenario, where unknown samples are forcibly misclassified into kn…
Automatic Modulation RecognitionDenoisingMathematical ProofsOpen Set LearningDeep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges
Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is m…
Automatic Modulation RecognitionDeep LearningSelf-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning
Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other…
Automatic Modulation RecognitionRepresentation LearningSelf-Supervised LearningTransfer LearningData-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
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 accu…
AttributeAutomatic Modulation RecognitionClassificationMixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition
With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to automatic …
Automatic Modulation RecognitionClassificationData AugmentationDeep LearningLearning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition
Recent learning-based image classification and speech recognition approaches make extensive use of attention mechanisms to achieve state-of-the-art recognition power, which demonstrates the effectiveness of attention mec…
Automatic Modulation Recognitionimage-classificationImage Classificationspeech-recognition+1An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation
Automatic modulation recognition (AMR) is a promising technology for intelligent communication receivers to detect signal modulation schemes. Recently, the emerging deep learning (DL) research has facilitated high-perfor…
Automatic Modulation RecognitionIntelligent Communicationparameter estimationSafeAMC: Adversarial training for robust modulation recognition models
In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. However, these models have been shown to be susceptible to adversarial perturbations, namely …
Automatic Modulation RecognitionA Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition
Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/q…
Automatic Modulation RecognitionA light neural network for modulation detection under impairments
We present a neural network architecture able to efficiently detect modulation scheme in a portion of I/Q signals. This network is lighter by up to two orders of magnitude than other state-of-the-art architectures workin…
Automatic Modulation RecognitionFully Dense Neural Network for the Automatic Modulation Recognition
Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR. Based on expert experience and spectrograms, they not only increase the difficulty of…
Automatic Modulation RecognitionDeep Neural Networks based Modrec: Some Results with Inter-Symbol Interference and Adversarial Examples
Recent successes and advances in Deep Neural Networks (DNN) in machine vision and Natural Language Processing (NLP) have motivated their use in traditional signal processing and communications systems. In this paper, we …
Automatic Modulation Recognition