Large-scale real-world radio signal recognition with deep learning
TLDR
In this paper, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B).About:
This article is published in Chinese Journal of Aeronautics.The article was published on 2021-10-13 and is currently open access. It has received 57 citations till now. The article focuses on the topics: Deep learning & Computer science.read more
Citations
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A Collaborative V2X Data Correction Method for Road Safety
TL;DR: A cOllaborative vehiClE dAta correctioN method (OCEAN) based on rationality and -learning techniques to correct the error V2X data for ensuring the driving safety of vehicles on the road, which can be deployed on both vehicles and road side unit.
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Radio Frequency Fingerprint Identification Based on Slice Integration Cooperation and Heat Constellation Trace Figure
TL;DR: This work proposes a novel RFF identification method based on heat constellation trace figure (HCTF) and slice integration cooperation (SIC) and shows that this method can achieve higher accuracy than the existing RFF methods.
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NAS-AMR: Neural Architecture Search-Based Automatic Modulation Recognition for Integrated Sensing and Communication Systems
TL;DR: In this article , a neural architecture search (NAS) based AMR method was proposed to automatically adjust the structure and parameters of DNN and find the optimal structure under the combination of training and constraints.
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Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things
TL;DR: In this paper , the authors proposed a new adversarial attack for reducing the sensing accuracy in DL-based spectrum sensing systems, which is an attack method on the training data of a machine learning tool.
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Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning
TL;DR: In this article , a few-shot SEI (FS-SEI) method based on deep metric ensemble learning (DMEL) was proposed for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals.
References
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Over-the-Air Deep Learning Based Radio Signal Classification
TL;DR: An in depth study on the performance of deep learning based radio signal classification for radio communications signals considers a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compares performance between the two approaches across a range of configurations and channel impairments.
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A Survey on Deep Learning: Algorithms, Techniques, and Applications
Samira Pouyanfar,Saad Sadiq,Yilin Yan,Haiman Tian,Yudong Tao,Maria Presa Reyes,Mei-Ling Shyu,Shu-Ching Chen,S. Sitharama Iyengar +8 more
TL;DR: A comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing is presented, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications.
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A Survey of the Usages of Deep Learning for Natural Language Processing
TL;DR: The field of natural language processing has been propelled forward by an explosion in the use of deep learning models over the last several years as mentioned in this paper, which includes several core linguistic processing issues in addition to many applications of computational linguistics.
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6G: Opening New Horizons for Integration of Comfort, Security, and Intelligence
TL;DR: Five 6G core services are identified and two centricities and eight key performance indices are detailed to describe these services, then enabling technologies to fulfill the KPIs are discussed and possible solutions are proposed.
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Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors
TL;DR: In this article, a new data-driven model for automatic modulation classification based on long short term memory (LSTM) is proposed, which learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments.