A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer
This paper presents a systematic and comprehensive survey that reviews the latest research
efforts focused on machine learning (ML) based performance improvement of wireless …
efforts focused on machine learning (ML) based performance improvement of wireless …
A survey of modulation classification using deep learning: Signal representation and data preprocessing
Modulation classification is one of the key tasks for communications systems monitoring,
management, and control for addressing technical issues, including spectrum awareness …
management, and control for addressing technical issues, including spectrum awareness …
Deep learning models for wireless signal classification with distributed low-cost spectrum sensors
This paper looks into the modulation classification problem for a distributed wireless
spectrum sensing network. First, a new data-driven model for automatic modulation …
spectrum sensing network. First, a new data-driven model for automatic modulation …
End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications
This paper presents end-to-end learning from spectrum data-an umbrella term for new
sophisticated wireless signal identification approaches in spectrum monitoring applications …
sophisticated wireless signal identification approaches in spectrum monitoring applications …
Machine learning for wireless link quality estimation: A survey
Since the emergence of wireless communication networks, a plethora of research papers
focus their attention on the quality aspects of wireless links. The analysis of the rich body of …
focus their attention on the quality aspects of wireless links. The analysis of the rich body of …
Real-time radio technology and modulation classification via an LSTM auto-encoder
Identification of the type of communication technology and/or modulation scheme based on
detected radio signal are challenging problems encountered in a variety of applications …
detected radio signal are challenging problems encountered in a variety of applications …
Fusion methods for CNN-based automatic modulation classification
S Zheng, P Qi, S Chen, X Yang - IEEE Access, 2019 - ieeexplore.ieee.org
An automatic modulation classification has a very broad application in wireless
communications. Recently, deep learning has been used to solve this problem and …
communications. Recently, deep learning has been used to solve this problem and …
Intelligent and behavioral-based detection of malware in IoT spectrum sensors
AH Celdrán, PMS Sánchez, MA Castillo… - International Journal of …, 2023 - Springer
Abstract The number of Cyber-Physical Systems (CPS) available in industrial environments
is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a …
is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a …
Reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion for industrial big spectrum data
With the rapid increase of industrial systems, industrial spectrum is stepping into the era of
big data, and at the same time spectrum resources are facing serious shortage. Cognitive …
big data, and at the same time spectrum resources are facing serious shortage. Cognitive …
[HTML][HTML] Fedstellar: A platform for decentralized federated learning
Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …
Machine Learning (ML) models across the participants of a federation while preserving data …