Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression
A Jagannath, J Jagannath - Physical Communication, 2022 - Elsevier
Future communication networks must address the scarce spectrum to accommodate
extensive growth of heterogeneous wireless devices. Efforts are underway to address …
extensive growth of heterogeneous wireless devices. Efforts are underway to address …
Machine learning in the air
Thanks to the recent advances in processing speed, data acquisition and storage, machine
learning (ML) is penetrating every facet of our lives, and transforming research in many …
learning (ML) is penetrating every facet of our lives, and transforming research in many …
[引用][C] Guest editorial: Deep learning‐based intelligent communication systems: Using big data analytics
Deep learning and big data analytics can be attributed to recent trends and opportunities in
many research activities and areas such as bioinformatics, beyond 5G and 6G …
many research activities and areas such as bioinformatics, beyond 5G and 6G …
Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be
a key enabler and a leading infrastructure provider in the information and communication …
a key enabler and a leading infrastructure provider in the information and communication …
The rfml ecosystem: A look at the unique challenges of applying deep learning to radio frequency applications
While deep machine learning technologies are now pervasive in state-of-the-art image
recognition and natural language processing applications, only in recent years have these …
recognition and natural language processing applications, only in recent years have these …
An rfml ecosystem: Considerations for the application of deep learning to spectrum situational awareness
While deep learning (DL) technologies are now pervasive in state-of-the-art Computer
Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have …
Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have …
Intelligent wireless communications enabled by cognitive radio and machine learning
The ability to intelligently utilize resources to meet the need of growing diversity in services
and user behavior marks the future of wireless communication systems. Intelligent wireless …
and user behavior marks the future of wireless communication systems. Intelligent wireless …
Deep learning convolutional neural networks for radio identification
Advances in software defined radio (SDR) technology allow unprecedented control on the
entire processing chain, allowing modification of each functional block as well as sampling …
entire processing chain, allowing modification of each functional block as well as sampling …
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 …