Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications
Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-
assisted multi-user communication (IRS-MUC) systems. However, different from traditional …
assisted multi-user communication (IRS-MUC) systems. However, different from traditional …
Decentralized federated learning via mutual knowledge transfer
In this article, we investigate the problem of decentralized federated learning (DFL) in
Internet of Things (IoT) systems, where a number of IoT clients train models collectively for a …
Internet of Things (IoT) systems, where a number of IoT clients train models collectively for a …
Deep neural networks for spectrum sensing: a review
As we advance towards 6G communication systems, the number of network devices
continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS) …
continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS) …
Attacking spectrum sensing with adversarial deep learning in cognitive radio-enabled internet of things
Cognitive radio-based Internet of Things (CR-IoT) network provides a solution for IoT
devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR …
devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR …
From cognitive to intelligent secondary cooperative networks for the future internet: Design, advances, and challenges
NA Khalek, W Hamouda - IEEE Network, 2020 - ieeexplore.ieee.org
Cognitive Radio (CR) technology was first introduced to solve the problem of radio spectrum
under-utilization. A cognitive radio network consists of smart radio devices that have the …
under-utilization. A cognitive radio network consists of smart radio devices that have the …
Predictive precoder design for OTFS-enabled URLLC: A deep learning approach
This paper investigates the orthogonal time frequency space (OTFS) transmission for
enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent …
enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent …
Spectrum sensing in cognitive radio: A deep learning based model
Spectrum sensing is an efficient technology for addressing the shortage of spectrum
resources. Widely used methods usually employ model‐based features as the test statistics …
resources. Widely used methods usually employ model‐based features as the test statistics …
Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach
The rapid technological advancements in the current modern world bring the attention of
researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is …
researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is …
Deep STFT-CNN for spectrum sensing in cognitive radio
Z Chen, YQ Xu, H Wang, D Guo - IEEE Communications …, 2020 - ieeexplore.ieee.org
Spectrum sensing is one of the crucial technologies used to solve the shortage of spectrum
resources. In this letter, based on the short-time Fourier transform (STFT) and convolutional …
resources. In this letter, based on the short-time Fourier transform (STFT) and convolutional …
Underwater target recognition based on multi-decision lofar spectrum enhancement: A deep-learning approach
J Chen, B Han, X Ma, J Zhang - Future Internet, 2021 - mdpi.com
Underwater target recognition is an important supporting technology for the development of
marine resources, which is mainly limited by the purity of feature extraction and the …
marine resources, which is mainly limited by the purity of feature extraction and the …