Less data, more knowledge: Building next generation semantic communication networks
Semantic communication is viewed as a revolutionary paradigm that can potentially
transform how we design and operate wireless communication systems. However, despite a …
transform how we design and operate wireless communication systems. However, despite a …
A survey on model-based, heuristic, and machine learning optimization approaches in RIS-aided wireless networks
Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key
enabler for envisioned 6G networks, for the purpose of improving the network capacity …
enabler for envisioned 6G networks, for the purpose of improving the network capacity …
Transformer-empowered 6G intelligent networks: From massive MIMO processing to semantic communication
It is anticipated that 6G wireless networks will accelerate the convergence of the physical
and cyber worlds and enable a paradigm-shift in the way we deploy and exploit …
and cyber worlds and enable a paradigm-shift in the way we deploy and exploit …
[HTML][HTML] Over-the-air federated learning: Status quo, open challenges, and future directions
The development of applications based on artificial intelligence and implemented over
wireless networks is increasingly rapidly and is expected to grow dramatically in the future …
wireless networks is increasingly rapidly and is expected to grow dramatically in the future …
Role of deep learning in wireless communications
Traditional communication system design has always been based on the paradigm of first
establishing a mathematical model of the communication channel, then designing and …
establishing a mathematical model of the communication channel, then designing and …
Privacy-preserving intelligent resource allocation for federated edge learning in quantum Internet
Federated learning (FL) is an emerging technology for empowering various applications that
generate large amounts of data in intelligent cyber-physical systems (ICPS). Though FL can …
generate large amounts of data in intelligent cyber-physical systems (ICPS). Though FL can …
Score-based source separation with applications to digital communication signals
We propose a new method for separating superimposed sources using diffusion-based
generative models. Our method relies only on separately trained statistical priors of …
generative models. Our method relies only on separately trained statistical priors of …
Online meta-learning for hybrid model-based deep receivers
Recent years have witnessed growing interest in the application of deep neural networks
(DNNs) for receiver design, which can potentially be applied in complex environments …
(DNNs) for receiver design, which can potentially be applied in complex environments …
Data augmentation for deep receivers
T Raviv, N Shlezinger - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs) allow digital receivers to learn to operate in complex
environments. To do so, DNNs should preferably be trained using large labeled data sets …
environments. To do so, DNNs should preferably be trained using large labeled data sets …
A space shift keying-based optimization scheme for secure communication in iiot
Security and privacy are vital challenges in Industrial Internet of Things (IIoT) systems due to
the inherent broadcast property of industrial wireless communication. Therefore, how to …
the inherent broadcast property of industrial wireless communication. Therefore, how to …