[HTML][HTML] Application of reinforcement learning and deep learning in multiple-input and multiple-output (MIMO) systems
The current wireless communication infrastructure has to face exponential development in
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Overview of deep learning-based CSI feedback in massive MIMO systems
Many performance gains achieved by massive multiple-input and multiple-output depend on
the accuracy of the downlink channel state information (CSI) at the transmitter (base station) …
the accuracy of the downlink channel state information (CSI) at the transmitter (base station) …
Data-driven deep learning for automatic modulation recognition in cognitive radios
Automatic modulation recognition (AMR) is an essential and challenging topic in the
development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation …
development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation …
Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback: Design, simulation, and analysis
Massive multiple-input multiple-output (MIMO) is a promising technology to increase link
capacity and energy efficiency. However, these benefits are based on available channel …
capacity and energy efficiency. However, these benefits are based on available channel …
[HTML][HTML] Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges
Future wireless communications are becoming increasingly complex with different radio
access technologies, transmission backhauls, and network slices, and they play an …
access technologies, transmission backhauls, and network slices, and they play an …
UWB NLOS/LOS classification using deep learning method
Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate
indoor position system (IPS). However, indoor environments were full of complex objects …
indoor position system (IPS). However, indoor environments were full of complex objects …
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 …
Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO
This paper shows that deep neural network (DNN) can be used for efficient and distributed
channel estimation, quantization, feedback, and downlink multiuser precoding for a …
channel estimation, quantization, feedback, and downlink multiuser precoding for a …
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 …