[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 …
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) …
An efficient specific emitter identification method based on complex-valued neural networks and network compression
Specific emitter identification (SEI) is a promising technology to discriminate the individual
emitter and enhance the security of various wireless communication systems. SEI is …
emitter and enhance the security of various wireless communication systems. SEI is …
Deep learning-based CSI feedback for beamforming in single-and multi-cell massive MIMO systems
The potentials of massive multiple-input multiple-output (MIMO) are all based on the
available instantaneous channel state information (CSI) at the base station (BS). Therefore …
available instantaneous channel state information (CSI) at the base station (BS). Therefore …
Distributed learning for automatic modulation classification in edge devices
Y Wang, L Guo, Y Zhao, J Yang… - IEEE Wireless …, 2020 - ieeexplore.ieee.org
Automatic modulation classification (AMC) is a typical technology for identifying different
modulation types, which has been widely applied into various scenarios. Recently, deep …
modulation types, which has been widely applied into various scenarios. Recently, deep …
Lightweight automatic modulation classification based on decentralized learning
Due to the implementation and performance limitations of centralized learning automatic
modulation classification (CentAMC) method, this paper proposes a decentralized learning …
modulation classification (CentAMC) method, this paper proposes a decentralized learning …
SALDR: Joint self-attention learning and dense refine for massive MIMO CSI feedback with multiple compression ratio
The advantages of massive multiple-input multiple-output (MIMO) techniques depend
heavily on the accuracy of channel state information (CSI). In frequency division duplexing …
heavily on the accuracy of channel state information (CSI). In frequency division duplexing …
Binary neural network aided CSI feedback in massive MIMO system
In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is
essential for the base station (BS) to achieve high performance gain. The CSI matrix needs …
essential for the base station (BS) to achieve high performance gain. The CSI matrix needs …
Deep joint source-channel coding for CSI feedback: An end-to-end approach
The increased throughput brought by MIMO technology relies on the knowledge of channel
state information (CSI) acquired in the base station (BS). To make the CSI feedback …
state information (CSI) acquired in the base station (BS). To make the CSI feedback …
Binarized aggregated network with quantization: Flexible deep learning deployment for CSI feedback in massive MIMO systems
Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better
spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to …
spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to …