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 implicit CSI feedback in massive MIMO
Massive multiple-input multiple-output can obtain more performance gain by exploiting the
downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI …
downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI …
Lightweight convolutional neural networks for CSI feedback in massive MIMO
In frequency division duplex mode of massive multiple-input multiple-output systems, the
downlink channel state information (CSI) must be sent to the base station (BS) through a …
downlink channel state information (CSI) must be sent to the base station (BS) through a …
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 …
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 …
CAnet: Uplink-aided downlink channel acquisition in FDD massive MIMO using deep learning
In frequency-division duplexing systems, the downlink channel state information (CSI)
acquisition scheme leads to high training and feedback overhead. In this work, we propose …
acquisition scheme leads to high training and feedback overhead. In this work, we propose …
ShuffleNet-inspired lightweight neural network design for automatic modulation classification methods in ubiquitous IoT cyber–physical systems
J Yin, L Guo, W Jiang, S Hong, J Yang - Computer Communications, 2021 - Elsevier
Automatic modulation classification (AMC) is one of the most important technologies of
cognitive radios and ubiquitous internet of things (IoT) cyber–physical systems, and it can be …
cognitive radios and ubiquitous internet of things (IoT) cyber–physical systems, and it can be …
High-accuracy CSI feedback with super-resolution network for massive MIMO systems
Acquiring accurate channel state information (CSI) is critical for downlink precoding in
frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems …
frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems …
Model-based learning network for 3-D localization in mmWave communications
Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new
opportunities for accurate cooperative localization, in which large bandwidths and antenna …
opportunities for accurate cooperative localization, in which large bandwidths and antenna …
Deep learning based OFDM channel estimation using frequency-time division and attention mechanism
A Yang, P Sun, T Rakesh, B Sun… - 2021 IEEE Globecom …, 2021 - ieeexplore.ieee.org
In this paper, we propose a frequency-time division network (FreqTimeNet) to improve the
performance of deep learning (DL) based OFDM channel estimation. This FreqTimeNet is …
performance of deep learning (DL) based OFDM channel estimation. This FreqTimeNet is …