Auto-encoders in deep learning—a review with new perspectives
S Chen, W Guo - Mathematics, 2023 - mdpi.com
Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …
development of neural networks. The auto-encoder is a key component of deep structure …
A survey of intelligent detection designs of HTML URL phishing attacks
Phishing attacks are a type of cybercrime that has grown in recent years. It is part of social
engineering attacks where an attacker deceives users by sending fake messages using …
engineering attacks where an attacker deceives users by sending fake messages using …
A learning-based end-to-end wireless communication system utilizing a deep neural network channel module
The existing end-to-end (E2E) wireless communication systems require fewer
communication modules and have a simple processing signal flow, compared to …
communication modules and have a simple processing signal flow, compared to …
Performance analysis of neural network-based unified physical layer for indoor hybrid LiFi–WiFi flying networks
The recent developments in unmanned aerial vehicles (UAVs) and indoor hybrid LiFi–WiFi
networks (HLWNs) present a significant opportunity for creating low-cost, power-efficient …
networks (HLWNs) present a significant opportunity for creating low-cost, power-efficient …
Deep Learning-based Joint Optimization of Closed-Loop FDD MmWave Massive MIMO: Pilot Adaptation, CSI Feedback, and Beamforming
J Jee, H Park - IEEE Transactions on Vehicular Technology, 2023 - ieeexplore.ieee.org
To alleviate severe attenuation of millimeter-wave (mmWave) channel, massive multi-input
multi-output (MIMO) in which a large number of antennas are utilized in base station (BS) is …
multi-output (MIMO) in which a large number of antennas are utilized in base station (BS) is …
A deep learning-based adaptive receiver for full-duplex systems
To accommodate the growth of data traffic of 6G and beyond networks, achieving a
significant improvement in spectrum efficiency is inevitable. Full-duplex systems are very …
significant improvement in spectrum efficiency is inevitable. Full-duplex systems are very …
Attention-empowered residual autoencoder for end-to-end communication systems
M Lu, B Zhou, Z Bu - IEEE Communications Letters, 2023 - ieeexplore.ieee.org
Channel autoencoders adopt neural networks to represent and optimize previous block-
driven communication systems from an end-to-end perspective. The existing deep fully …
driven communication systems from an end-to-end perspective. The existing deep fully …
A modified deep learning based MIMO communication for integrated sensing, communication and computing systems
C Duan, J Zhang - Digital Signal Processing, 2023 - Elsevier
The future wireless networks are expected to be data-driven integrated sensing,
communication and computing (ISCC) systems and benefit from employing deep learning to …
communication and computing (ISCC) systems and benefit from employing deep learning to …
[HTML][HTML] End-to-End auto-encoder system for deep residual shrinkage network for AWGN channels
W Zhao, S Hu - Journal of Computer and Communications, 2023 - scirp.org
With the rapid development of deep learning methods, the data-driven approach has shown
powerful advantages over the model-driven one. In this paper, we propose an end-to-end …
powerful advantages over the model-driven one. In this paper, we propose an end-to-end …
Co-GRU enhanced end-to-end design for long-haul coherent transmission systems
In recent years, the end-to-end (E2E) scheme based on deep learning (DL) has been
proposed as a potential scheme to jointly optimize the encoder and the decoder parameters …
proposed as a potential scheme to jointly optimize the encoder and the decoder parameters …