Lightweight automatic modulation classification via progressive differentiable architecture search
Automatic modulation classification (AMC) is a key step of signal demodulation that
determines whether the receiver can correctly receive the transmitted signal without prior …
determines whether the receiver can correctly receive the transmitted signal without prior …
Automatic modulation classification: A deep architecture survey
Automatic modulation classification (AMC), which aims to blindly identify the modulation type
of an incoming signal at the receiver in wireless communication systems, is a fundamental …
of an incoming signal at the receiver in wireless communication systems, is a fundamental …
A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends
Automatic modulation classification (AMC) is an important stage in intelligent wireless
communication receivers. It is a necessary process after signal detection, and before …
communication receivers. It is a necessary process after signal detection, and before …
[HTML][HTML] A hybrid model for automatic modulation classification based on residual neural networks and long short term memory
MM Elsagheer, SM Ramzy - Alexandria Engineering Journal, 2023 - Elsevier
This paper introduces a deep learning (DL)-based Automatic Modulation Classification
(AMC) model. Our model is considered to be a receiver with a modulation classifier that is …
(AMC) model. Our model is considered to be a receiver with a modulation classifier that is …
Deep Learning‐Based Solutions for 5G Network and 5G‐Enabled Internet of Vehicles: Advances, Meta‐Data Analysis, and Future Direction
MS Almutairi - Mathematical Problems in Engineering, 2022 - Wiley Online Library
The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new
challenges on the 5G network cybersecurity defense system, resource management …
challenges on the 5G network cybersecurity defense system, resource management …
Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network
In this work, the deep learning (DL)-based fifth-generation (5G) non-orthogonal multiple
access (NOMA) detector is investigated over the independent and identically distributed (iid) …
access (NOMA) detector is investigated over the independent and identically distributed (iid) …
Deep learning-driven opportunistic spectrum access (OSA) framework for cognitive 5G and beyond 5G (B5G) networks
The evolving 5G and beyond 5G (B5G) wireless technologies are envisioned to provide
ubiquitous connectivity and great heterogeneity in communication infrastructure by …
ubiquitous connectivity and great heterogeneity in communication infrastructure by …
Embedding-assisted attentional deep learning for real-world RF fingerprinting of Bluetooth
A scalable and computationally efficient framework is designed to fingerprint real-world
Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) …
Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) …
Radio frequency spectrum sensing by automatic modulation classification in cognitive radio system using multiscale deep CNN
RR Yakkati, RR Yakkati, RK Tripathy… - IEEE sensors …, 2021 - ieeexplore.ieee.org
Automatic modulation categorization (AMC) is used in many applications such as cognitive
radio, adaptive communication, electronic reconnaissance, and non-cooperative …
radio, adaptive communication, electronic reconnaissance, and non-cooperative …
Robust automatic modulation classification using asymmetric trilinear attention net with noisy activation function
Nowadays, automatic modulation classification (AMC) plays an essential role in the
cognitive radio based non-cooperative wireless communication system. Although numerous …
cognitive radio based non-cooperative wireless communication system. Although numerous …