Results 221 to 230 of about 96,602 (273)

Likelihood-Ratio Approaches to Automatic Modulation Classification

IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 2011
Adaptive modulation and automatic modulation classification are highly demanded in software-defined radio (SDR) for both commercial and military applications. Various design options of automatic classifiers have attracted researchers in developing 3G and 4G wireless communication systems.
MengChu Zhou
exaly   +2 more sources

Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation

IEEE Transactions on Vehicular Technology, 2018
In orthogonal frequency division multiplexing (OFDM) with index modulation, the modulation parameters to be classified include both the signal constellation and the number of active subcarriers. This is different from conventional OFDM schemes where only the signal constellation needs to be classified.
Jianping Zheng
exaly   +2 more sources

A Front End for Discriminative Learning in Automatic Modulation Classification

IEEE Communications Letters, 2011
This work presents a novel method for automatic modulation classification based on discriminative learning. The features are the ordered magnitude and phase of the received symbols at the output of the matched filter. The results using the proposed front end and support vector machines are compared to other techniques.
Claudomir Cardoso, Aldebaro Klautau
exaly   +2 more sources

Automatic Modulation Classification using DenseNet

2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP), 2021
Wireless Communication over long distances has revolutionised the way world works, functions and interacts.Modulation of a signal containing information has made all this possible.The identification of modulation type of a signal forms an integral part in several military and civilian applications.While the classical approaches of modulation ...
Sameera Shaik, S. Kirthiga
openaire   +1 more source

Comparison of Automatic Modulation Classification Techniques

Journal of Communications, 2022
The advancement of digital communication and technology triggered new challenges related to the channel and radio spectrum utilization. From the other hand, real-time communications are keen of time where requests need to be processed in very short time.
Salah Ayad Jassim, Ibrahim Khider
openaire   +1 more source

LSTM-based Automatic Modulation Classification

2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2020
Recently, automatic modulation classification (AMC) has been studied by more and more researchers, and a host of methods based on deep learning have been proposed. Different from image data, signal data is a sequence that changes with time, and has temporal characteristics. Like the spatial feature, the temporal feature cannot be ignored. In this paper,
Quan Zhou 0008, Xiaojun Jing, Yuan He 0009, Yuanhao Cui, Michel Kadoch, Mohamed Cheriet   +5 more
openaire   +1 more source

Real-Time Automatic Modulation Classification

2019 International Conference on Field-Programmable Technology (ICFPT), 2019
Deep learning based techniques have shown promising results over traditional hand-crafted methods for automatic modulation classification for radio signals. However, implementation of these deep learning models on specialized hardware can be challenging, as both latency and throughput performance are critical to achieving real-time response to over-the-
Stephen Tridgell, David Boland, Philip H. W. Leong, Siddhartha 0003   +3 more
openaire   +1 more source

Automatic Modulation Classification in the Presence of Interference

2019 European Conference on Networks and Communications (EuCNC), 2019
A modulation recognition method based on a con-volutional neural network (CNN) architecture is assessed through classification of synthetic baseband signals in the presence of a second interfering signal source. The complexity and adaptability of CNNs is leveraged so as to forgo statistical feature extraction procedures and efficiently classify based ...
P. Triantaris, E. Tsimbalo, W. H. Chin, D. Gunduz   +3 more
openaire   +2 more sources

Automatic Modulation Classification Based on the Improved AlexNet

2021 International Wireless Communications and Mobile Computing (IWCMC), 2021
In the military and civilian domains, the modulation classification in the communication system is an extremely important technology that needs to be constantly updated and improved. In this paper, we present an automatic modulation classification (AMC) model to do modulation classification in 5 typical types of signal modulation BPSK, QPSK, 8PSK ...
Zheao Li, Zhongjin Jiang, Jie Huang 0004
openaire   +1 more source

Complex-Valued Networks for Automatic Modulation Classification

IEEE Transactions on Vehicular Technology, 2020
Deep learning (DL) has been recognized as an effective solution for automatic modulation classification (AMC). However, most recent DL based AMC works are based on real-valued operations and representations. In this correspondence, we aim to demonstrate the high potential of complex-valued networks for AMC. We present the design of several key building
Ya Tu, Yun Lin 0005, Changbo Hou, Shiwen Mao   +3 more
openaire   +1 more source