A novel deep learning and polar transformation framework for an adaptive automatic modulation classification

P Ghasemzadeh, S Banerjee… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Automatic Modulation Classification (AMC) is an approach to identify an observed signal's
most likely modulation scheme without any a priori knowledge of the intercepted signal. In …

GGCNN: An efficiency-maximizing gated graph convolutional neural network architecture for automatic modulation identification

P Ghasemzadeh, M Hempel, H Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Automatic modulation identification (AMI) is a technique to detect the modulation type and
order of a received signal, which has the potential to enhance cognitive radio capabilities for …

A new framework for automatic modulation classification using deep belief networks

P Ghasemzadeh, S Banerjee… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
Automatic Modulation Classification (AMC) is the process of determining the modulation
scheme of an intercepted signal with no a priori information about its characteristics. AMC's …

A spatial-diversity MIMO dataset for RF signal processing research

P Ghasemzadeh, M Hempel… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The procedure of classifying a detected signal's modulation scheme with no a priori
information is known as automatic modulation classification (AMC). AMC has presented …

Evaluation of machine learning-driven automatic modulation classifiers under various signal models

P Ghasemzadeh, S Banerjee… - ASME/IEEE Joint …, 2020 - asmedigitalcollection.asme.org
Abstract Automatic Modulation Classification (AMC) is becoming an essential component in
receiver designs for next-generation communication systems, such as Cognitive Radios …

A novel graph neural network-based framework for automatic modulation classification in mobile environments

P Ghasemzadeh - 2023 - search.proquest.com
Automatic modulation classification (AMC) refers to a signal processing procedure through
which the modulation type and order of an observed signal are identified without any prior …

An OFDM-Based Transceiver Analysis for Railroad Applications

P Ghasemzadeh, M Hempel, H Sharif… - … and Mobile Computing …, 2022 - ieeexplore.ieee.org
The proliferation of wireless technologies in recent years has significantly impacted the
North American freight railroad industry, and enabled them to drastically expand their …

Detecting dark cars using a novel multi-antenna aei tag reader design for increased read distance and reliability

P Ghasemzadeh, S Banerjee… - ASME/IEEE Joint …, 2020 - asmedigitalcollection.asme.org
Abstract Automatic Equipment Identification (AEI) tags are installed on all rail cars in North
America to tag rolling stock and facilitate fault detection using wayside detectors. And yet …

Maximizing RF Communications Throughput for Railroad Applications at 160 MHz

P Ghasemzadeh, M Hempel… - ASME/IEEE Joint …, 2022 - asmedigitalcollection.asme.org
As the railroad industry in North America pursues the further expansion of their wireless
communications capabilities in support of many different railroad applications, such as …

Detecting dark cars in railroad operations using multi-antenna beamforming for long-distance discovery and identification of aei tags

P Ghasemzadeh, S Banerjee, M Hempel… - 2020 International …, 2020 - ieeexplore.ieee.org
One recurring problem that the North American railroad industry faces is the tracking of lost
railroad cars. Railroad cars are often parked on track sidings of train stations or train depot …