Distributed Automatic Modulation Classification with Compressed Data

Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency-selective multipath fading, and time-varying channels in real-world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood-based (LB) and feature-based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal-to-noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC. K E Y W O R D S automatic modulation classification (AMC), deep learning (DL), feature-based (FB) methods, likelihood-based (LB) methods | INTRODUCTIONAutomatic modulation classification (AMC) is important in wireless communication systems used in military and civilian applications to enhance the efficiency of the spectrum utilization, redue the overhead, and resolve the shortage problems. Unfortunately, the restricted spectrum resources barely satisfy the ever-increasing demand for 5G 1,2 and Internet of Things (IoT) networks. 3 The AMC can be used for better management of the available spectrum. A simple block diagram of a communication system based on AMC is presented in Figure 1. 4 The AMC architecture contains two steps: signal preprocessing and a proper algorithm for classification. The preprocessing tasks involve reduction of noise, carrier frequency estimation, symbol period estimation, equalization, and signal power evaluation. On the other hand, the AMC methods comprise traditional methods including decisiontheoretic methods and feature-based methods 4,5 along with advanced methods 6 as shown in Figure 2.

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“…Wong et al 122 Every node within the environment has a different realization of the same signal in a distributed setting.…”

Section: Performing Amc In a Distributed Environmentsmentioning

confidence: 99%

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Abdel‐Moneim

El‐Shafai

Abdel-Salam

et al. 2021

Int J Communication

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“…RFML based approaches have aimed to replace the human intelligence and domain expertise required to identify and characterize these features using deep neural networks and advanced architectures, such as CNNs and Recurrent Neural Networks (RNNs), to both blindly and automatically identify separating features and classify signals of interest, with minimal pre-processing and less a priori knowledge [48], [52], [56], [57], [82]. Given the significant research in RFML-based modulation classification, it can be argued that AMC is one of the most mature fields in RFML, and has been deployed in real-world products [122].…”

Section: A Spectrum Sensingmentioning

confidence: 99%

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Wong,

Clark,

Flowers

et al. 2020

Preprint

Self Cite

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While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.

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Signal Processing-Based Deep Learning for Blind Symbol Decoding and Modulation Classification

Hanna

Dick

Čabrić

2022

IEEE J. Select. Areas Commun.

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Distributed Automatic Modulation Classification with Compressed Data

Cited by 4 publications

References 17 publications

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Signal Processing-Based Deep Learning for Blind Symbol Decoding and Modulation Classification

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