Blind Modulation Classification for Asynchronous OFDM Systems Over Unknown Signal Parameters and Channel Statistics

Gupta, Rahul; Kumar, Sushant; Majhi, Sudhan

doi:10.1109/tvt.2020.2981935

Cited by 65 publications

(33 citation statements)

References 33 publications

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“…Design of a modulation classifier mainly consists of two key steps: signal preprocessing and selection of effective classification algorithm. Preprocessing involves estimation of signal statistics constituting carrier frequency, signal power, and other statistical signal information as per requirements of the classification algorithm [8][9][10]. Modulation classification algorithms can be considered either likelihood based or feature based [7,10].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

Roy

Yadav

Pal

et al. 2021

Computational Intelligence and Neuroscience

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With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweight, ensemble model with convolution, long short term memory (LSTM), and gated recurrent unit (GRU) layers. The proposed model is termed as deep recurrent convoluted network with additional gated layer (DRCaG). It has been tested on a dataset derived from the RadioML2016(b) and comprises of 8 different modulation types named as BPSK, QPSK, 8-PSK, 16-QAM, 4-PAM, CPFSK, GFSK, and WBFM. The performance of the proposed model has been presented through extensive simulation in terms of training loss, accuracy, and confusion matrix with variable signal to noise ratio (SNR) ranging from −20 dB to +20 dB and it demonstrates the superiority of DRCaG vis-a-vis existing ones.

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Section: Introductionmentioning

confidence: 99%

[Retracted] An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

Roy

Yadav

Pal

et al. 2021

Computational Intelligence and Neuroscience

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“…By using (12), the non-zero cycle frequency for π/4-QPSK, MSK, and OQPSK is obtained at 4f c ± f s /2, 4f c ± 2f s , and 4f c , respectively [14]. For QPSK, 16-QAM, and 16-PAM modulation formats, we get the same feature value i.e., non-zero cycle frequency at 4f c and 4f c ± f s .…”

Section: A Integrated Receiver Architecture With Modulation Classification and Energy Harvestingmentioning

confidence: 83%

Simultaneous Wireless Power Transfer and Modulation Classification

Gupta

Krikidis

2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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This work proposes a new simultaneous wireless power transfer and modulation classification (SWPTMC) scheme appropriate for internet of things (IoT) applications. The problem of SWPTMC is investigated for various modulation formats, i.e., quadrature phase-shift-keying (QPSK), π/4-QPSK, offset QPSK (OQPSK), 16-pulse amplitude modulation (16-PAM), 16quadrature amplitude modulation (16-QAM), and minimum shift keying (MSK). We propose three receiver architectures, i.e., an integrated receiver, a separate receiver with power splitting (PS), and a separate receiver with energy harvesting (EH)-based classification; all the architectures are studied under a nonlinear model with a certain sensitivity and saturation level. Also, we derive the average harvested power over a Rayleigh fading channel for the different modulation formats. Two different approaches are used for the blind modulation classification (MC) algorithm: one for the intermediate frequency signal and the other for the baseband signal. Both the MC algorithms are based on the higher-order cumulants and cyclic cumulants of the received signal. The cyclic cumulants use the non-zero cycle frequency position, while the cumulants use threshold values for classifying modulation formats. Monte Carlo simulations are used to evaluate the performance of the proposed SWPTMC schemes. The results show that we can simultaneously harvest power without much affecting the classifier performance. Moreover, with an integrated receiver, we can simultaneously perform MC and harvest power without the requirement of PS circuit.

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“…Various MC algorithms for the OFDM systems were carried out in [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ]. The algorithms for multiple-input multiple-output and OFDM (MIMO-OFDM) systems based on deep neural network (DNN) and Gibbs sampling are investigated in [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

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Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

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Blind Modulation Classification for Asynchronous OFDM Systems Over Unknown Signal Parameters and Channel Statistics

Cited by 65 publications

References 33 publications

[Retracted] An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

[Retracted] An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

Simultaneous Wireless Power Transfer and Modulation Classification

A Survey of Blind Modulation Classification Techniques for OFDM Signals

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