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A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar, A and Majhi, S and Gui, G and Wu, H-C and Yuen, C (2022) A Survey of Blind Modulation Classification Techniques for OFDM Signals. In: Sensors, 22 (3).

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Official URL: https://doi.org/10.3390/s22031020


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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Item Type: Journal Article
Publication: Sensors
Publisher: MDPI
Additional Information: The copyright for this article belongs to Authors
Keywords: Backpropagation; Bit error rate; Convolution; Convolutional neural networks; Decision trees; Efficiency; Long short-term memory; Modulation; Orthogonal frequency division multiplexing; Radio transceivers; Software radio; Support vector machines; Surveys, Blind modulation classification; Convolutional neural network; Cyclic cumulants; Deep learning; Higher order cumulant (HOC); Higherorder cumulant and cyclic cumulant; Ma ximum likelihoods; Maximum a posteriori; Maximum-likelihood; Modulation classification; Orthogonal frequency-division multiplexing; Probability of correct classifications; Testbed implementation, Maximum likelihood
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 07 Feb 2022 12:06
Last Modified: 07 Feb 2022 12:06
URI: http://eprints.iisc.ac.in/id/eprint/71232

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