Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation

Zheng, Jianping; Lv, Yongfeng

doi:10.1109/tvt.2018.2839735

Cited by 94 publications

(34 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The statistical optimization expression, whether described in (21) or equivalently in (22), is in general difficult because it is nonconvex in the premise of an ML function. The expectation-maximization (EM) algorithm can be applied to…”

Section: Expectation-maximizationmentioning

confidence: 99%

“…A number of recent studies in the context of LB and FB categories can be identified in the literature and a few samples of studies are considered here [20][21][22][23][24][25][26]. An LB algorithm for automatically identifying different quadrature amplitude modulation (QAM) and phase-shift keying (PSK) modulation schemes was given in [20].…”

Section: Introductionmentioning

confidence: 99%

“…Their approach is blind as it does not Automatic Modulation Classification Using Interacting Multiple Model -Kalman Filter for Channel Estimation need prior knowledge of the carrier frequency or the baud rate. An LB algorithm was also used in the AMC system for constellation identification of orthogonal frequency division multiplexing (OFDM) signal with index modulation, as an indicator of active subcarriers, in [21]. Two scenarios were proposed, known CSI and unknown CSI, with the latter conducted blindly by default.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

Salam

Sheriff

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

Section: Expectation-maximizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

Salam

Sheriff

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…They are maximum likelihood-and feature extractionbased modulation identification. The maximum likelihood-based classification method executes the likelihood function on the received signal [8]. Examples of systems that use this approach are (i) faster maximum likelihood function-based MC [9], (ii) expectation conditional maximization algorithm [10], and (iii) sparse coefficient-based expectation maximization algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

View full text Add to dashboard Cite

The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

show abstract

“…Classification algorithms can be split into two groups, Likelihood based (LB) algorithms and Feature based (FB) algorithms [3]. Likelihood based algorithms focus on hypothesis testing based on the probability density function of the received signal which provides sufficient information regarding recognition [4]. Owing to their analytical nature, LB methods provide optimal classification on the basis of signal and channel estimates.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Modulation Classification using Single Inception Module based Convolutional Neural Network

Mumtaz¹,

Khurram²,

Adnan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation

Cited by 94 publications

References 30 publications

Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

Autonomous Modulation Classification using Single Inception Module based Convolutional Neural Network

Contact Info

Product

Resources

About