Denoising Higher-Order Moments for Blind Digital Modulation Identification in Multiple-Antenna Systems

Kharbech, Sofiane; Simon, Eric Pierre; Belazi, Akram; Wang, Xiang

doi:10.1109/lwc.2020.2969157

Cited by 8 publications

(4 citation statements)

References 19 publications

(28 reference statements)

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“…To show its scalability for real-world applications, the ISSA is applied for feature weighting in the context of blind digital modulation recognition for a multi-antenna system. The signals model and the identification system are the ones employed in [12]. The system model considers a frequency-flat block-fading multiple-input-multiple-output (MIMO) channel with m transmitting antennas and n receiving antennas.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The MD classifier identifies the modulation scheme by calculating the Euclidean distance of a feature vector with all the theoretical ones and then selecting the closest. In order to improve the performance of the identification system, authors in [12] introduced a feature-denoising approach. In our paper, for the same purpose, we will embed a feature weighting approach, i.e., we added weights to the initially extracted HOS so that the misclassification rate is minimized.…”

Section: Problem Formulationmentioning

confidence: 99%

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Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

et al. 2021

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In modulation identification issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature characteristics. Feature weighting boosts the performance of machine learning algorithms, particularly the class of instance-based learning algorithms such as the Minimum Distance (MD) classifier, in which the distance measure is highly sensitive to the magnitude of features. In this paper, we propose an improved version of the Salp Swarm optimization Algorithm (SSA), called ISSA, that will be applied to optimize feature weights for an MD classifier. The aim is to improve the performance of a blind digital modulation detection approach in the context of multiple-antenna systems. The improvements introduced to SSA mainly rely on the opposition-based learning technique. Computer simulations show that the ISSA outperforms the SSA as well as the algorithms that derive from it. The ISSA also exhibits the best performance once it is applied for feature weighting in the above context.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

et al. 2021

Self Cite

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“…Under more extreme circumstances, only a few samples can be obtained and these methods will be not effective anymore. However, recent works have demonstrated that feature-based algorithms are still very efficient and improve classification performance with few training data [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Recognition in Two- Way Relaying Channel with the Presence of Relay Transceiver Hardware Impairments

Tayakout

Bouchibane

Boutellaa

et al. 2023

Preprint

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Modulation recognition (MR) is well-known as one of the enabling tools, which aims to further afford efficient and secure communications in cognitive radio (CR) context for 5G and beyond networks. In this Paper, we propose a robust MR approach designed for two-way MIMO cooperative relaying network by specifically taking into account two crucial constraints: hardware impairments at relay transceiver and co-channel interference. To mitigate the effect of these impairments, feature-based artificial neural network (ANN) recognizer combined with a nonlinear design of the relay processing matrix with dirty paper coding (DPC) is investigated in this work. Simulations using two receive criterions, namely DPC-ZF and DPC-MMSE, have been carried out to validate the effectiveness of the proposed approach. It has been proved that both DPC-ZF and DPC-MMSE provide high modulation recognition, whatever the impairments level at the relay transceiver.

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“…Two general categories of algorithms can be used to solve AMC problems: likelihood-based (LB) [6][7][8][9][10][11][12] and feature-based (FB) [13][14][15][16][17][18][19][20][21][22]. LB algorithms are derived from three the likelihood ratio tests: average likelihood, generalized likelihood, and hybrid ratio test likelihood.…”

Section: Introductionmentioning

confidence: 99%

A Novel Self-Learning Filters for Automatic Modulation Classification Based on Deep Residual Shrinking Networks

2023

KSII TIIS

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Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and openset signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

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Denoising Higher-Order Moments for Blind Digital Modulation Identification in Multiple-Antenna Systems

Cited by 8 publications

References 19 publications

Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

Automatic Modulation Recognition in Two- Way Relaying Channel with the Presence of Relay Transceiver Hardware Impairments

A Novel Self-Learning Filters for Automatic Modulation Classification Based on Deep Residual Shrinking Networks

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