Performance of AdaBoost classifier in recognition of superposed modulations for MIMO TWRC with physical-layer network coding

Chikha, Wassim Ben; Chaoui, Slim; Attia, Rabah

doi:10.23919/softcom.2017.8115498

Cited by 4 publications

(6 citation statements)

References 21 publications

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“…The most widely existing modulation classification algorithms in the literature are proposed to estimate a single M-ary modulation [29][30][31][32][33][34]. However, the superposition of two M-ary modulations leads to a significant augmentation of the resulting constellation size in addition to an unusual spatial arrangement [35,36]. In fact, the superposition of two modulations with orders M 1 and M 2 leads to a modulation with an order upper bounded by M 1 × M 2 .…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the superimposed modulated useful information with the noise results in the dispersion of the constellation points from their appropriate positions. In [35,36], the authors have addressed the problem of modulation classification of superimposed modulations in two-way relaying MIMO systems with PNC under Rayleigh channels. In addition, the zero-forcing (ZF) precoding technique is applied at each source node before transmission.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

Chikha¹,

Almadhor²

2022

Computers, Materials &Amp; Continua

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To promote reliable and secure communications in the cognitive radio network, the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation. In this paper, we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output (MIMO) two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution. Our purpose consists of classifying pairs of users modulations from superimposed signals. To achieve this goal, we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier. We use several efficiency metrics including the true positive (TP) rate, false positive (FP) rate, precision, recall, F-Measure and receiver operating characteristic (ROC) area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification. Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio, including the worst case (i.e., m = 0.5), where the fading distribution follows a one-sided Gaussian distribution. We also carry out a comparative study between our proposal using MultiBoostAB classifier with the decision tree (J48) classifier. Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier. In addition, we study the impact of the symbols number, path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

Chikha¹,

Almadhor²

2022

Computers, Materials &Amp; Continua

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“…In [21], a multiple-input multiple-output (MIMO) two-way relaying channel (TWRC) with physical layer network coding (PLNC) needs the recognition of a pair of source-modulations from the superposed constellation at the relay. The suggested algorithm is divided into two steps.…”

Section: Introductionmentioning

confidence: 99%

“…Since the publishing of [21], the AdaBoost technique has been used in this line of work with noncoherent receivers for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulation. It is important to include a brief survey about other noncoherent modulation schemes such as differential chaotic shift keying (DCSK), noise reduction DCSK (NR-DCSK), short reference-DCSK (SR-DCSK), permutation index DCSK (PI-DCSK) and multiuser orthogonal frequency division multiplexing DCSK (OFDM DCSK).…”

Section: Introductionmentioning

confidence: 99%

A Coherent Performance for Noncoherent Wireless Systems Using AdaBoost Technique

et al. 2019

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Boosting is a machine learning approach built upon the idea of producing a highly precise prediction rule by combining many relatively weak and imprecise rules. The Adaptive Boosting (AdaBoost) algorithm was the first practical boosting algorithm. It remains one of the most broadly used and studied, with applications in many fields. In this paper, the AdaBoost algorithm is utilized to improve the bit error rate (BER) of different modulation techniques. By feeding the noisy received signal into the AdaBoost algorithm, it is able to recover the transmitted data from the noisy signal. Consequently, it reconstructs the constellation diagram of the modulation technique. This is done by removing the noise that affects and changes the signal space of the data. As a result, AdaBoost shows an improvement in the BER of coherently detected binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK). The AdaBoost is next used to improve the BER of the noncoherent detection of the used modulation techniques. The improvement appears in the form of better results of the noncoherent simulated BER in comparison to that of the theoretical noncoherent BER. Therefore, the AdaBoost algorithm is able to achieve a coherent performance for the noncoherent system. The AdaBoost is simulated for several techniques in additive white Gaussian noise (AWGN) and Rayleigh fading channels so, as to verify the improving effect of the AdaBoost algorithm.

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“…For ensemble learning, Adaboost has been used for recognition of source modulations for multiple-input multiple-output two-way relaying channel (MIMO TWRC) with physical-layer network coding (PLNC), and it achieved good performance at acceptable SNR values [ 17 ], but it did not have the robust algorithms for recognition of other communication parameters. The Gradient Boosting Decision Tree (GBDT) was used for High Resolution Range Profile (HRRP) target recognition, which showed that GBDT achieved better recognition results and higher calculation efficiency than the Support Vector Machine (SVM) and Naive Bayes classifier [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

Zhang

Jin

et al. 2018

Entropy

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Abstract:In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Rényi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is −10 dB.

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Performance of AdaBoost classifier in recognition of superposed modulations for MIMO TWRC with physical-layer network coding

Cited by 4 publications

References 21 publications

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

A Coherent Performance for Noncoherent Wireless Systems Using AdaBoost Technique

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

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