Identification of superposed modulations for two‐way relaying MIMO systems with physical‐layer network coding

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

doi:10.1049/iet-com.2016.0335

Cited by 7 publications

(6 citation statements)

References 30 publications

(27 reference statements)

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“…To the best of our knowledge, all the previously works for MR did not take into account the presence of the relay transceiver hardware impairments and co-channel interference which does not reflect the practical case . Indeed, the study in [20] is the only one that focused on the superposed modulations identification for two-way relay system using a simple ZF precoding technique without considering these constraints.…”

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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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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“…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%

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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“…Furthermore, several approaches have been proposed for the detection of the coding [ 16 , 17 ] and other algorithms for estimating the channel matrix are also available [ 18 , 19 , 20 ]. In the last few decades, there has been a great interest on modulation detection for MIMO communications systems [ 21 , 22 , 23 , 24 , 25 , 26 ]. In fact, the misdetection of the modulation type causes a considerable performance degradation in the estimation of source information as the modulation estimation is done prior to the demodulation phase.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 23 ], Lau et al proposed an algorithm dedicated for the short-range devices that use the European 868 MHz band under the same conditions, i.e., perfect CSI and uncorrelated channels. Under the same conditions, the detection of superposed modulations for two-way relaying MIMO systems with the physical-layer network coding is studied in [ 22 ]. While there have been considerable works that assumed the conditions of the perfect CSI and uncorrelated channels, little attention has been paid to the cooperative MIMO systems with imperfect CSI and under correlated channels.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for 5G MIMO Modulation Detection

Chikha

Almadhor

Khalid

2021

Sensors

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Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.

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Identification of superposed modulations for two‐way relaying MIMO systems with physical‐layer network coding

Cited by 7 publications

References 30 publications

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

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

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

Machine Learning for 5G MIMO Modulation Detection

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