Advanced search ?

Object

Comparative Analysis of Classifiers for Higher-order Statistics-based Modulation Recognition in Cooperative STBC-OFDM, Journal of Telecommunications and Information Technology, 2026, nr 1
Download

Title: Comparative Analysis of Classifiers for Higher-order Statistics-based Modulation Recognition in Cooperative STBC-OFDM, Journal of Telecommunications and Information Technology, 2026, nr 1

Creator:

Dehri, Brahim ; Moulay, Hakima ; Daikh, Ahmed Amine ; Besseghier, Mokhtar

Date:

2026, nr 1

Resource Type:

artykuł

Group publication title:

2026, nr 1, JTIT-artykuły

Description:

kwartalnik

Abstrakt:

Precise classification of modulation in cooperative relaying networks remains challenging in the presence of carrier frequency offset (CFO) and imperfect channel state information (CSI). This paper conducts a comprehensive comparative analysis of automatic modulation classification (AMC) methods for distributed space-time block-coded orthogonal frequency division multiplexing (DSTBC-OFDM) systems under these impairments. A unified simulation framework is developed that combines pilot-assisted CFO and CSI estimation with higher-order statistics (HOS)-based feature extraction. Four widely used machine learning classifiers, i.e. feedforward neural network, support vector machine, random forest classifier, and adaptive boosting, are benchmarked under identical channel and noise conditions. Monte Carlo simulations are performed across varying SNR levels and fading scenarios, enabling a fair assessment of classification accuracy, robustness to residual estimation errors, and relative computational complexity. The results provide practical insights into the strengths and limitations of each classifier in cooperative STBC-OFDM environments, offering valuable guidelines for selecting AMC techniques in future cooperative wireless systems.

Number:

1

Publisher:

National Institute of Telecommunications

Resource Identifier:

oai:bc.itl.waw.pl:2425

DOI:

10.26636/jtit.2026.1.2299

eISSN:

1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

License:

CC BY 4.0

Subject and keywords:

automatic modulation classification CFO estimation CSI DSTBC-OFDM higher-order statistics machine learning

Object collections:

Last modified:

Apr 1, 2026

In our library since:

Apr 1, 2026

Number of object content hits:

105

All available object's versions:

https://bc.itl.waw.pl/publication/2746

Show description in RDF format:

RDF

Show description in OAI-PMH format:

OAI-PMH

Edition name Date
Dehri, Brahim; Moulay, Hakima; Daikh, Ahmed Amine; Besseghier, Mokhtar, Comparative Analysis of Classifiers for Higher-order Statistics-based Modulation Recognition in Cooperative STBC-OFDM, Journal of Telecommunications and Information Technology, 2026, nr 1 Apr 1, 2026

Objects Similar

2026, nr 3, JTIT-artykuły
2026, nr 3, JTIT-artykuły
Machine Learning-based Automatic Modulation Classification for 5G-Advanced and 6G Waveforms: Robust Identification Under Realistic Channel Impairments, Journal of Telecommunications and Information Technology, 2026, nr 3

Horch, Abdelkader Besseghier, Mokhtar Ghouali, Samir Louni, Abderrahmane

2026, nr 3
artykuł
QoS-based Joint User Selection and Scheduling for MU-MIMO WLANs, Journal of Telecommunications and Information Technology, 2017, nr 4

Hency, Victor Dhason Berlin Rao, Dasari Srinivasa

2017, nr 4
artykuł
2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Generalized Learning Approach to Deep Neural Networks, Journal of Telecommunications and Information Technology, 2024, nr 3

Ponti, Francesca Frezza, Fabrizio Simeoni, Patrizio Parisi, Raffaele

2024, nr 3
artykuł
2026, nr 2, JTIT-artykuły
2026, nr 2, JTIT-artykuły
Evaluating AES Payload Encryption for Securing MQTT-based Smart Home Networks with Machine Learning-based Intrusion Detection, Journal of Telecommunications and Information Technology, 2026, nr 2

Gajewski, Mariusz Sałabun, Wojciech

2026, nr 2
artykuł
2025, nr 3, JTIT-artykuły
2025, nr 3, JTIT-artykuły
A Comprehensive Study on Path Loss Estimation Using Deep Hybrid Learning in 5G Networks, Journal of Telecommunications and Information Technology, 2025, nr 3

Yeaser, Kazi Md Abrar Hassan, Kazi Md Abir

2025, nr 3
artykuł
2026, nr 3, JTIT-artykuły
2026, nr 3, JTIT-artykuły
Explainable Network Traffic Classification Using XGBoost and SHAP: Interpreting Feature Contributions Across Traffic Classes, Journal of Telecommunications and Information Technology, 2026, nr 3

Deghem, Boubakar Seddik Aid, Lahcene Mostefaoui, Kadda

2026, nr 3
artykuł
2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems, Journal of Telecommunications and Information Technology, 2024, nr 3

Qasaymeh, Mahmoud M. Alqatawneh, Ali Khodeir, Mahmoud A. Aljaafreh, Ahmad F.

2024, nr 3
artykuł
Contextual probability, Journal of Telecommunications and Information Technology, 2003, nr 3

Wang, Hui

2003, nr 3
artykuł
More
×

Citation

Citation style:

Contact details

Address

Instytut Łączności – Państwowy Instytut Badawczy ul. Szachowa 1 04-894 Warszawa

Phone

(+48) 5128 696

E-Mail

biblioteka@il-pib.pl

Visit us!

This page uses 'cookies'. More information