Tytuł:

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

Tytuł publikacji grupowej:

2026, nr 1, JTIT-artykuły

Autor:

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

Temat i słowa kluczowe:

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

Opis:

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.

Numer:

1

Wydawca:

National Institute of Telecommunications

Data wydania:

2026, nr 1

Typ zasobu:

artykuł

DOI:

10.26636/jtit.2026.1.2299

eISSN:

1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Licencja:

CC BY 4.0