This study investigates the application of ensemble learning techniques for automatic modulation classification (AMC) in a reflective intelligent surface (RIS)-aided wireless communication system. The transmitter is considered to utilize five possible phase-shift keying and quadrature-amplitude modulation schemes for data transmission. The receiver extracts cumulant-based and spectral-based features from the received data, and employs three ensemble classifiers, namely XGBoost, LightGBM, and Random Forest for AMC. Furthermore, the important features for each classifier are identified, and their performance is computed and compared with the classifiers using all the features. Numerical results show that the LightGBM classifier performs the best in terms of AMC for the considered system and effectively classifies the modulation schemes at low signal-to-noise ratio values.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Automatic Modulation Classification in RIS-Assisted Wireless Communication Systems using Ensemble Learning Techniques


    Contributors:


    Publication date :

    2023-10-10


    Size :

    1094980 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Automatic Modulation Recognition in Wireless Communication Systems Using Feature-Based Approach

    Almohamad, Tarik Adnan / Salleh, M. F. M. / Mahmud, Mohd Nazri et al. | TIBKAT | 2019


    Automatic Modulation Recognition in Wireless Communication Systems Using Feature-Based Approach

    Almohamad, Tarik Adnan / Salleh, M. F. M. / Mahmud, Mohd Nazri et al. | Springer Verlag | 2019