Automatic modulation recognition (AMR) is one of the most essential techniques in non-cooperative orthogonal time frequency space (OTFS) and orthogonal frequency division multiplexing (OFDM) communication systems. Since coexistence of OTFS and OFDM is a potential and practical solution in the future wireless communication scenarios, classification of the OTFS scheme and the OFDM scheme will be a challenging and meaningful task. In this paper, we propose a deep learning-based method, including multi-layer convolution neural networks (CNNs) and an attention-based residual Squeeze-and-Excitation Module (SE), to extract effective characteristics of OTFS and OFDM signals in multi-path Doppler spread fading channel. To obtain comparable and convincing results, the design of OTFS transmitters is on the basis of OFDM systems and contains six different sub-carrier modulation modes (BPSK, QPSK, 8PSK, 16QAM, 64QAM and 256QAM). Meanwhile, data structures of the signals are all well-deigned for fair comparisons. In addition, datasets include five modulation modes (OTFS, OFDM and other commonly-used modulation modes) and different Doppler spread values to verify our proposed method. The simulations show that our proposed SE-CNN model performs better than other baseline methods. Moreover, extensive experiment results demonstrate the robustness of our proposed method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning-Based Automatic Modulation Recognition in OTFS and OFDM systems


    Beteiligte:
    Zhou, Jinggan (Autor:in) / Liao, Xuewen (Autor:in) / Gao, Zhenzhen (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    1260880 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Spectrally Precoded OTFS Modulation

    Chen, Wei-Chang / Lu, Chang-Hung / Chung, Char-Dir | IEEE | 2022



    OTFS vs. OFDM in High-speed Vehicular Traffic Scenarios

    Compagnoni, Alessandro / Tuninato, Riccardo / Chiasserini, Carla Fabiana et al. | IEEE | 2025


    Doppler Diversity Reception for OTFS Modulation

    Gong, Zhihan / Liu, Shengheng / Huang, Yongming | IEEE | 2022


    On OTFS and OFDM Radar Signal Design Based on the Ambiguity Function Analysis

    Wang, Bowen / Luo, Wenqi / Zhu, Jianchi et al. | IEEE | 2023