Communication in High-Frequency (HF) band allows for good-quality, low-cost, and long-distance data-link transmission over diverse landscapes in aerial communication systems. However, as limited frequency resources are allocated, HF band suffers from poor spectrum efficiency when the channel is congested with many users. To maintain the robustness of the data-link transmission, Automatic Link Establishment (ALE) is the worldwide standard for sustaining HF communication of voice, data, instant messaging, internet messaging, and image communications. Technologies, such as spectrum sensing, Dynamic Spectrum Access (DSA) are utilised in ALE with the primary step of automatic mode recognition based on cognitive radio. Conventional methods, such as Automatic Modulation Recognition (AMR) targets at the classification of single modulation, while modern communication systems require recognising multiple modes in combination of various number of tones, tone spacing, and tone interval. In this study, an approach that features filling the gap using deep learning is proposed. By characterising the common in-use mode formats in HF range, investigation shows that spectrogram diagram varies significantly, which necessitates the accurate characterisation and classification of multiple communication modes. Specifically, Convolutional Neural Network (CNN or ConvNet) is adopted for classification. The dataset is collected through USRP N210 with GNU Radio simulation. By reconstructing the communication in selected modes, the mode formats are classified. The performance result of recognition accuracy is displayed with confusion matrix. The confident classification of spectral characteristics, as well as accurate estimation, are established for practical communication scenarios.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    High-Frequency Band Automatic Mode Recognition Using Deep Learning


    Beteiligte:
    Xu, Zhengjia (Autor:in) / Savvaris, Al (Autor:in) / Tsourdos, Antonios (Autor:in) / Alawadi, Tareq (Autor:in)


    Erscheinungsdatum :

    2018-09-01


    Format / Umfang :

    2737105 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Automatic Two Wheeler License Plate Recognition Using Deep Learning Techniques

    Dias, Anacia / Almeida, Annalie Maria D. / Fernandes, Domnic Seadon et al. | IEEE | 2023


    Sketch face Recognition using Deep Learning

    Rubeena / Kavitha, E. | IEEE | 2021


    Automatic Road Pavement Distress Recognition Using Deep Learning Networks from Unmanned Aerial Imagery

    Farhad Samadzadegan / Farzaneh Dadrass Javan / Farnaz Ashtari Mahini et al. | DOAJ | 2024

    Freier Zugriff

    Automatic navigation using deep reinforcement learning

    BALAKRISHNAN KAUSHIK / NARAYANAN PRAVEEN / LAKEHAL-AYAT MOHSEN | Europäisches Patentamt | 2023

    Freier Zugriff

    Bicycle Automatic Transmission Using Deep Learning

    OH SEUNG SIK / CHOI HO YEON / JOE KYOUNG JAE et al. | Europäisches Patentamt | 2020

    Freier Zugriff