Dual-functional radar-communication (DFRC) is a promising direction in the future integrated sensing and communication system. The joint radar and communication (JRC) beamforming scheme is recently developed in DFRC systems. To address the JRC beamforming challenge, conventional approaches predominantly rely on convex optimization methods, which severely depend on precise channel estimation and entail a high computational complexity. Motivated by this, a deep learning-based optimization approach is investigated for tackling the JRC beamforming problem. To enhance the overall performance, we design a deep alternating neural network architecture. Simulation results verify that our proposed method guarantees the required sensing performance and outperforms numerical algorithms in terms of the average data rate of communication users.


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    Titel :

    Deep Learning-Based Joint Transmit Beamforming for Integrated Sensing and Communication System


    Beteiligte:
    Yang, Ruming (Autor:in) / Zhu, Zhiming (Autor:in) / Zhang, Jiexin (Autor:in) / Xu, Shu (Autor:in) / Li, Chunguo (Autor:in) / Huang, Yongming (Autor:in) / Yang, Luxi (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    878170 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch