With the development of vehicular networks, spectrum resources are becoming increasingly scarce, and improving spectrum efficiency (SE) has become more and more important. Compared with common half-duplex (HD) technology, wireless full-duplex (FD) technology can effectively improve SE. Therefore, in this paper, we introduce FD into vehicle-to-vehicle (V2V) links, investigate the problem of spectrum resource reuse in vehicular networks, and realize the reuse of FD V2V links for the spectrum occupied by vehicle-to-infrastructure (V2I) links. At the same time, we also adopt a multi-agent deep reinforcement learning method to solve the problem that the rapid changes in the vehicular environment. The simulation results show that under 120 dB self-interference cancellation, the transmission rate of FD V2V links is significantly improved compared with that of HD V2V links.


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    Titel :

    Resource Allocation of Full-Duplex Vehicular Networks Based on Multi-Agent Deep Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wei (Herausgeber:in) / Mu, Jiasong (Herausgeber:in) / Liu, Xin (Herausgeber:in) / Na, Zhenyu Na (Herausgeber:in) / Ren, Jie (Autor:in) / Han, Liang (Autor:in)

    Kongress:

    International Conference on Artificial Intelligence in China ; 2023 ; ChangBaiShan, China July 22, 2023 - July 23, 2023



    Erscheinungsdatum :

    23.03.2024


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch