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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Wei (editor) / Mu, Jiasong (editor) / Liu, Xin (editor) / Na, Zhenyu Na (editor) / Ren, Jie (author) / Han, Liang (author)

    Conference:

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



    Publication date :

    2024-03-23


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English