Deep reinforcement learning for UAV navigation through massive MIMO technique

Hongji Huang, Yuchun Yang, Hong Wang, Zhiguo Ding, Hikmet Sari, Fumiyuki Adachi

    Research output: Contribution to journalArticlepeer-review

    37 Citations (Scopus)


    Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-the-art deep reinforcement learning with the UAV navigation through massive multiple-input-multiple-output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with those of the other schemes.

    Original languageEnglish
    Article number8894381
    Pages (from-to)1117-1121
    Number of pages5
    JournalIEEE Transactions on Vehicular Technology
    Issue number1
    Publication statusPublished - 2020 Jan


    • Massive multiple-input-multiple-output (MIMO)
    • UAV navigation
    • deep reinforcement learning

    ASJC Scopus subject areas

    • Automotive Engineering
    • Aerospace Engineering
    • Electrical and Electronic Engineering
    • Applied Mathematics


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