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

78 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


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