  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-theart 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 theUAVs 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.
