期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 69, 期 1, 页码 1117-1121出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2952549
关键词
Massive multiple-input-multiple-output (MIMO); deep reinforcement learning; UAV navigation
资金
- National Natural Science Foundation of China [61801246]
- Natural Science Foundation of Jiangsu Province [BK20170910]
- China Postdoc Innovation Talent Supporting Program [BX20180143]
- Open Research Foundation of National Mobile Communications Research Laboratory of Southeast University [2018D09]
- NUPTSF [NY217005, NY217031]
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.
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