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Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique
- Source :
- IEEE Transactions on Vehicular Technology. 69:1117-1121
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- 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.
- Subjects :
- Computer Networks and Communications
business.industry
Computer science
MIMO
Real-time computing
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Aerospace Engineering
ComputerApplications_COMPUTERSINOTHERSYSTEMS
020302 automobile design & engineering
02 engineering and technology
ComputingMethodologies_ARTIFICIALINTELLIGENCE
0203 mechanical engineering
Automotive Engineering
Reinforcement learning
Wireless
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 19399359 and 00189545
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Vehicular Technology
- Accession number :
- edsair.doi...........d77ae0813a97d15fb17190270b5a8981