# Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

arXiv: https://arxiv.org/abs/1901.10832 · HTML: https://mlsanity.com/paper/1901.10832/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 10 of 10)

- [Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization](https://mlsanity.com/paper/2105.11013/) (2021)
- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [DeTrack: A Benchmark and Altitude-Aware Dual World Model for Drone-embodied Tracking](https://mlsanity.com/paper/2605.17451/) (2026)
- [A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directions](https://mlsanity.com/paper/2608.07571/) (2026)
- [5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul](https://mlsanity.com/paper/2202.02006/) (2022)
- [Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning](https://mlsanity.com/paper/2303.09013/) (2023)
- [Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2305.10760/) (2023)
- [Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors](https://mlsanity.com/paper/2609.14426/) (2026)

## Abstract (arXiv)

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.