# Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

## TL;DR

This paper introduces a deep reinforcement learning approach using a deep Q-network to improve UAV navigation in massive MIMO environments, enhancing real-time decision-making and link selection.

## Contribution

It presents a novel deep Q-network based method for UAV navigation that effectively captures UAV motion and optimizes link selection in real time.

## Key findings

- Enhanced coverage and convergence in simulations
- Superior performance over existing schemes
- Effective decision-making based on received signal strengths

## 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-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.