A Time-Efficient and Attention-Aware Deployment Strategy for UAV Networks Driven by Deep Reinforcement Learning

Authors:
Publisher website
Google Scholar
Abstract
Collaborative unmanned aerial vehicle (UAV) networking has the characteristics of flexibility, efficiency, ubiquity, etc., which can enhance wireless network coverage and improve the quality of service to ground users. However, collaborative UAV networking has main challenges such as location deployment and energy optimization. For collaborative networking scenarios, it is necessary to focus on solving key issues such as algorithm convergence and time complexity. In response to the above problems, we propose to leverage hybrid deep reinforcement learning (DRL) with spatial attention mechanism to reduce time complexity according to actual application requirements of location deployment. Firstly, the convolutional neural network is used to improve the ability of discriminating state features. Then, an improved spatial attention mechanism is introduced to apply different weights to state features of different spatial locations, and focuses on the state features that are favorable for UAV deployment in spatial locations. Finally, the offline training state attention model is added to the state input of hybrid deep reinforcement learning for adaptive deployment training. The simulation results show that the training time of the algorithm can be greatly reduced while both mean opinion score (MOS) and energy consumption performance are reduced by about 10%.

This publication has 11 references indexed in Scilit: