Dynamic UAV Deployment for Differentiated Services: A Multi-Agent Imitation Learning Based Approach

Xiaojie Wang, Zhaolong Ning, Song Guo, Miaowen Wen, Lei Guo, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed ϵ-Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

Original languageEnglish (US)
Pages (from-to)2131-2146
Number of pages16
JournalIEEE Transactions on Mobile Computing
Volume22
Issue number4
DOIs
StatePublished - Apr 1 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

Keywords

  • Nash equilibrium
  • UAV deployment
  • decentralized training
  • differentiated services
  • imitation learning

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