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Chapter

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Title

Multi-Agent Q-Learning for Drone Base Stations

Authors

[ 1 ] Instytut Radiokomunikacji, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2023

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • wireless communication
  • energy consumption
  • base stations
  • Q-learning
  • mobility models
  • satellite broadcasting
  • interference
Abstract

EN We formulate a drone base stations (DBSs) localization problem that improves the users’ received signal-to-interference-plus-noise ratio (SINR) and the fairness between users in terms of receiving sufficient channel quality. In contrast to other works, our algorithm adapts to the actual users distribution on the ground without knowing their locations but rather their channel measurements history. We also leverage the fact that moving a DBS results in reduced aerodynamic energy consumption and illustrate that moving DBSs intelligently at a certain speed actually reduces energy consumption. We propose a multi-agent Q-learning formulation to solve this problem which requires less computation than its single agent counterpart and show by extensive simulations the improvements in terms of system fairness and link reliability relative to the benchmark solutions while leveraging a realistic users mobility model.

Pages (from - to)

261 - 266

DOI

10.1109/WiMob58348.2023.10187736

URL

https://ieeexplore.ieee.org/document/10187736

Book

2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

Presented on

19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023, 21-23.06.2023, Montreal, Canada

Ministry points / chapter

20

Ministry points / conference (CORE)

70

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