Publications

Link to my PhD thesis "Machine Learning Methods for UAV-aided Wireless Networks"

Journals

• Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
  M. Theile, H. Bayerlein, M. Caccamo, and A. Sangiovanni-Vincentelli
  arXiv preprint
  [code] [arXiv:2309.03157] [PDF]

  
  

• Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
  H. Bayerlein, M. Theile, M. Caccamo, and D. Gesbert
  IEEE Open Journal of the Communications Society, vol. 2, pp. 1171-1187, 2021, doi: 10.1109/OJCOMS.2021.3081996.
  [code] [IEEE Xplore] [arXiv:2010.12461] [PDF]

  
  

Conferences

• Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
  J. Chen, O. Esrafilian, H. Bayerlein, D. Gesbert, and M. Caccamo
  IEEE Global Communications Conference (GLOBECOM) WS, Taipei, Taiwan, 4-8 December 2023.
  [code] [IEEE Xplore] [arXiv:2109.15266] [PDF]

  
  

• Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
  R. Trumpp, H. Bayerlein, and D. Gesbert
  IEEE Intelligent Vehicles Symposium 2022, Aachen, Germany, 5-9 June 2022.
  [IEEE Xplore] [arXiv:2109.15266] [PDF]

  
  

• Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks
  O. Esrafilian, H. Bayerlein, and D. Gesbert
  IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 7-11 December 2021.
  [IEEE Xplore] [arXiv:2104.10403] [PDF]

  
  

• UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
  M. Theile, H. Bayerlein, R. Nai, D. Gesbert, and M. Caccamo
  20th International Conference on Advanced Robotics (ICAR), Ljubljana, Slovenia, 6-10 December 2021.
  [code] [IEEE Xplore] [arXiv:2010.06917] [PDF]

  
  

• UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
  H. Bayerlein, M. Theile, M. Caccamo, and D. Gesbert
  IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, 7-11 December 2020.
  [code] [IEEE Xplore] [arXiv:2007.00544] [PDF]

  
  

• UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
  M. Theile, H. Bayerlein, R. Nai, D. Gesbert, and M. Caccamo
  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 25-29 October 2020.
  [IEEE Xplore] [arXiv:2003.02609] [PDF]

  
  

• Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots
  H. Bayerlein, R. Gangula, and D. Gesbert
  52nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 28-31 October 2018.
  [IEEE Xplore] [PDF]

  
  

• Trajectory Optimization for Autonomous Flying Base Station via Reinforcement Learning
  H. Bayerlein, P. de Kerret, and D. Gesbert
  19th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece, 25-28 June 2018.
  [IEEE Xplore] [PDF]

  
  

• Comparison of a 2D- and 3D-based Graphical User Interface for Localization Listening Tests
  M. Schoeffler, S. Westphal, A. Adami, H. Bayerlein, and J. Herre
  EAA Joint Symposium on Auralization and Ambisonics, Berlin, Germany, 3-5 April 2014.
  [PDF]

  
  

• An Experiment about Estimating the Number of Instruments in Polyphonic Music: A Comparison between Internet and Laboratory Results
  M. Schoeffler, F.-R. Stöter, H. Bayerlein, B. Edler, and J. Herre
  14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 4-8 November 2013.
  [PDF]

  
  

Posters/Reports

• Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
  R. Trumpp, H. Bayerlein, and D. Gesbert
  BMW-EURECOM-TUM 8th Summer School on Frontiers in Machine Intelligence, Saint-Raphaël, France, July 4-9, 2022.
  *Best Poster Award*
  [link] [poster]

  
  

• UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
  H. Bayerlein, M. Theile, M. Caccamo, and D. Gesbert
  Machine Learning Summer School (MLSS) Indonesia, online, 3-9 August 2020.
  [link] [poster]

  
  

• Machine Learning for Analysis and Classification of Radar Satellite Data
  H. Bayerlein, A. Hirose, and D. Gesbert
  JSPS Summer Program Orientation Session, SOKENDAI, Shonan Village Center, Hayama, Japan, 14 June 2019.
  [link] [poster]

  
  

• Optimal Trajectory of Autonomous Flying Base Stations via Reinforcement Learning
  H. Bayerlein, P. de Kerret, and D. Gesbert
  1st TUM-EURECOM Workshop on Communications and Security, Munich, Germany, 14-15 December 2017.
  [link] [poster]

  
  

• ER-Force Team Description Paper for RoboCup 2014
  H. Bayerlein, A. Danzer, M. Eischer, A. Hauck, M. Hoffmann, P. Kallwies, and M. Lieret
  18th Annual RoboCup International Symposium, João Pessoa, Brazil, 19-25 July 2014.
  [PDF]

Invited Talks

• Research Experience in Japan: Machine Learning and Radar Satellite Data
  Japan Society for the Promotion of Science (JSPS) Junior Forum, Luebeck, Germany, 2 November 2019.
  [link]

  
  

• Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots
  Doctoral Seminar on Methods of Signal Processing, Technical University of Munich, Germany, 21 March 2019.
  [link]