Harald Bayerlein

472 total citations
9 papers, 321 citations indexed

About

Harald Bayerlein is a scholar working on Aerospace Engineering, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Harald Bayerlein has authored 9 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Aerospace Engineering, 5 papers in Computer Networks and Communications and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Harald Bayerlein's work include UAV Applications and Optimization (6 papers), Distributed Control Multi-Agent Systems (5 papers) and Robotic Path Planning Algorithms (2 papers). Harald Bayerlein is often cited by papers focused on UAV Applications and Optimization (6 papers), Distributed Control Multi-Agent Systems (5 papers) and Robotic Path Planning Algorithms (2 papers). Harald Bayerlein collaborates with scholars based in France and Germany. Harald Bayerlein's co-authors include David Gesbert, Mirco Theile, Marco Caccamo, Paul de Kerret, Omid Esrafilian, Jürgen Herre, Bernd Edler, Fabian-Robert Stöter, Alexander J. Adami and Jichao Chen and has published in prestigious journals such as IEEE Open Journal of the Communications Society, arXiv (Cornell University) and 2021 IEEE Global Communications Conference (GLOBECOM).

In The Last Decade

Harald Bayerlein

9 papers receiving 312 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Harald Bayerlein France	7	229	144	129	94	40	9	321
Muhammad Morshed Alam South Korea	9	286 1.2×	149 1.0×	193 1.5×	70 0.7×	36 0.9×	19	413
Savvas Papaioannou Cyprus	12	243 1.1×	186 1.3×	150 1.2×	60 0.6×	61 1.5×	30	361
Linlin Sun China	12	236 1.0×	97 0.7×	196 1.5×	303 3.2×	59 1.5×	27	500
Christian Grasso Italy	12	224 1.0×	75 0.5×	262 2.0×	185 2.0×	34 0.8×	35	393
Weijia Wang China	9	227 1.0×	122 0.8×	151 1.2×	79 0.8×	66 1.6×	30	355
Mirmojtaba Gharibi Canada	2	251 1.1×	82 0.6×	199 1.5×	132 1.4×	46 1.1×	2	354
Sabur Baidya United States	10	88 0.4×	121 0.8×	248 1.9×	131 1.4×	67 1.7×	32	404
Daniel Fernando Pigatto Brazil	9	121 0.5×	70 0.5×	117 0.9×	72 0.8×	51 1.3×	30	254
Zhiyu Mou China	5	296 1.3×	105 0.7×	214 1.7×	172 1.8×	59 1.5×	11	414

Countries citing papers authored by Harald Bayerlein

Since Specialization

Citations

This map shows the geographic impact of Harald Bayerlein's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Harald Bayerlein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harald Bayerlein more than expected).

Fields of papers citing papers by Harald Bayerlein

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Harald Bayerlein. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Harald Bayerlein. The network helps show where Harald Bayerlein may publish in the future.

Co-authorship network of co-authors of Harald Bayerlein

This figure shows the co-authorship network connecting the top 25 collaborators of Harald Bayerlein. A scholar is included among the top collaborators of Harald Bayerlein based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Harald Bayerlein. Harald Bayerlein is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

Chen, Jichao, Omid Esrafilian, Harald Bayerlein, David Gesbert, & Marco Caccamo. (2023). Model-Aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks. 818–823. 2 indexed citations

Bayerlein, Harald, et al.. (2022). Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance. 2022 IEEE Intelligent Vehicles Symposium (IV). 331–336. 13 indexed citations

Theile, Mirco, et al.. (2021). UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning. arXiv (Cornell University). 539–546. 49 indexed citations

Bayerlein, Harald, Mirco Theile, Marco Caccamo, & David Gesbert. (2021). Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning. IEEE Open Journal of the Communications Society. 2. 1171–1187. 132 indexed citations

Esrafilian, Omid, Harald Bayerlein, & David Gesbert. (2021). Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks. 2021 IEEE Global Communications Conference (GLOBECOM). 1–6. 8 indexed citations

Bayerlein, Harald, et al.. (2018). Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots. 2018 52nd Asilomar Conference on Signals, Systems, and Computers. 724–728. 15 indexed citations

Bayerlein, Harald, Paul de Kerret, & David Gesbert. (2018). Trajectory Optimization for Autonomous Flying Base Station via Reinforcement Learning. 1–5. 85 indexed citations

Adami, Alexander J., et al.. (2014). Comparison of a 2D- and 3D-Based Graphical User Interface for Localization Listening Tests. DepositOnce. 3 indexed citations

Stöter, Fabian-Robert, et al.. (2013). An Experiment About Estimating The Number Of Instruments In Polyphonic Music: A Comparison Between Internet And Laboratory Results.. Zenodo (CERN European Organization for Nuclear Research). 389–394. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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