Caspar v. Lengerke studied Electrical Engineering at RWTH Aachen University and received his Bachelor’s and Master’s degree in 2017 and 2019 respectively. For his Master’s thesis he worked on Machine Learning in the Physical Layer and stayed for an internship with Nokia Bell Labs in Stuttgart. Thereafter, he studied Economics at Heidelberg University before joining the Deutsche Telekom Chair of Communication Networks at TU Dresden in July 2021.
His research focuses on goal-oriented communication. Since current communication methods are getting closer and closer to the theoretical limit postulated by Claude Shannon 70 years ago, new avenues of research are necessary to find ways to still the demand for even more connectivity in the future. While Shannon considered the goal of any communication to be irrelevant to the underlying engineering task, information theorists have found the opposite to be true. Any communication goal can be achieved using Shannon’s understanding of communication, but certain goals can be achieved by transmitting significantly less data than Shannon’s theory demands. Goal-oriented communication allows for drastic improvements over traditional communication methods by exploiting knowledge of the goal of the communication, and utilisation of previously useless resources like common randomness and noiseless feedback to increase the channel capacity.
Prominent examples of communication goals with significant proven gains over Shannon’s view include identification via channels and common randomness generation. In identification, the goal of the communication is for the receiver to verify whether the transmitter sent a specific message or not. This is, for example, of interest in massive machine-type communications and digital twin applications. Common randomness generation allows for two parties to agree on a certain amount of random data by transmitting less data than the amount of random data finally agreed upon. Common randomness is of special interest for security applications and can significantly increase identification capacity.
Distributed Platoon Control via Semantic-Aware Identification Codes Proceedings Article
In: IEEE International Conference on Communications (ICC), Montreal, Canada, 2025.
Evaluating Transport Layer Congestion Control Algorithms: A Comprehensive Survey Journal Article
In: IEEE Communications Surveys & Tutorials, pp. 1-1, 2025.
Codes for Identification via Channels: Tutorial for Communications Generalists Journal Article
In: IEEE Communications Surveys & Tutorials, 2025.
NET Playground - A Multi-Functional Testbed for Distributed Systems Proceedings Article
In: IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pp. 140-145, Tbilisi, Georgia, 2024.
Revisiting MARS: Storing Coded Packets In-Advance for IPFS Proceedings Article
In: IEEE International Conference on Communications (ICC), pp. 4692-4697, Denver, USA, 2024.
Identification Codes for Increased Reliability in Digital Twin Applications over Noisy Channels Proceedings Article
In: International Conference on Metaverse Computing, Networking and Applications (MetaCom), pp. 550-557, IEEE, Kyoto, Japan, 2023.
Beyond the Bound: A New Performance Perspective for Identification via Channels Journal Article
In: IEEE Journal on Selected Areas in Communications, vol. 41, iss. 8, pp. 2687-2706, 2023.
Identification Codes: A Topical Review with Design Guidelines for Practical Systems Journal Article
In: IEEE Access, vol. 11, pp. 14961-14982, 2023.
Stopping the Data Flood: Post-Shannon Traffic Reduction in Digital-Twins Applications Proceedings Article
In: NOMS 2022-2022, pp. 1-5, 2022, ISSN: 2374-9709.
A deep learning wireless transceiver with fully learned modulation and synchronization Proceedings Article
In: 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1-6, IEEE 2019.
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