Literature Database Entry


Lisa Y. Debus, Pit Hofmann, Jorge Torres Gómez, Frank H. P. Fitzek and Falko Dressler, "Reinforcement Learning-based Receiver for Molecular Communication with Mobility," Proceedings of IEEE Global Communications Conference (GLOBECOM 2023), Kuala Lumpur, Malaysia, December 2023, pp. 558–564.


Molecular communication (MC) is getting closer to becoming a next-generation communication technology with many applications in life sciences and other industrial appli- cations. Multiple techniques have been proposed on how to design MC receivers depending on the channel characteristics. Experimentally, first testbeds also demonstrate the potentialities for communication using molecules as carriers. In this paper, we focus on developing a reinforcement learning (RL)-based receiver, targeting a realistic scenario with testbed measurements, and addressing transmitter mobility. Leveraging on reported solutions for machine learning (ML) methods, we demonstrate the usability of a RL agent to synchronize the receiver to the received signal. We evidence the learning capabilities of the agent to compensate for the impact of mobility, achieving a low probability of missed detection and small misalignment with the symbol time.

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Lisa Y. Debus
Pit Hofmann
Jorge Torres Gómez
Frank H. P. Fitzek
Falko Dressler

BibTeX reference

    author = {Debus, Lisa Y. and Hofmann, Pit and Torres G{\'{o}}mez, Jorge and Fitzek, Frank H. P. and Dressler, Falko},
    doi = {10.1109/GLOBECOM54140.2023.10436754},
    title = {{Reinforcement Learning-based Receiver for Molecular Communication with Mobility}},
    pages = {558--564},
    publisher = {IEEE},
    address = {Kuala Lumpur, Malaysia},
    booktitle = {IEEE Global Communications Conference (GLOBECOM 2023)},
    month = {12},
    year = {2023},

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