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Demo abstract: How fuzzy logic can enhance energy management in Wireless Sensor nodes equipped by energy harvesters and wake-up radios.

, , and . DASIP, page 229-230. IEEE, (2016)

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End-to-End Learning of Communications Systems Without a Channel Model., and . ACSSC, page 298-303. IEEE, (2018)Model-free Training of End-to-end Communication Systems., and . CoRR, (2018)Learning Radio Environments by Differentiable Ray Tracing., , , , , , and . CoRR, (2023)Waveform Learning for Next-Generation Wireless Communication Systems., and . CoRR, (2021)Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier., , , , and . CoRR, (2022)Trainable Communication Systems: Concepts and Prototype., , , , , and . IEEE Trans. Commun., 68 (9): 5489-5503 (2020)Joint Learning of Geometric and Probabilistic Constellation Shaping., , and . GLOBECOM Workshops, page 1-6. IEEE, (2019)Energy Harvesting Wireless Sensor Networks Leveraging Wake-up Receivers: Energy Managers and MAC Protocols. (Réseaux de Capteurs Sans Fils Auto-alimentés Utilisant des Wake-up Radios: Gestionnaire d'Énergie et Protocoles MAC).. University of Rennes 1, France, (2017)End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication., and . IEEE Trans. Wirel. Commun., 21 (2): 1049-1063 (2022)End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints., , , and . GLOBECOM (Workshops), page 1-6. IEEE, (2021)