S
Sofie Pollin
Researcher at Katholieke Universiteit Leuven
Publications - 408
Citations - 8315
Sofie Pollin is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: MIMO & Wireless. The author has an hindex of 37, co-authored 366 publications receiving 6426 citations. Previous affiliations of Sofie Pollin include University of Wisconsin-Madison & University of Copenhagen Faculty of Science.
Papers
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Journal ArticleDOI
Performance Analysis of Slotted Carrier Sense IEEE 802.15.4 Medium Access Layer
Sofie Pollin,Mustafa Ergen,Sinem Coleri Ergen,Bruno Bougard,L. Der Perre,Ingrid Moerman,Ahmad Bahai,Pravin Varaiya,F. Catthoor +8 more
TL;DR: Whether this MAC scheme meets the design constraints of low-power and low-cost sensor networks is analyzed, and a detailed analytical evaluation of its performance in a star topology network, for uplink and acknowledged uplink traffic is provided.
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Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors
TL;DR: In this article, a new data-driven model for automatic modulation classification based on long short term memory (LSTM) is proposed, which learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments.
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Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity
TL;DR: This framework incorporates both height-dependent path loss exponent and small-scale fading, and unifies a widely used ground-to-ground channel model with that of A2G for the analysis of large-scale wireless networks, and derives analytical expressions for the optimal UAV height that minimizes the outage probability of an arbitrary A1G link.
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LTE in the sky: trading off propagation benefits with interference costs for aerial nodes
TL;DR: Interference is going to be a major limiting factor when LTE enabled UAVs are introduced, and that strong technical solutions will have to be found.
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Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors.
TL;DR: In this paper, a new data-driven model for Automatic Modulation Classification (AMC) based on long short term memory (LSTM) is proposed, which learns from the time domain amplitude and phase information of the modulation schemes present in the training data.