Titlebook: Compressed Sensing in Information Processing; Gitta Kutyniok,Holger Rauhut,Robert J. Kunsch Book 2022 The Editor(s) (if applicable) and Th

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Fast Radio Propagation Prediction with Deep Learning,a point . (transmitter location) to any point . on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the pathloss function for all pairs of transmitter–receiver locations is very important. In realistic propagation env

Engaging

fluoroscopy

Over-the-Air Computation for Distributed Machine Learning and Consensus in Large Wireless Networks,ted consensus algorithms and distributed Machine Learning. For the latter, we specifically consider an application of OTA computation in Horizontal and Vertical Federated Learning. We then survey some of our own contributions to these fields, which include a scheme for analog Over-the-Air computatio

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Information Theory and Recovery Algorithms for Data Fusion in Earth Observation, ..)) + .. and their applications in remote sensing. We focus on the identification of feed-forward artificial neural networks and on quantized compressed sensing via message passing. Besides theoretical guarantees, we provide novel algorithmic approaches and numerical solutions, and we show concret

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HALO

Compressed Sensing in the Spherical Near-Field to Far-Field Transformation,l sampling scheme used for such measurements is heavily oversampled. However, several problems arise when reducing the sampling points, since the lack of . knowledge of the radiation characteristics complicates the choice of the points that do, in fact, contain information. Additionally, the conside

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Strategisches Vertriebsmanagementoximate dual certificates and the golfing scheme, which are often better suited to deal with practical measurement structures, but sometimes lead to weaker guarantees. Lastly, we review recent progress toward analyzing descent cones also for structured scenarios—exploiting the idea of splitting the

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https://doi.org/10.1007/978-3-031-58971-3 the channel, that is, the covariance of its spreading function. Along with a review of known results, we present some new insights on the problem and discuss how to utilize linear side constraints on the covariance as in the case of deterministic channels. We also present some numerical simulations