We propose a model to estimate the packet delivery rate in a LoRaWAN cell, when all nodes have the same traffic generation process and may use repetitions. The model predicts the transmission success rate for any cell range and node density, with similar traffic from all nodes. We find that the transmission success depends on striking a balance between the adverse effects of attenuation and collisions; in small cells, it is highly dependent on the suitable allocation of spreading factors, whereas using packet repetitions is more effective in large cells.
Can random matrices change the future of machine learning? by Romain Couillet (Gipsa)
Can random matrices change the future of machine learning? by Romain Couillet (Gipsa)
January 23, 2020 –
Romain COUILLET (professor at CentraleSupélec, University ParisSaclay; IDEX GSTATS Chair & MIAI LargeDATA Chair, University Grenoble-Alpes)
Title: Can random matrices change the future of machine learning?
Abstract: Many standard machine learning algorithms and intuitions are known to misbehave, if not dramatically collapse, when operated on large dimensional data. In this talk, we will show that large dimensional statistics, and particularly random matrix theory, not only can elucidate this behavior but provides a new set of tools to understand and (sometimes drastically) improve machine learning algorithms. Besides, we will show that our various theoretical findings are provably applicable to very realistic and not-so-large dimensional data.
Keynote: Radu Horaud
HDR Nicolas Gast