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Machine Learning for Communications Emerging Technologies Initiatives

The aim of this Emerging Technology Initiative (ETI) is to foster research and innovation surrounding the use of machine learning (ML) for the physical (PHY) and medium access control (MAC) layers for all types of communication systems, such as wireless, optical, satellite, and molecular. We provide a list of Best Readings in MLC for newcomers and organize conference workshops, tracks, sessions, industry symposia, tutorials, summer schools, data science competitions, as well as special issues in journals. We aim to establish common data sets and related benchmarks and invite authors to open-source their code for reproducible research. We maintain a blog where members can write articles, opinions, perspectives or present their research in an accessible way.

Chair | Jakob Hoydis
Vice Chairs | Tim O’Shea | Elisabeth de Carvalho | Marwa Chafii
Industry Liaison Officers | Hugo Tullberg | Yan Xin
Datasets & Competitions Officer | Maximilian Arnold
Workshops, Tutorials, & Symposia Officers | Slawomir Stanczak | Marios Kountouris | Marco di Renzo
Research Blog Officer | Meysam Sadeghi