Waveform Design for Reliable Communications in Doubly Dispersive Channels

We are pleased to host Dr. Marwa Chafii, recipient of the Best PhD Award in France in 2016. She has been named in 2020 as one of the top 10 Rising Stars in the field of Computer Networking and Communications. She was selected by IEEE Communication Society to win the Best Editor Award in 2020 for her role in IEEE Communication Letters. Dr. Marwa Chafii completed her postgraduate studies at France’s top technical university, CentraleSupélec, where she received her Master’s in advanced wireless communication systems in 2013, and Ph.D. in electrical engineering in 2016. She held several visiting researcher appointment between 2014 and 2016, including the University of Oxford (UK), University of York (UK), Yokohama National University (Japan), and Poznan University of Technology (Poland). She joined the Vodafone Chair Mobile Communication Systems at the Technical University of Dresden, Germany, in February 2018 as a research group leader. Since September 2018, she is an associate professor at ETIS (CY Paris University, ENSEA, CNRS), France, where she holds a Chair of Excellence on Artificial Intelligence from CY Initiative. Her research interests include advanced waveform design, machine learning for wireless communications, and indoor localization. Since 2019, she serves as Associate Editor at IEEE Communications Letters and as a Guest Editor at frontiers in Communications and networks since 2020. She is currently vice-chair of the IEEE ComSoc Emerging Technologies Initiative (ETI) on Machine Learning for Communications, and leading the Education working group of the IEEE ComSoc ETI on Integrated Sensing and Communications.

Internet of Things (IoT) networks will underpin the global functioning of our societies and enable formidable progress in many sectors. In the forthcoming beyond fifth-generation (B5G) systems, we will experience the fulfillment of demanding use cases, notably critical IoT systems, usually with stringent reliability and latency requirements. In particular, for high mobility scenarios such as vehicular communications, the transmission reliability is challenging since the wireless channel becomes both time and frequency dispersive. In this talk, we will show how new waveform design is a key enabling technology for achieving reliable communications in fast fading environments. We will see how designing spreading waveforms, unique-word-based transmissions, and machine learning-based channel estimation, can increase the reliability of communications for doubly selective channels.