Abstract
Wireless networks are currently being redesigned to provide sensing and communication services. In this talk, I provide an overview of the different aspects that can be integrated to provide dual service. I start by presenting a vision of the network, emphasizing that the role of machine learning is so critical that I envision what I call an ISLAC (integrated sensing, learning, and communication) network. I define the concept of the ISLAC map and its connection to the idea of sensing-aided network operation–a less conventional avenue to integrate sensing and communication- which leads to the vision of the network as a learning entity. Moreover, I will discuss specific examples of the integration of machine learning and conventional signal processing algorithms to solve specific tasks in dual-purpose networks. Then, I will discuss how the integration of new elements in the infrastructure of the ISLAC network can lead to enhanced performance, both for sensing and communication services. I will also review examples of the benefits of integration of information from different network sensing modalities. Finally, I will address the concept of multimodal integration, where information from conventional sensors and network sensing modalities can be fused for different purposes.
Brief Biography
Nuria González-Prelcic (SM, IEEE) received her Ph.D. with Honors in 2000 from the University of Vigo, Spain. She is a Professor at the ECE Department of the University of California San Diego since January 2024. She previously held faculty positions at the ECE Department of NC State University, USA (2020-2023) and at the Signal Theory and Communications Department at the University of Vigo, Spain (2000-2020). She also held visiting positions at the University of Texas at Austin and the University of New Mexico. She was also the founding director of the Atlantic Research Center for Information and Communication Technologies (Atlantic) at the University of Vigo (2008-2017). Her main research interests include signal processing and machine learning for wireless communications, with a focus on MIMO processing for mmWave communication, joint sensing and communication, sensor-aided communication, signal processing under hardware impairments, vehicular communication, and multiantenna technology for LEO satellite communication. She has published more than 150 papers in these areas, including a highly cited tutorial on signal processing for mmWave MIMO published in the IEEE Journal of Selected Topics in Signal Processing which has received the 2020 IEEE SPS Donald G. Fink Overview Paper Award, and a paper pioneering the idea of enabling automotive radar with a WiFi waveform that won the 2022 IEEE Vehicular Technology Society Best Vehicular Electronics Paper Award. She is the lead editor in the forthcoming special issue on integrated sensing and communication featured by the IEEE Signal Processing Magazine. She was an Editor for IEEE Transactions in Wireless Communications (2016-2020). She is currently an Editor for IEEE Transactions in Communications. She is a member of the IEEE Signal Processing Society TWG on Integrated Sensing and Communication, SAM Technical Committee, SPCOM Technical Committee, and IEEE SPS Education Board.