Context awareness is key for designing dynamic wireless networks in tactical scenarios. Context is used to adapt the network to the environment and mission requirements after deployment. This represents a paradigm shift with respect to traditional networks that are designed in advance and do not adapt once deployed. Different technologies can be used to acquire context information. However, they require deploying ad-hoc hardware, e.g., radars, which may be infeasible in tactical scenarios, e.g., when setting up a temporary drone-based network. Notably, wireless devices continuously monitor the way signals propagate to compensate for channel impairments and decode data. Hence, channel measurements can be leveraged as a proxy for the environment and adapt the network to it. The joint design of sensing and communication, which goes under the name of integrated sensing and communications (ISAC), is the backbone for network intelligence. This tutorial provides a comprehensive overview of how to obtain context information from channel measurements. The principles behind ISAC and its potential impact on wireless networks will be detailed, with a focus on tactical scenarios. Mathematical foundations will be explained and signal processing for sensing will be showcased, together with advanced state-of-the-art machine learning (ML)-based strategies.
We will build a Wi-Fi sensing system for human activity recognition, from the channel state information (CSI) collected by a commercial IEEE 802.11ac router to a deep learning classifier that is tested in environments, on people, and on days never seen during training. Everything runs in Google Colab: you only need a laptop or tablet with a Google account, no installation is required. Attendees without a laptop can follow the notebook projected during the session.
The notebook and the data will be released at the beginning of the hands-on session.
Dr. Francesca Meneghello (Northeastern University, USA) is a Principal Research Scientist at the Institute for Intelligent Networked Systems (INSI) at Northeastern University, USA. She received her Ph.D. degree in Information Engineering in 2022 from the University of Padova, Italy, and has been a Postdoctoral Researcher at the Department of Information Engineering at the same University. Her work focuses on wireless communications and machine learning, contributing to defining and developing next-generation data-driven wireless technologies that can adapt to the context. She serves as TPC co-chair of the IEEE INFOCOM DeepWireless Workshop and guest editor of npj Wireless Technology. She has received a Best Paper Award at IEEE INFOCOM 2025 and 2026. She is a 2023 Fulbright-Schuman alumna and is a recipient of an MSCA Global Postdoctoral Fellowship funded by the European Commission under the Horizon Europe scheme (2026–2029).